May 20, 2021
Bioconductors:
We are pleased to announce Bioconductor 3.13, consisting of 2042 software packages, 406 experiment data packages, 965 annotation packages, and 29 workflows.
There are 133 new software packages, 22 new data experiment packages, 7 new annotation packages, 1 new workflow, no new books, and many updates and improvements to existing packages; Bioconductor 3.13 is compatible with R 4.1.0, and is supported on Linux, 32- and 64-bit Windows, and macOS 10.14.6 Mojave or higher. This release will include an updated Bioconductor Docker containers.
Thank you to everyone for your contribution to Bioconductor
Visit Bioconductor BiocViews for details and downloads.
To update to or install Bioconductor 3.13:
Install R 4.1.0. Bioconductor 3.13 has been designed expressly for this version of R.
Follow the instructions at Installing Bioconductor.
There are 133 new software packages in this release of Bioconductor.
airpart Airpart identifies sets of genes displaying differential cell-type-specific allelic imbalance across cell types or states, utilizing single-cell allelic counts. It makes use of a generalized fused lasso with binomial observations of allelic counts to partition cell types by their allelic imbalance. Alternatively, a nonparametric method for partitioning cell types is offered. The package includes a number of visualizations and quality control functions for examining single cell allelic imbalance datasets.
autonomics This package offers a generic and intuitive solution for cross-platform omics data analysis. It has functions for import, preprocessing, exploration, contrast analysis and visualization of omics data. It follows a tidy, functional programming paradigm.
awst We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experimental artifacts.
barcodetrackR barcodetrackR is an R package developed for the analysis and visualization of clonal tracking data. Data required is samples and tag abundances in matrix form. Usually from cellular barcoding experiments, integration site retrieval analyses, or similar technologies.
biodb The biodb package provides access to standard remote chemical and biological databases (ChEBI, KEGG, HMDB, …), as well as to in-house local database files (CSV, SQLite), with easy retrieval of entries, access to web services, search of compounds by mass and/or name, and mass spectra matching for LCMS and MSMS. Its architecture as a development framework facilitates the development of new database connectors for local projects or inside separate published packages.
BioNERO BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the “wisdom of the crowds” principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks.
BloodGen3Module The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.1101/525709v2 and https://pubmed.ncbi.nlm.nih.gov/33624743/, the results of module repertoire analyses can be represented in a fingerprint format, where red and blue spots indicate increases or decreases in module activity. These spots are subsequently represented either on a grid, with each position being assigned to a given module, or in a heatmap where the samples are arranged in columns and the modules in rows.
bnem bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate).
BumpyMatrix Implements the BumpyMatrix class and several subclasses for holding non-scalar objects in each entry of the matrix. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface - hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects.
CAEN With the development of high-throughput techniques, more and more gene expression analysis tend to replace hybridization-based microarrays with the revolutionary technology.The novel method encodes the category again by employing the rank of samples for each gene in each class. We then consider the correlation coefficient of gene and class with rank of sample and new rank of category. The highest correlation coefficient genes are considered as the feature genes which are most effective to classify the samples.
cbpManager This R package provides an R Shiny application that enables the user to generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics. Create cancer studies and edit its metadata. Upload mutation data of a patient that will be concatenated to the data_mutation_extended.txt file of the study. Create and edit clinical patient data, sample data, and timeline data. Create custom timeline tracks for patients.
CelliD CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data.
cellmigRation Import TIFF images of fluorescently labeled cells, and track cell movements over time. Parallelization is supported for image processing and for fast computation of cell trajectories. In-depth analysis of cell trajectories is enabled by 15 trajectory analysis functions.
censcyt Methods for differential abundance analysis in high-dimensional cytometry data when a covariate is subject to right censoring (e.g. survival time) based on multiple imputation and generalized linear mixed models.
CIMICE CIMICE is a tool in the field of tumor phylogenetics and its goal is to build a Markov Chain (called Cancer Progression Markov Chain, CPMC) in order to model tumor subtypes evolution. The input of CIMICE is a Mutational Matrix, so a boolean matrix representing altered genes in a collection of samples. These samples are assumed to be obtained with single-cell DNA analysis techniques and the tool is specifically written to use the peculiarities of this data for the CMPC construction.
CNVgears This package contains a set of functions to perform several type of processing and analysis on CNVs calling pipelines/algorithms results in an integrated manner and regardless of the raw data type (SNPs array or NGS). It provides functions to combine multiple CNV calling results into a single object, filter them, compute CNVRs (CNV Regions) and inheritance patterns, detect genic load, and more. The package is best suited for studies in human family-based cohorts.
CNViz CNViz takes probe, gene, and segment-level log2 copy number ratios and launches a Shiny app to visualize your sample’s copy number profile. You can also integrate loss of heterozygosity (LOH) and single nucleotide variant (SNV) data.
ComPrAn This package is for analysis of SILAC labeled complexome profiling data. It uses peptide table in tab-delimited format as an input and produces ready-to-use tables and plots.
conclus CONCLUS is a tool for robust clustering and positive marker features selection of single-cell RNA-seq (sc-RNA-seq) datasets. It takes advantage of a consensus clustering approach that greatly simplify sc-RNA-seq data analysis for the user. Of note, CONCLUS does not cover the preprocessing steps of sequencing files obtained following next-generation sequencing. CONCLUS is organized into the following steps: Generation of multiple t-SNE plots with a range of parameters including different selection of genes extracted from PCA. Use the Density-based spatial clustering of applications with noise (DBSCAN) algorithm for idenfication of clusters in each generated t-SNE plot. All DBSCAN results are combined into a cell similarity matrix. The cell similarity matrix is used to define “CONSENSUS” clusters conserved accross the previously defined clustering solutions. Identify marker genes for each concensus cluster.
condiments This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an ‘omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format.
CONSTANd Normalizes a data matrix data
by
raking (using the RAS method by Bacharach, see references) the
Nrows by Ncols matrix such that the row means and column means
equal 1. The result is a normalized data matrix K=RAS
, a product
of row mulipliers R
and column multipliers S
with the original
matrix A
. Missing information needs to be presented as NA
values and not as zero values, because CONSTANd is able to ignore
missing values when calculating the mean. Using CONSTANd
normalization allows for the direct comparison of values between
samples within the same and even across different
CONSTANd-normalized data matrices.
cosmosR COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets based on prior knowledge of signaling, metabolic, and gene regulatory networks. It estimated the activities of transcrption factors and kinases and finds a network-level causal reasoning. Thereby, COSMOS provides mechanistic hypotheses for experimental observations across mulit-omics datasets.
CTDquerier Package to retrieve and visualize data from the Comparative Toxicogenomics Database (http://ctdbase.org/). The downloaded data is formated as DataFrames for further downstream analyses.
cyanoFilter An approach to filter out and/or identify phytoplankton cells from all particles measured via flow cytometry pigment and cell complexity information. It does this using a sequence of one-dimensional gates on pre-defined channels measuring certain pigmentation and complexity. The package is especially tuned for cyanobacteria, but will work fine for phytoplankton communities where there is at least one cell characteristic that differentiates every phytoplankton in the community.
CytoGLMM The CytoGLMM R package implements two multiple regression strategies: A bootstrapped generalized linear model (GLM) and a generalized linear mixed model (GLMM). Most current data analysis tools compare expressions across many computationally discovered cell types. CytoGLMM focuses on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. As a result, CytoGLMM finds differential proteins in flow and mass cytometry data while reducing biases arising from marker correlations and safeguarding against false discoveries induced by patient heterogeneity.
dce Compute differential causal effects (dce) on (biological) networks. Given observational samples from a control experiment and non-control (e.g., cancer) for two genes A and B, we can compute differential causal effects with a (generalized) linear regression. If the causal effect of gene A on gene B in the control samples is different from the causal effect in the non-control samples the dce will differ from zero. We regularize the dce computation by the inclusion of prior network information from pathway databases such as KEGG.
decoupleR Transcriptome profiling followed by differential gene expression analysis often leads to lists of genes that are hard to analyze and interpret. Downstream analysis tools can be used to summarize deregulation events into a smaller set of biologically interpretable features. In particular, methods that estimate the activity of transcription factors (TFs) from gene expression are commonly used. It has been shown that the transcriptional targets of a TF yield a much more robust estimation of the TF activity than observing the expression of the TF itself. Consequently, for the estimation of transcription factor activities, a network of transcriptional regulation is required in combination with a statistical algorithm that summarizes the expression of the target genes into a single activity score. Over the years, many different regulatory networks and statistical algorithms have been developed, mostly in a fixed combination of one network and one algorithm. To systematically evaluate both networks and algorithms, we developed decoupleR , an R package that allows users to apply efficiently any combination provided.
DeepPINCS The identification of novel compound-protein interaction (CPI) is important in drug discovery. Revealing unknown compound-protein interactions is useful to design a new drug for a target protein by screening candidate compounds. The accurate CPI prediction assists in effective drug discovery process. To identify potential CPI effectively, prediction methods based on machine learning and deep learning have been developed. Data for sequences are provided as discrete symbolic data. In the data, compounds are represented as SMILES (simplified molecular-input line-entry system) strings and proteins are sequences in which the characters are amino acids. The outcome is defined as a variable that indicates how strong two molecules interact with each other or whether there is an interaction between them. In this package, a deep-learning based model that takes only sequence information of both compounds and proteins as input and the outcome as output is used to predict CPI. The model is implemented by using compound and protein encoders with useful features. The CPI model also supports other modeling tasks, including protein-protein interaction (PPI), chemical-chemical interaction (CCI), or single compounds and proteins. Although the model is designed for proteins, DNA and RNA can be used if they are represented as sequences.
DelayedRandomArray Implements a DelayedArray of random values where the realization of the sampled values is delayed until they are needed. Reproducible sampling within any subarray is achieved by chunking where each chunk is initialized with a different random seed and stream. The usual distributions in the stats package are supported, along with scalar, vector and arrays for the parameters.
DExMA performing all the steps of gene expression meta-analysis without eliminating those genes that are presented in almost two data sets. It provides the necessary functions to be able to perform the different methods of gene expression meta-analysis. In addition, it contains functions to apply quality controls, download GEO data sets and show graphical representations of the results.
diffUTR The diffUTR package provides a uniform interface and plotting functions for limma/edgeR/DEXSeq -powered differential bin/exon usage. It includes in addition an improved version of the limma::diffSplice method. Most importantly, diffUTR further extends the application of these frameworks to differential UTR usage analysis using poly-A site databases.
dir.expiry Implements an expiration system for access to versioned directories. Directories that have not been accessed by a registered function within a certain time frame are deleted. This aims to reduce disk usage by eliminating obsolete caches generated by old versions of packages.
drugTargetInteractions Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain.
epialleleR Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls hypermethylated epiallele frequencies at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Other functionality includes computing the empirical cumulative distribution function for per-read beta values, and testing the significance of the association between epiallele methylation status and base frequencies at particular genomic positions (SNPs).
epidecodeR epidecodeR is a package capable of analysing impact of degree of DNA/RNA epigenetic chemical modifications on dysregulation of genes or proteins. This package integrates chemical modification data generated from a host of epigenomic or epitranscriptomic techniques such as ChIP-seq, ATAC-seq, m6A-seq, etc. and dysregulated gene lists in the form of differential gene expression, ribosome occupancy or differential protein translation and identify impact of dysregulation of genes caused due to varying degrees of chemical modifications associated with the genes. epidecodeR generates cumulative distribution function (CDF) plots showing shifts in trend of overall log2FC between genes divided into groups based on the degree of modification associated with the genes. The tool also tests for significance of difference in log2FC between groups of genes.
epigraHMM epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological/technical replicates, and and one for differial peaks from multi-replicate multi-condition experiments. For the latter, window-specific posterior probabilities associated with read count enrichment for every possible combinatorial pattern are provided.
EWCE Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses.
FEAST Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as “features”), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have significant impact on the clustering accuracy. FEAST is an R library for selecting most representative features before performing the core of scRNA-seq clustering. It can be used as a plug-in for the etablished clustering algorithms such as SC3, TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm includes three steps: 1. consensus clustering; 2. gene-level significance inference; 3. validation of an optimized feature set.
fedup An R package that tests for enrichment and depletion of user-defined pathways using a Fisher’s exact test. The method is designed for versatile pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run pathway analysis on custom annotations. This package is also integrated with Cytoscape to provide network-based pathway visualization that enhances the interpretability of the results.
fgga Package that implements the FGGA algorithm. This package provides a hierarchical ensemble method based ob factor graphs for the consistent GO annotation of protein coding genes. FGGA embodies elements of predicate logic, communication theory, supervised learning and inference in graphical models.
flowGraph Identifies maximal differential cell populations in flow cytometry data taking into account dependencies between cell populations; flowGraph calculates and plots SpecEnr abundance scores given cell population cell counts.
fobitools A set of tools for interacting with Food-Biomarker Ontology (FOBI). A collection of basic manipulation tools for biological significance analysis, graphs, and text mining strategies for annotating nutritional data.
GenomicDistributions If you have a set of genomic ranges, this package can help you with visualization and comparison. It produces several kinds of plots, for example: Chromosome distribution plots, which visualize how your regions are distributed over chromosomes; feature distance distribution plots, which visualizes how your regions are distributed relative to a feature of interest, like Transcription Start Sites (TSSs); genomic partition plots, which visualize how your regions overlap given genomic features such as promoters, introns, exons, or intergenic regions. It also makes it easy to compare one set of ranges to another.
GenomicSuperSignature This package contains the index, which is the Replicable and interpretable Axes of Variation (RAV) extracted from public RNA sequencing datasets by clustering and averaging top PCs. This index, named as RAVindex, is further annotated with MeSH terms and GSEA. Functions to connect PCs from new datasets to RAVs, extract interpretable annotations, and provide intuitive visualization, are implemented in this package. Overall, this package enables researchers to analyze new data in the context of existing databases with minimal computing resources.
GEOfastq GEOfastq is used to download fastq files from the European Nucleotide Archive (ENA) starting with an accession from the Gene Expression Omnibus (GEO). To do this, sample metadata is retrieved from GEO and the Sequence Read Archive (SRA). SRA run accessions are then used to construct FTP and aspera download links for fastq files generated by the ENA.
GeomxTools Tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included.
geva Statistic methods to evaluate variations of differential expression (DE) between multiple biological conditions. It takes into account the fold-changes and p-values from previous differential expression (DE) results that use large-scale data (e.g., microarray and RNA-seq) and evaluates which genes would react in response to the distinct experiments. This evaluation involves an unique pipeline of statistical methods, including weighted summarization, quantile detection, cluster analysis, and ANOVA tests, in order to classify a subset of relevant genes whose DE is similar or dependent to certain biological factors.
granulator granulator is an R package for the cell type deconvolution of heterogeneous tissues based on bulk RNA-seq data or single cell RNA-seq expression profiles. The package provides a unified testing interface to rapidly run and benchmark multiple state-of-the-art deconvolution methods. Data for the deconvolution of peripheral blood mononuclear cells (PBMCs) into individual immune cell types is provided as well.
hca This package provides users with the ability
to query the Human Cell Atlas data repository for single-cell
experiment data. The projects()
, files()
, samples()
and
bundles()
functions retrieve summary information on each of these
indexes; corresponding *_details()
are available for individual
entries of each index. File-based resources can be downloaded using
files_download()
. Advanced use of the package allows the user to
page through large result sets, and to flexibly query the
‘list-of-lists’ structure representing query responses.
HGC HGC
(short for Hierarchical Graph-based
Clustering) is a R package for conducting hierarchical clustering
on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea
is to construct a dendrogram of cells on their shared nearest
neighbor (SNN) graph. HGC
provides functions for building cell
graphs and for conducting hierarchical clustering on the graph.
HiCDCPlus Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw contact matrix for each chromosome up to a specified genomic distance, binned by uniform genomic intervals or restriction enzyme fragments, by training a background model to account for random polymer ligation and systematic sources of read count variation.
HubPub HubPub provides users with functionality to help with the Bioconductor Hub structures. The package provides the ability to create a skeleton of a Hub style package that the user can then populate with the necessary information. There are also functions to help add resources to the Hub package metadata files as well as publish data to the Bioconductor S3 bucket.
immunotation MHC (major histocompatibility complex) molecules are cell surface complexes that present antigens to T cells. The repertoire of antigens presented in a given genetic background largely depends on the sequence of the encoded MHC molecules, and thus, in humans, on the highly variable HLA (human leukocyte antigen) genes of the hyperpolymorphic HLA locus. More than 28,000 different HLA alleles have been reported, with significant differences in allele frequencies between human populations worldwide. Reproducible and consistent annotation of HLA alleles in large-scale bioinformatics workflows remains challenging, because the available reference databases and software tools often use different HLA naming schemes. The package immunotation provides tools for consistent annotation of HLA genes in typical immunoinformatics workflows such as for example the prediction of MHC-presented peptides in different human donors. Converter functions that provide mappings between different HLA naming schemes are based on the MHC restriction ontology (MRO). The package also provides automated access to HLA alleles frequencies in worldwide human reference populations stored in the Allele Frequency Net Database.
interacCircos Implement in an efficient approach to display the genomic data, relationship, information in an interactive circular genome(Circos) plot. ‘interacCircos’ are inspired by ‘circosJS’, ‘BioCircos.js’ and ‘NG-Circos’ and we integrate the modules of ‘circosJS’, ‘BioCircos.js’ and ‘NG-Circos’ into this R package, based on ‘htmlwidgets’ framework.
InteractiveComplexHeatmap This package can easily make heatmaps which are produced by the ComplexHeatmap package into interactive applications. It provides two types of interactivities: 1. on the interactive graphics device, and 2. on a Shiny app. It also provides functions for integrating the interactive heatmap widgets for more complex Shiny app development.
InterCellar InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, the user can define interaction-pairs modules and link them to significant functional terms from Pathways or Gene Ontology.
IRISFGM Single-cell RNA-Seq data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of functional gene modules (FGM) can help to understand gene interactive networks and complex biological processes. QUBIC2 is recognized as one of the most efficient and effective tools for FGM identification from scRNA-Seq data. However, its availability is limited to a C implementation, and its applicative power is affected by only a few downstream analyses functionalities. We developed an R package named IRIS-FGM (integrative scRNA-Seq interpretation system for functional gene module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can identify co-expressed and co-regulated FGMs, predict types/clusters, identify differentially expressed genes, and perform functional enrichment analysis. It is noteworthy that IRIS-FGM also applies Seurat objects that can be easily used in the Seurat vignettes.
KBoost Reconstructing gene regulatory networks and transcription factor activity is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-art algorithm are often not able to handle large amounts of data. Furthermore, many of the present methods predict numerous false positives and are unable to integrate other sources of information such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. KBoost can also use a prior network built on previously known transcription factor targets. We have benchmarked KBoost using three different datasets against other high performing algorithms. The results show that our method compares favourably to other methods across datasets.
lisaClust lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.
LRcell The goal of LRcell is to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. To achieve this, LRcell utilizes sets of cell marker genes acquired from single-cell RNA-sequencing (scRNA-seq) as indicators for various cell types in the tissue of interest. Next, for each cell type, using its marker genes as indicators, we apply Logistic Regression on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value. Finally, these p-values are compared to predict which one(s) are likely to be responsible for the differential gene expression pattern observed in the bulk RNA-seq experiments. LRcell is inspired by the LRpath[@sartor2009lrpath] algorithm developed by Sartor et al., originally designed for pathway/gene set enrichment analysis. LRcell contains three major components: LRcell analysis, plot generation and marker gene selection. All modules in this package are written in R. This package also provides marker genes in the Prefrontal Cortex (pFC) human brain region, human PBMC and nine mouse brain regions (Frontal Cortex, Cerebellum, Globus Pallidus, Hippocampus, Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra and Thalamus).
MACSr The Model-based Analysis of ChIP-Seq (MACS) is a widely used toolkit for identifying transcript factor binding sites. This package is an R wrapper of the lastest MACS3.
MAGAR “Methylation-Aware Genotype Association in R” (MAGAR) computes methQTL from DNA methylation and genotyping data from matched samples. MAGAR uses a linear modeling stragety to call CpGs/SNPs that are methQTLs. MAGAR accounts for the local correlation structure of CpGs.
MatrixQCvis Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object.
memes A seamless interface to the MEME Suite family of tools for motif analysis. ‘memes’ provides data aware utilities for using GRanges objects as entrypoints to motif analysis, data structures for examining & editing motif lists, and novel data visualizations. ‘memes’ functions and data structures are amenable to both base R and tidyverse workflows.
MetaboCoreUtils MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound mass-to-charge ratios and masses or functions to work with chemical formulas. The package provides also a set of adduct definitions and information on some commercially available internal standard mixes commonly used in MS experiments.
metapod Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate.
methylscaper methylscaper is an R package for processing and visualizing data jointly profiling methylation and chromatin accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The package supports both single-cell and single-molecule data, and a common interface for jointly visualizing both data types through the generation of ordered representational methylation-state matrices. The Shiny app allows for an interactive seriation process of refinement and re-weighting that optimally orders the cells or DNA molecules to discover methylation patterns and nucleosome positioning.
mia mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization.
miaViz miaViz implements plotting function to work with TreeSummarizedExperiment and related objects in a context of microbiome analysis. Among others this includes plotting tree, graph and microbiome series data.
midasHLA MiDAS is a R package for immunogenetics data transformation and statistical analysis. MiDAS accepts input data in the form of HLA alleles and KIR types, and can transform it into biologically meaningful variables, enabling HLA amino acid fine mapping, analyses of HLA evolutionary divergence, KIR gene presence, as well as validated HLA-KIR interactions. Further, it allows comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS closes a gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to T cell, Natural Killer cell, and disease biology.
miloR This package performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using a negative bionomial generalized linear model.
mina An increasing number of microbiome datasets have been generated and analyzed with the help of rapidly developing sequencing technologies. At present, analysis of taxonomic profiling data is mainly conducted using composition-based methods, which ignores interactions between community members. Besides this, a lack of efficient ways to compare microbial interaction networks limited the study of community dynamics. To better understand how community diversity is affected by complex interactions between its members, we developed a framework (Microbial community dIversity and Network Analysis, mina), a comprehensive framework for microbial community diversity analysis and network comparison. By defining and integrating network-derived community features, we greatly reduce noise-to-signal ratio for diversity analyses. A bootstrap and permutation-based method was implemented to assess community network dissimilarities and extract discriminative features in a statistically principled way.
miQC Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA encoded genes (mtDNA) and (ii) if a small number of genes are detected. miQC is data-driven QC metric that jointly models both the proportion of reads mapping to mtDNA and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset.
mirTarRnaSeq mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments.
mistyR mistyR is an impolementation of the Multiview Intercellular SpaTialmodeling framework (MISTy). MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution.
moanin Simple and efficient workflow for time-course gene expression data, built on publictly available open-source projects hosted on CRAN and bioconductor. moanin provides helper functions for all the steps required for analysing time-course data using functional data analysis: (1) functional modeling of the timecourse data; (2) differential expression analysis; (3) clustering; (4) downstream analysis.
ModCon Collection of functions to calculate a nucleotide sequence surrounding for splice donors sites to either activate or repress donor usage. The proposed alternative nucleotide sequence encodes the same amino acid and could be applied e.g. in reporter systems to silence or activate cryptic splice donor sites.
MQmetrics The package MQmetrics (MaxQuant metrics) provides a workflow to analyze the quality and reproducibility of your proteomics mass spectrometry analysis from MaxQuant.Input data are extracted from several MaxQuant output tables, and produces a pdf report. It includes several visualization tools to check numerous parameters regarding the quality of the runs.It also includes two functions to visualize the iRT peptides from Biognosysin case they were spiked in the samples.
MsBackendMassbank Mass spectrometry (MS) data backend supporting import and export of MS/MS library spectra from MassBank record files. Different backends are available that allow handling of data in plain MassBank text file format or allow also to interact directly with MassBank SQL databases. Objects from this package are supposed to be used with the Spectra Bioconductor package. This package thus adds MassBank support to the Spectra package.
MsBackendMgf Mass spectrometry (MS) data backend supporting import and export of MS/MS spectra data from Mascot Generic Format (mgf) files. Objects defined in this package are supposed to be used with the Spectra Bioconductor package. This package thus adds mgf file support to the Spectra package.
MsFeatures The MsFeature package defines
functionality for Mass Spectrometry features. This includes
functions to group (LC-MS) features based on some of their
properties, such as retention time (coeluting features), or
correlation of signals across samples. This packge hence allows to
group features, and its results can be used as an input for the
QFeatures
package which allows to aggregate abundance levels of
features within each group. This package defines concepts and
functions for base and common data types, implementations for more
specific data types are expected to be implemented in the
respective packages (such as e.g. xcms
). All functionality of
this package is implemented in a modular way which allows
combination of different grouping approaches and enables its re-use
in other R packages.
msqrob2 msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2’s hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data.
MSstatsLOBD The MSstatsLOBD package allows calculation and visualization of limit of blac (LOB) and limit of detection (LOD). We define the LOB as the highest apparent concentration of a peptide expected when replicates of a blank sample containing no peptides are measured. The LOD is defined as the measured concentration value for which the probability of falsely claiming the absence of a peptide in the sample is 0.05, given a probability 0.05 of falsely claiming its presence. These functionalities were previously a part of the MSstats package. The methodology is described in Galitzine (2018) <doi:10.1074/mcp.RA117.000322>.
multiSight multiSight is an R package providing an user-friendly graphical interface to analyze your omic datasets in a multi-omics manner based on Stouffer’s p-value pooling and multi-block statistical methods. For each omic dataset you furnish, multiSight provides classification models with feature selection you can use as biosignature: (i) To forecast phenotypes (e.g. to diagnostic tasks, histological subtyping), (ii) To design Pathways and gene ontology enrichments (Over Representation Analysis), (iii) To build Network inference linked to PubMed querying to make assumptions easier and data-driven.
mumosa Assorted utilities for multi-modal analyses of single-cell datasets. Includes functions to combine multiple modalities for downstream analysis, perform MNN-based batch correction across multiple modalities, and to compute correlations between assay values for different modalities.
MungeSumstats The MungeSumstats package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also removes duplicates across SNPs.
NanoStringNCTools Tools for NanoString Technologies nCounter Technology. Provides support for reading RCC files into an ExpressionSet derived object. Also includes methods for QC and normalizaztion of NanoString data.
nempi Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models.
ORFhunteR The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules from the NCBI RefSeq and Ensembl databases. The package can be used in basic and applied biomedical research related to the study of the transcriptome of normal as well as altered (for example, cancer) human cells.
PDATK Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making.
PFP An implementation of the pathway fingerprint framework that introduced in paper “Pathway Fingerprint: a novel pathway knowledge and topology based method for biomarker discovery and characterization”. This method provides a systematic comparisons between a gene set (such as a list of differentially expressed genes) and well-studied “basic pathway networks” (KEGG pathways), measuring the importance of pathways and genes for the gene set. The package is helpful for researchers to find the biomarkers and its function.
PhenoGeneRanker This package is a gene/phenotype prioritization tool that utilizes multiplex heterogeneous gene phenotype network. PhenoGeneRanker allows multi-layer gene and phenotype networks. It also calculates empirical p-values of gene/phenotype ranking using random stratified sampling of genes/phenotypes based on their connectivity degree in the network. https://dl.acm.org/doi/10.1145/3307339.3342155.
PhIPData PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters.
planet This package contains R functions to infer additional biological variables to supplemental DNA methylation analysis of placental data. This includes inferring ethnicity/ancestry, gestational age, and cell composition from placental DNA methylation array (450k/850k) data. The package comes with an example processed placental dataset.
PoDCall Reads files exported from ‘QuantaSoft’ containing amplitude values from a run of ddPCR (96 well plate) and robustly sets thresholds to determine positive droplets for each channel of each individual well. Concentration and normalized concentration in addition to other metrics is then calculated for each well. Results are returned as a table, optionally written to file, as well as optional plots (scatterplot and histogram) for both channels per well written to file. The package includes a shiny application which provides an interactive and user-friendly interface to the full functionality of PoDCall.
POWSC Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship.
ppcseq Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.
ptairMS This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the ‘sample by features’ table of peak intensities in addition to the sample and feature metadata (as a single ExpressionSet object for subsequent statistical analysis). This package also permit usefull tools for cohorts management as analyzing data progressively, visualization tools and quality control. The steps include calibration, expiration detection, peak detection and quantification, feature alignment, missing value imputation and feature annotation. Applications to exhaled air and cell culture in headspace are described in the vignettes and examples. This package was used for data analysis of Gassin Delyle study on adults undergoing invasive mechanical ventilation in the intensive care unit due to severe COVID-19 or non-COVID-19 acute respiratory distress syndrome (ARDS), and permit to identfy four potentiel biomarquers of the infection.
quantiseqr This package provides a streamlined workflow for the quanTIseq method, developed to perform the quantification of the Tumor Immune contexture from RNA-seq data. The quantification is performed against the TIL10 signature (dissecting the contributions of ten immune cell types), carefully crafted from a collection of human RNA-seq samples. The TIL10 signature has been extensively validated using simulated, flow cytometry, and immunohistochemistry data.
ramr ramr is an R package for detection of low-frequency aberrant methylation events in large datasets obtained by methylation profiling using array or high-throughput bisulfite sequencing. In addition, package provides functions to visualize found aberrantly methylated regions (AMRs), and to generate sets of all possible regions to be used as reference sets for enrichment analysis.
rawrr This package wraps the functionality of the RawFileReader .NET assembly. Within the R environment, spectra and chromatograms are represented by S3 objects (Kockmann T. et al. (2020) <doi:10.1101/2020.10.30.362533>). The package provides basic functions to download and install the required third-party libraries. The package is developed, tested, and used at the Functional Genomics Center Zurich, Switzerland https://fgcz.ch.
Rbec Rbec is a adapted version of DADA2 for analyzing amplicon sequencing data from synthetic communities (SynComs), where the reference sequences for each strain exists. Rbec can not only accurately profile the microbial compositions in SynComs, but also predict the contaminants in SynCom samples.
RCSL A novel clustering algorithm and toolkit RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both lo-cal similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similar-ity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similar-ity of a cell to other cells is a linear combination of its global similarity and local similarity.
ReactomeContentService4R Reactome is a free, open-source, open access, curated and peer-reviewed knowledgebase of bio-molecular pathways. This package is to interact with the Reactome Content Service API. Pre-built functions would allow users to retrieve data and images that consist of proteins, pathways, and other molecules related to a specific gene or entity in Reactome.
ReactomeGraph4R Pathways, reactions, and biological entities in Reactome knowledge are systematically represented as an ordered network. Instances are represented as nodes and relationships between instances as edges; they are all stored in the Reactome Graph Database. This package serves as an interface to query the interconnected data from a local Neo4j database, with the aim of minimizing the usage of Neo4j Cypher queries.
RiboDiPA This package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization.
RLassoCox RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types.
SANTA This package provides methods for measuring the strength of association between a network and a phenotype. It does this by measuring clustering of the phenotype across the network (Knet). Vertices can also be individually ranked by their strength of association with high-weight vertices (Knode).
satuRn satuRn provides a higly performant and scalable framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest.
ScaledMatrix Provides delayed computation of a matrix of scaled and centered values. The result is equivalent to using the scale() function but avoids explicit realization of a dense matrix during block processing. This permits greater efficiency in common operations, most notably matrix multiplication.
SCArray Provides large-scale single-cell RNA-seq data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language.
scClassifR The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scClassifR also allows users to train their own models to predict new cell types based on specific research needs.
sechm sechm provides a simple interface between SummarizedExperiment objects and the ComplexHeatmap package. It enables plotting annotated heatmaps from SE objects, with easy access to rowData and colData columns, and implements a number of features to make the generation of heatmaps easier and more flexible. These functionalities used to be part of the SEtools package.
shinyepico ShinyÉPICo is a graphical pipeline to analyze Illumina DNA methylation arrays (450k or EPIC). It allows to calculate differentially methylated positions and differentially methylated regions in a user-friendly interface. Moreover, it includes several options to export the results and obtain files to perform downstream analysis.
SingleMoleculeFootprinting SingleMoleculeFootprinting is an R package providing functions to analyze Single Molecule Footprinting (SMF) data. Following the workflow exemplified in its vignette, the user will be able to perform basic data analysis of SMF data with minimal coding effort. Starting from an aligned bam file, we show how to perform quality controls over sequencing libraries, extract methylation information at the single molecule level accounting for the two possible kind of SMF experiments (single enzyme or double enzyme), classify single molecules based on their patterns of molecular occupancy, plot SMF information at a given genomic location
sitadela Provides an interface to build a unified database of genomic annotations and their coordinates (gene, transcript and exon levels). It is aimed to be used when simple tab-delimited annotations (or simple GRanges objects) are required instead of the more complex annotation Bioconductor packages. Also useful when combinatorial annotation elements are reuired, such as RefSeq coordinates with Ensembl biotypes. Finally, it can download, construct and handle annotations with versioned genes and transcripts (where available, e.g. RefSeq and latest Ensembl). This is particularly useful in precision medicine applications where the latter must be reported.
SOMNiBUS This package aims to analyse count-based methylation data on predefined genomic regions, such as those obtained by targeted sequencing, and thus to identify differentially methylated regions (DMRs) that are associated with phenotypes or traits. The method is built a rich flexible model that allows for the effects, on the methylation levels, of multiple covariates to vary smoothly along genomic regions. At the same time, this method also allows for sequencing errors and can adjust for variability in cell type mixture.
SplicingFactory The SplicingFactory R package uses transcript-level expression values to analyze splicing diversity based on various statistical measures, like Shannon entropy or the Gini index. These measures can quantify transcript isoform diversity within samples or between conditions. Additionally, the package analyzes the isoform diversity data, looking for significant changes between conditions.
Summix This package contains the Summix method for estimating and adjusting for ancestry in genetic summary allele frequency data. The function summix estimates reference ancestry proportions using a mixture model. The adjAF function produces ancestry adjusted allele frequencies for an observed sample with ancestry proportions matching a target person or sample.
supersigs Generate SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome. Functions included in the package allow the user to learn supervised mutational signatures from their data and apply them to new data. The methodology is based on the one described in Afsari (2021, ELife).
systemPipeTools systemPipeTools package extends the widely used systemPipeR (SPR) workflow environment with an enhanced toolkit for data visualization, including utilities to automate the data visualizaton for analysis of differentially expressed genes (DEGs). systemPipeTools provides data transformation and data exploration functions via scatterplots, hierarchical clustering heatMaps, principal component analysis, multidimensional scaling, generalized principal components, t-Distributed Stochastic Neighbor embedding (t-SNE), and MA and volcano plots. All these utilities can be integrated with the modular design of the systemPipeR environment that allows users to easily substitute any of these features and/or custom with alternatives.
tLOH tLOH, or transcriptomicsLOH, assesses evidence for loss of heterozygosity (LOH) in pre-processed spatial transcriptomics data. This tool requires spatial transcriptomics cluster and allele count information at likely heterozygous single-nucleotide polymorphism (SNP) positions in VCF format. Bayes factors are calculated at each SNP to determine likelihood of potential loss of heterozygosity event. Two plotting functions are included to visualize allele fraction and aggregated Bayes factor per chromosome. Data generated with the 10X Genomics Visium Spatial Gene Expression platform must be pre-processed to obtain an individual sample VCF with columns for each cluster. Required fields are allele depth (AD) with counts for reference/alternative alleles and read depth (DP).
TrajectoryGeometry Given a time series or pseudo-times series of gene expression data, we might wish to know: Do the changes in gene expression in these data exhibit directionality? Are there turning points in this directionality. Do different subsets of the data move in different directions? This package uses spherical geometry to probe these sorts of questions. In particular, if we are looking at (say) the first n dimensions of the PCA of gene expression, directionality can be detected as the clustering of points on the (n-1)-dimensional sphere.
TrajectoryUtils Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime inference results.
TraRe TraRe (Transcriptional Rewiring) is an R package which contains the necessary tools to carry out several functions. Identification of module-based gene regulatory networks (GRN); score-based classification of these modules via a rewiring test; visualization of rewired modules to analyze condition-based GRN deregulation and drop out genes recovering via cliques methodology. For each tool, an html report can be generated containing useful information about the generated GRN and statistical data about the performed tests. These tools have been developed considering sequenced data (RNA-Seq).
Travel Creates a virtual pointer for R’s ALTREP object which does not have the data allocates in memory. The pointer is made by the file mapping of a virtual file so it behaves exactly the same as a regular pointer. All the requests to access the pointer will be sent to the underlying file system and eventually handled by a customized data-reading function. The main purpose of the package is to reduce the memory consumption when using R’s vector to represent a large data. The use cases of the package include on-disk data representation, compressed vector(e.g. RLE) and etc.
treekoR treekoR is a novel framework that aims to utilise the hierarchical nature of single cell cytometry data to find robust and interpretable associations between cell subsets and patient clinical end points. These associations are aimed to recapitulate the nested proportions prevalent in workflows inovlving manual gating, which are often overlooked in workflows using automatic clustering to identify cell populations. We developed treekoR to: Derive a hierarchical tree structure of cell clusters; measure the proportions to parent (proportions of cells each node in the tree relative to the number of cells belonging its parent node), in addition to the proportions to all (proportion of cells in each node relative to all cells); perform significance testing using the calculated proportions; and provide an interactive html visualisation to help highlight key results.
tricycle The package contains functions to infer and visualize cell cycle process using Single Cell RNASeq data. It exploits the idea of transfer learning, projecting new data to the previous learned biologically interpretable space. We provide a pre-learned cell cycle space, which could be used to infer cell cycle time of human and mouse single cell samples. In addition, we also offer functions to visualize cell cycle time on different embeddings and functions to build new reference.
ttgsea Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens.
VarCon VarCon is an R package which converts the positional information from the annotation of an single nucleotide variation (SNV) (either referring to the coding sequence or the reference genomic sequence). It retrieves the genomic reference sequence around the position of the single nucleotide variation. To asses, whether the SNV could potentially influence binding of splicing regulatory proteins VarCon calcualtes the HEXplorer score as an estimation. Besides, VarCon additionally reports splice site strengths of splice sites within the retrieved genomic sequence and any changes due to the SNV.
vissE This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches.
wppi Protein-protein interaction data is essential for omics data analysis and modeling. Database knowledge is general, not specific for cell type, physiological condition or any other context determining which connections are functional and contribute to the signaling. Functional annotations such as Gene Ontology and Human Phenotype Ontology might help to evaluate the relevance of interactions. This package predicts functional relevance of protein-protein interactions based on functional annotations such as Human Protein Ontology and Gene Ontology, and prioritizes genes based on network topology, functional scores and a path search algorithm.
XNAString The XNAString package allows for description of base sequences and associated chemical modifications in a single object. XNAString is able to capture single stranded, as well as double stranded molecules. Chemical modifications are represented as independent strings associated with different features of the molecules (base sequence, sugar sequence, backbone sequence, modifications) and can be read or written to a HELM notation. It also enables secondary structure prediction using RNAfold from ViennaRNA. XNAString is designed to be efficient representation of nucleic-acid based therapeutics, therefore it stores information about target sequences and provides interface for matching and alignment functions from Biostrings package.
There are 22 new data experiment packages in this release of Bioconductor.
BeadSorted.Saliva.EPIC Raw data objects used to estimate saliva cell proportion estimates in ewastools. The FlowSorted.Saliva.EPIC object is constructed from saples assayed by Lauren Middleton et. al. (2021).
BioImageDbs The package provides a bioimage dataset for the image analysis using machine learning and deep learning. The dataset includes microscopy imaging data with supervised labels. The data is provided as R list data that can be loaded to Keras/tensorflow in R.
DExMAdata Data objects needed to allSameID() function of DExMA package. There are also some objects that are necessary to be able to apply the examples of the DExMA package, which illustrate package functionality.
emtdata This package provides pre-processed RNA-seq data where the epithelial to mesenchymal transition was induced on cell lines. These data come from three publications Cursons et al. (2015), Cursons etl al. (2018) and Foroutan et al. (2017). In each of these publications, EMT was induces across multiple cell lines following treatment by TGFb among other stimulants. This data will be useful in determining the regulatory programs modified in order to achieve an EMT. Data were processed by the Davis laboratory in the Bioinformatics division at WEHI.
ewceData This package provides reference data required for ewce. Expression Weighted Celltype Enrichment (EWCE) is used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses.
GenomicDistributionsData This package provides ready to use reference data for GenomicDistributions package. Raw data was obtained from ensembldb and processed with helper functions. Data files are available for the following genome assemblies: hg19, hg38, mm9 and mm10.
GSE13015 Microarray expression matrix platform GPL6106 and clinical data for 67 septicemic patients and made them available as GEO accession GSE13015. GSE13015 data have been parsed into a SummarizedExperiment object available in ExperimentHub. This data data could be used as an example supporting BloodGen3Module R package.
imcdatasets The imcdatasets package provides access to publicly available IMC datasets. IMC is a technology that enables measurement of > 40 proteins from tissue sections. The generated images can be segmented to extract single cell data. Datasets typically consist of three elements: a SingleCellExperiment object containing single cell data, a CytoImageList object containing multichannel images and a CytoImageList object containing the cell masks that were used to extract the single cell data from the images.
LRcellTypeMarkers This is an external ExperimentData package for LRcell. This data package contains the gene enrichment scores calculated from scRNA-seq dataset which indicates the gene enrichment of each cell type in certain brain region. LRcell package is used to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. For more details, please visit: https://github.com/marvinquiet/LRcell.
MACSdata Test datasets from the MACS3 test
examples are use in the examples of the MACSr
package. All 9
datasets are uploaded to the ExperimentHub
. The original data can
be found at: https://github.com/macs3-project/MACS/.
methylclockData Collection of 9 datasets, andrews and bakulski cord blood, blood gse35069, blood gse35069 chen, blood gse35069 complete, combined cord blood, cord bloo d gse68456, gervin and lyle cord blood, guintivano dlpfc and saliva gse48472”. Data downloaded from meffil. Data used to estimate cell counts using Extrinsic epigenetic age acceleration (EEAA) method Collection of 12 datasets to use with MethylClock package to estimate chronological and gestational DNA methylationwith estimators to use wit different methylation clocks
microbiomeDataSets microbiomeDataSets is a collection of microbiome datasets loaded from Bioconductor’S ExperimentHub infrastructure. The datasets serve as reference for workflows and vignettes published adjacent to the microbiome analysis tools on Bioconductor. Additional datasets can be added overtime and additions from authors are welcome.
MouseThymusAgeing This package provides data access to counts matrices and meta-data for single-cell RNA sequencing data of thymic epithlial cells across mouse ageing using SMARTseq2 and 10X Genommics chemistries. Access is provided as a data package via ExperimentHub. It is designed to facilitate the re-use of data from Baran-Gale et al. in a consistent format that includes relevant and informative meta-data.
msigdb This package provides the Molecular Signatures Database (MSigDB) in a R accessible objects. Signatures are stored in GeneSet class objects form the GSEABase package and the entire database is stored in a GeneSetCollection object. These data are then hosted on the ExperimentHub. Data used in this package was obtained from the MSigDB of the Broad Institute. Metadata for each gene set is stored along with the gene set in the GeneSet class object.
ObMiTi The package provide RNA-seq count for 2 strains of mus musclus; Wild type and Ob/Ob. Each strain was divided into two groups, and each group received either chow diet or high fat diet. RNA expression was measured after 12 weeks in 7 tissues.
preciseTADhub An experimentdata package to supplement the preciseTAD package containing pre-trained models and the variable importances of each genomic annotation used to build the model parsed into list objects and available in ExperimentHub. In total, preciseTADhub provides access to n=84 random forest classification models optimized to predict TAD/chromatin loop boundary regions and stored as .RDS files. The value, n, comes from the fact that we considered l=2 cell lines {GM12878, K562}, g=2 ground truth boundaries {Arrowhead, Peakachu}, and c=21 autosomal chromosomes {CHR1, CHR2, …, CHR22} (omitting CHR9). Furthermore, each object is itself a two-item list containing: (1) the model object, and (2) the variable importances for CTCF, RAD21, SMC3, and ZNF143 used to predict boundary regions. Each model is trained via a “holdout” strategy, in which data from chromosomes {CHR1, CHR2, …, CHRi-1, CHRi+1, …, CHR22} were used to build the model and the ith chromosome was reserved for testing. See https://doi.org/10.1101/2020.09.03.282186 for more detail on the model building strategy.
ptairData The package ptairData contains two raw datasets from Proton-Transfer-Reaction Time-of-Flight mass spectrometer acquisitions (PTR-TOF-MS), in the HDF5 format. One from the exhaled air of two volunteer healthy individuals with three replicates, and one from the cell culture headspace from two mycobacteria species and one control (culture medium only) with two replicates. Those datasets are used in the examples and in the vignette of the ptairMS package (PTR-TOF-MS data pre-processing). There are also used to gererate the ptrSet in the ptairMS data : exhaledPtrset and mycobacteriaSet
scpdata The package disseminates mass
spectrometry (MS)-based single-cell proteomics (SCP) datasets. The
data were collected from published work and formatted using the
scp
data structure. The data sets contain quantitative
information at spectrum, peptide and/or protein level for single
cells or minute sample amounts.
SimBenchData The SimBenchData package contains a total of 35 single-cell RNA-seq datasets covering a wide range of data characteristics, including major sequencing protocols, multiple tissue types, and both human and mouse sources.
SingleMoleculeFootprintingData This Data package contains data objcets relevanat for the SingleMoleculeFootprinting package. More specifically, it contains one example of aligned sequencing data (.bam & .bai) necessary to run the SingleMoleculeFootprinting vignette. Additionally, we provide data that are essential for some functions to work correctly such as BaitCapture() and SampleCorrelation().
STexampleData Collection of spatially resolved transcriptomics datasets in SpatialExperiment Bioconductor format, for use in examples, demonstrations, tutorials, and other purposes. The datasets have been sourced from various publicly available sources, and cover several technological platforms.
TENxVisiumData Collection of Visium spatial gene expression datasets by 10X Genomics, formatted into objects of class SpatialExperiment. Data cover various organisms and tissues, and include: single- and multi-section experiments, as well as single sections subjected to both whole transcriptome and targeted panel analysis. Datasets may be used for testing of and as examples in packages, for tutorials and workflow demonstrations, or similar purposes.
There are 7 new annotation packages in this release of Bioconductor.
AHLRBaseDbs Supplies AnnotationHub with LRbaseDb
Ligand-Receptor annotation databases for many species. All the SQLite files
are generated by our Snakemake workflow
lrbase-workflow. For the
details, see the README.md of lrbase-workflow.
AHMeSHDbs Supplies AnnotationHub with MeSHDb
NIH MeSH
annotation databases for many species. All the SQLite files and metadata.csv
are generated by our Snakemake workflow
mesh-workflow.
AHPathbankDbs The package provides a comprehensive mapping table of metabolites and proteins linked to PathBank pathways. The tables include HMDB, KEGG, ChEBI, CAS, Drugbank, Uniprot IDs. The tables are provided for each of the 10 species (“Homo sapiens”, “Escherichia coli”, “Mus musculus”, “Arabidopsis thaliana”, “Saccharomyces cerevisiae”, “Bos taurus”, “Caenorhabditis elegans”, “Rattus norvegicus”, “Drosophila melanogaster”, and “Pseudomonas aeruginosa”). These table information can be used for Metabolite Set (and other) Enrichment Analysis.
AHPubMedDbs Supplies AnnotationHub with some preprocessed sqlite, tibble, and data.table datasets of PubMed. All the datasets are generated by our Snakemake workflow pubmed-workflow. For the details, see the README.md of pubmed-workflow.
gwascatData This package manages a text file in cloud with March 30 2021 snapshot of EBI/EMBL GWAS catalog.This simplifies access to a snapshot of EBI GWASCAT. More current images can be obtained using the gwascat package.
MafH5.gnomAD.v3.1.1.GRCh38 Store minor allele frequency data from the Genome Aggregation Database (gnomAD version 3.1.1) for the human genome version GRCh38.
Orthology.eg.db Orthology mapping package, based on data from NCBI, using NCBI Gene IDs and Taxonomy IDs.
There is 1 new workflow package in this release of Bioconductor.
There are no new online books.
Changes in version 1.9.3 (2021-01-28)
Now defaulting to exclude sex chromosomes from model fitting
Also included sgc argument in twosamplecompare
Data frame output of ACEcall and twosamplecompare are now restricted to selected chromosomes
Changes in version 1.9.1 (2021-01-15)
accommodating fitting of chromosomes with only a single germline copy (e.g. X and Y in males)
added the option to specify which cellularities to include in squaremodel
option to save readCounts-object in runACE
Changes in version 0.0.99
Changes in version 3.1.5
Removed warning about future_options deprecation
Changes in version 3.1.4
bug fix loading bruker files
Changes in version 3.1.3 (2020-11-19)
nmr_pca_outliers_plot modified to show names in all boundaries of the plot
Changes in version 3.1.2 (2020-11-04)
Bug fix related with Bioconductor Renviron variable R_CHECK_LENGTH_1_CONDITION
Changes in version 3.1.1 (2020-10-30)
Modified order of autor list
Changes in version 3.1.0 (2020-10-22)
Changes in version 1.1.5 (2021-03-09)
Bugs fix: fix the bug of inflated p-values and inconsistent output formats.
Changes in version 1.1.4 (2021-02-28)
Bug fix: fix the bug when metadata contains only a single variable and some samples were removed with the minimum library size cutoff.
Changes in version 1.1.3 (2021-02-19)
Add a warning message for the case of the small number of taxa.
Changes in version 1.1.2 (2020-12-08)
Bug fix: fix the bug that the sampling fraction estimate will return a single number instead of a vector.
Changes in version 1.1.1 (2020-11-20)
Integrating with functions from the microbiome package.
Changes in version 1.54.0
NEW FEATURES
There is a new replacement package for the Inparanoid orthology packages, called Orthology.eg.db
This package uses NCBI orthology data to map NCBI Gene IDs between species using the usual select() interface
MODIFICATIONS
UniGene data have been removed from OrgDb and ChipDb packages
Gene type data have been added to OrgDb and ChipDb packages
Changes in version 1.53.0
NEW FEATURES
MODIFICATIONS
Changes in version 1.34.0
NEW FEATURES
Removed UniGene from OrgDb and ChipDb packages
Added Gene Type table to OrgDb and ChipDb packages
Added functionality to build Orthology.eg.db package which maps NCBI Gene IDs between species
RSQLite deprecated usage of dbGetQuery for database altering statements; updated to use dbExecute instead
Changes in version 2.99.0
MAJOR UPDATES
(2.99.0) The default caching location has changed. Instead of rappdirs::user_cache_dir using tools::R_user_dir. To avoid conflicting caches, a user will have to manage an old cache location before proceeding. Information for handling an old cache location is provided in the vignette.
(2.99.0) Another major change, a default caching location is automatically created in a non interactive session instead of using a temporary location. In an interactive session, a user is still prompted for permission.
Changes in version 2.23.0
USER-VISIBLE MODIFICATIONS
MODIFICATIONS
Changes in version 1.21.0
MODIFICATIONS
1.21.9 Add PNG as valid source type
1.21.4 Removed vignette for creating annotation hub package. Reference and refer to single vignette in AnnotationHub
1.21.3 Tags for database now combination of biocViews and meta$Tags. Also checks for valid AnnotationHub or AnnotationHubSoftware biocViews.
1.21.2 Add mtx.gz as valid source type
BUG CORRECTION
INTERNAL BUG CORRECTION
REMOVED
Changes in version 1.4.0
NEW FEATURES
(v 1.3.1) support Rawls() service (more fine-grained implementation / extension of the ‘Terra()’ orchestration API).
(v 1.3.2) introduce avworkspace_*() functions for viewing and updating workflow configurations.
(v 1.3.3) introduce avnotebooks_() functions for managing notebooks on workspaces and runtimes.
(v 1.3.11) introduce avtable_paged() for page-wise access to tables
(v 1.3.14) introduce avworkspace_clone() for cloning existing workspaces.
(v 1.3.21) avworkspaces() returns a tibble of available workspaces.
(v 1.3.24) gsutil_rsync() supports a regular expresion exclude = to exclude files from synchronization.
(v 1.3.24) avworkflow_localize() copies workflow control and / or output files to the local disk.
USER VISIBLE CHANGES
(v 1.3.1) service functions have signatures like fun(x, …, .body = list(y)), where x is a argument for the ‘URL’ of the RESTful interface, and y is an argument for the ‘BODY’ of POST and similar requests. The … provide backward compatibility, and is used to populate elements of .body; the full interface is required when URL and BODY have identically named arguments.
(v 1.3.10, 1.3.11) return ‘entity’ column with name ‘table_id’, rather than ‘name’.
(v 1.3.22) localize() / delocalize() warn when dry = TRUE, so that lack of localization is more apparent.
(v 1.3.24) gsutil_stat() returns a tibble summaring bucket status, rather than character().
(v 1.3.30) Add Referer: header to all Leonardo requests
BUG FIXES
(v 1.3.6) when .body consists of 1 argument, it is represented as an unnamed set.
(v 1.3.7) allow positional matching for .body arguments
(v. 1.2.1 / 1.3.31) drs_stat() returns a single record per URL when multiple hashes available.
Changes in version 1.2.0
New Features
Bug Fixes
Changes in version 1.5.5 (2021-01-31)
Added ThreeMostPairBam to support paired-end bam.
Changes in version 1.5.4 (2021-01-10)
Fixed the bug in PASEXP_3UTR.
Changes in version 1.5.3 (2021-01-05)
Updated the link of PolyA_DB.
Changes in version 1.5.1 (2020-12-07)
Updated Imports and authors.
Changes in version 1.8.4 (2021-05-12)
R > v4.0.0 is now required
Update documentation & vignette
Code cleaning
Changes in version 1.8.3 (2021-04-05)
artmsProtein2SiteConversion now supports uniprot id isoforms (thanks Emily King)
Update vignette to make clearer how to provide several protein ids in “normalization_reference” (thanks Olga Schubert)
Changes in version 1.8.2 (2021-03-18)
artmsProtein2SiteConversion: New PTM available as argument, a new
user defined PTM:XXX:yy
. Check documentation to find out more
QC plots: by default, all qc plots are now output to a folder directory, by type (qc-basic, qc-extended, qc_summary)
artMS working directory: artMS will create all the folders and subfolders relative to the working directory. No need to specify the full path to the working directory, but the user must set the working directory: setwd(“/path/to/working/directory/”)
Configuration file data object updates:
“output” the user can add a folder where would like to have the output results file. For example, “output: results_202003/example-results.txt” would create the “resutls_202003” folder (if it does not exist) with all the results files available there
“LFC” (log2fc) updated to range -0.58 to 0.58, i.e., a fold change larger than a 1.5 (instead of 2 as before)
Update and improve documentation and vignette
Several bug fixes
Changes in version 1.8.1 (2020-10-27)
Update “plotPCA” message
Update documentation and vignettes
Changes in version 2.1.3
NEW CITATION
ASpli was published on Bioinformatics
https://doi.org/10.1093/bioinformatics/btab141
FEATURES
BUG FIXES
gbCounts identifies correctly NA in junction’s name
minAnchor in jCounts was hardcoded so it had no impact on analysis. It is being passed correctly now.
Changes in version 2.1
NEW FUNCTIONS AND FUNCTIONALITIES
New locus plot helper function .plotGenePattern.
plots coverage, junctions and architecture
FEATURES
Enhancement to the vignettes.
Quick start section was modify in order to use gtf and bam files provided by ASpli package
Add new section ASpli overview in vignette
BUG FIXES
binGenome function assigns correctly bins located at the start and the end of each gene
binGenome function calculates correctly gene range overlap
Changes in version 1.15.11
Break the limitation of sequence length must have ends less than or equal to 536870912.
Changes in version 1.15.10
fix the issue that idxstatsBam return values with “*”
Changes in version 1.15.9
Add rmarkdown as suggest package.
Changes in version 1.15.8
update documentation for the case when no BSgenome object is available.
Changes in version 1.15.7
fix the NA values for TSSEscore when infinite value is in the data.
Changes in version 1.15.6
fix the missing link of documentation for rtracklyaer:import.
Changes in version 1.15.5
remove duplicates when shift reads.
Changes in version 1.15.4
Fix the issue when empty object input into exportBamFile.
Changes in version 1.15.3
Reuse header when exportBamFile in splitGAlignmentsByCut function.
Changes in version 1.15.2
Fix the tag MC in exportBamFile function.
Changes in version 1.15.1
write exportBamFile function to replace rtracklayer::export.bam.
Changes in version 1.13
Changes in version 0.99.0 (2020-08-05)
Changes in version 0.99.1
NEW FEATURES
SIGNIFICANT USER-VISIBLE CHANGES
BUG FIXES
None.
Changes in version 0.99.0
NEW FEATURES
SIGNIFICANT USER-VISIBLE CHANGES
BUG FIXES
Changes in version 0.99.0 (2021-02-22)
Changes in version 2.3.4 (2021-04-18)
Add missing import from scran
Changes in version 2.3.3 (2021-04-14)
Version bump to trigger new build
Changes in version 2.3.2 (2021-04-14)
Bug fixes in handling of divide and conquer inference.
Changes in version 2.3.1 (2020-12-14)
scaling of mu.mu
in .EmpiricalBayesMu
to match the scale given by
spike-ins
scaling of mu0
in .BASiCS_MCMC_Start
to match the scale given by
spike-ins
when using an EB prior for mu
lower minimum tolerance mintol_mu
(1e-5 instead of 1e-3) as a
default value
in .BASiCS_MCMC_ExtraArgs
Changes in version 1.4.0
Support installation from Python package directories on the file system.
Clean Conda package directories during a system installation to reduce disk usage.
Changes in version 1.4.0
Avoid caching the installer when performing a system installation in installConda(). Otherwise, cache in the external directory to avoid requiring/polluting BiocFileCache’s cache.
Officially give up on Windows 32-bit support in installConda().
Migrated activateEnvironment() back here, from basilisk.
Added cleanConda() utility to clean the Conda environment.
Added setCondaPackageDir() to set the Conda package cache directory.
Changes in version 1.8.0
Migrate findMutualNN() to BiocNeighbors.
Support d=NA in multiBatchPCA() for more convenient disabling of PCA in calling functions.
Bugfix for d=NA with specified subset.row= in fastMNN().
Added the applyMultiSCE() function to easily apply functions across main/alternative Experiments from multiple SingleCellExperiment inputs.
Added the mnnDeltaVariance() function to compute diagnostics from the variances of the differences between MNN pairs.
Added the quickCorrect() function to quickly perform intersection, normalization, feature selection and correction.
Added some clustering-based diagnostics (clusterAbundanceVar(), clusterAbundanceTest() and compareMergedClusters()) from the OSCA book.
File-backed matrices are now realized into memory prior to multiBatchPCA().
Changes in version 1.1.3
Minor improvements and fixes
getRDS() updated with new URL.
Changes in version 1.1.2
Minor improvements and fixes
clusterPlot() accepts character vectors and factors as arguments to label.
Changes in version 1.1.1
Minor improvements and fixes
spatialPreprocess() uses exact rather than approximate PCA by default.
Changes in version 1.1.0
New Bioconductor devel (3.13)
Changes in version 2.8.0
Improve the efficiency of sparse row subsetting in non-DelayedArray rowBlockApply().
Avoid overhead of DelayedArray block processing when DelayedArray is pristine and the type is supported.
Migrated whichNonZero() from scuttle.
Added toCsparse() to make it easier to convert SparseArraySeeds to CsparseMatrixes.
Added realizeFileBackedMatrix() to, well, realize a DelayedMatrix with file-backed components.
Changes in version 2.7.1 (2020-11-03)
Changes in version 1.7.3
Add approaches based on pValue and qValue to generate present/absent calls
Default approach to generate calls is pValue
Add function merging_libraries
allowing to merge calls
per condition
Changes in version 1.27
BUG FIX
(1.27.17) Update support site watched tags. tags are case insensitive
(1.27.15) Reporting checking of vignette despite package type (@lshep, #136)
(1.27.9) Allow portability of child Rmd documents via parseFile
(1.27.3) Correct check for if package already exists in CRAN/Bioc
(1.27.3) Correct check for single colon use
(1.27.2) Correct path to R license database file by calling R.home(‘share’).
NEW FEATURES
(1.27.16) Check vignettes for all package types (@lshep, #136)
(1.27.12) Check for LazyData: TRUE
in the DESCRIPTION (@lshep,
#128)
(1.27.11) R version dependency check in the ‘DESCRIPTION’ is now a ‘NOTE’ (@lshep, #126)
(1.27.10) Check for ‘error’ and other keywords in signaler functions, ‘message’, ‘warning’, and ‘stop’ etc. (@hpages, #125)
(1.27.8) Check for ‘tests’ entry in ‘.Rbuildignore’
(1.27.7) Removed BiocCheck and BiocCheckGitClone installation
scripts;
recommended usage is BiocCheck()
(1.27.6) Check that a user has the package name in watched tags of support site
(1.27.5) Check for ‘paste’ / ‘paste0’ in signaler functions, ‘message’, ‘warning’, and ‘stop’ (@LiNk-NY, #64)
(1.27.4) Check for downloads from external resources (github, gitlab, bitbucket, dropbox; @LiNk-NY, #75)
(1.27.1) Check that licenses do not exclude classes of users, e.g., non-academic users.
Changes in version 1.99
MAJOR UPDATES
(1.99.0) The default caching location has changed. Instead of rappdirs::user_cache_dir using tools::R_user_dir. To avoid conflicting caches, a user will have to manage an old cache location before proceeding. Information for handling an old cache location is provided in the vignette.
(1.99.0) Another major change, a default caching location is automatically created in a non interactive session instead of using a temporary location. In an interactive session, a user is still prompted for permission.
(1.99.0) An enviornment variable may be set system wide or user wide to control the default caching location: BFC_CACHE. Note: do not use R variables or command line export to set this variable. It must be set system wide or user wide for reproducibility in future R sessions or else it must be specified upon ever usage. It must be set before calling library(BiocFileCache) to take effect.
(1.99.0) Fixes partial argument matching error in SQL function SQLExecute
(1.15.1) Added file locking for thread-safe SQL operations. Thanks for the PR @LTLA
BUG FIX
Changes in version 1.10.0
Migrated findMutualNN() from batchelor.
Vendored the RcppAnnoy headers for greater reproducibility.
Added a distance=”Cosine” option for all algorithms.
Changes in version 1.26
USER VISIBLE CHANGES
Changes in version 1.10.0
NEW FEATURES
biocPkgRanges
allows for easy identification of package statuses
from
the build report for a specified range of packages (ordered
alphabetically)
biocBuildEmail
provides core-team functionality for sending email
notifications to package maintainers
SIGNIFICANT USER-VISIBLE CHANGES
biocBuildEmail
allows for saving a credentials file for email
authentication via the credFile
argument
setCache
uses tools::R_user_dir("BiocPkgTools", "cache")
instead
of
rappdirs::user_cache_dir
BUG FIXES
biocBuildReport
accounts for some packages whose DESCRIPTION
file
is
malformed
biocBuildReport
updated to changes in the build report format
Changes in version 2.20.0
Changes in version 1.1.10
NEW FEATURES
Added the function use_bioc_coc() as requested by Luke Zappia et al.
Changes in version 1.1.9
NEW FEATURES
Now use_bioc_github_action() has a docker argument which controls whether to build a docker image at the end of the GHA workflow (only on Linux) as requested by Kévin Rue-Albrecht.
Changes in version 1.1.7
BUG FIXES
Switch to match usethis 2.0.1 which changed a lot of the internal code in biocthis.
Changes in version 1.1.4
NEW FEATURES
Switched from knitcitations to RefManageR given the discussion at https://github.com/cboettig/knitcitations/issues/107.
Changes in version 1.1.3
BUG FIXES
Changes in version 1.59.0
ENHANCEMENT
(1.57.3) Add biocViews term DifferentialDNA3DStructure
(1.57.2) Add CRAN packages to reverse dependency list
(1.57.1) Add biocViews term Chlamydomonas_reinhardtii
Changes in version 0.99.11 (2021-05-17)
Change example inside BiodbConfig to avoid misinterpretation of
set('cache.directory', '~/my.biodb.cache')
, leading to believe that
some files are written inside USER HOME folder when running the
example.
Changes in version 0.99.10 (2021-05-07)
Correct documentation of C++ function.
Changes in version 0.99.9 (2021-05-07)
Solving some NOTES from BiocCheck.
Correct example in Progress class.
Changes in version 0.99.8 (2021-05-06)
Correct template travis.yml for extensions: missing deps install, run all checks.
Improve template Makefile for extensions.
Add missing test-cpp.R template file for running C++ tests from testthat.
Limit by default the entries to test to one entry inside generic tests.
Improve vignettes.
Rename default vignette into “biodb.Rmd”.
Changes in version 0.99.7 (2021-04-27)
Renamed Biodb class into BiodbMain in order to avoid “Rd warning: Previous alias or file overwritten by alias: biodb” on Windows platform.
Implement newInst() global function for creating new BiodbMain instance.
Changes in version 0.99.6 (2021-04-27)
Rebuilding doc.
Changes in version 0.99.5 (2021-04-27)
Add missing parameters documentation for runGenericTests().
Changes in version 0.99.4 (2021-04-27)
Move long tests to separate directory “long”.
Changes in version 0.99.3 (2021-04-27)
Added “biodb” as watched tag on my profile on support site https://support.bioconductor.org/.
Changes in version 0.99.2 (2021-04-27)
Switch to MassBank extract for testing MassCsvFile and MassSqlite connectors.
Changes in version 0.99.1 (2021-04-26)
Remove Git files refused by BiocCheckGitClone.
Use CHECK_RENVIRON in local tests.
Correct condition in BiodbEntryFields::getRealName() that did not pass check.
Add all doc files man/*.Rd for BiocCheck run on http://bioconductor.org.
Changes in version 0.99.0 (2021-04-22)
Submitted to Bioconductor
Changes in version 2.48.0
NEW FEATURES
getSequence() now allows the cache to be turned off via the ‘useCache’ argument.
Automatic detection of SSL issues with Ensembl, and appropriate settings applied to httr functions used by biomaRt.
BUG FIXES
Addressed issue with getSequence() and ID types that are not available on the ‘sequences’ page. This could result in truncated sequences being returned from a query.
getBM() would fail if it found a cache entry, but the file was corrupted. Invalid entries are now detected and deleted if encountered.
Changes in version 0.99.0 (2021-03-05)
Changes in version 1.19.2
MINOR MODIFICATION
Changes in version 0.99.38
Changes in version 1.2.0
Previously zero-weight edges are now assigned a nominal positive weight in makeSNNGraph().
Added MbkmeansParam() to wrap mini-batch k-means from mbkmeans.
Added SOMParam() to wrap self-organizing map implementation from kohonen.
Added AffinityParam() to wrap the affinity propagation code from apcluster.
Added DbscanParam() to provide a custom DBSCAN implementation with automatic eps choice.
Added PamParam() to wrap the PAM implementation from cluster.
Added ClaraParam() to wrap the CLARA implementation from cluster.
Added AgnesParam() to wrap the agglomerative nesting method from cluster.
Added DianaParam() to wrap the divisive analysis method from cluster.
Added clusterSweep() to easily perform parameter sweeps via clusterRows().
Added linkClusters() to find relationships between clusters in different clusterings.
Added compareClusterings() to compute similarities between multiple clusterings.
Added nestedClusters() to quantify the degree of nesting across two clusterings.
Moved objects into objects$kmeans for KmeansParam() when full=TRUE.
Moved objects into objects$hclust for HclustParam() when full=TRUE.
Added clusterRMSD() to compute the root-mean-squared-deviation for each cluster.
Changes in version 2.1.2
BUG FIXES
Changes in version 1.5.2 (2020-12-04)
Changes in version 1.34.0
BUG FIXES
Reform the CTSS class. New accessor: CTSS()
(with no dot).
Correct a class error when loading BAM files. (Closes #36).
Use the BSgenome object from the main environment if available.
Changes in version 1.47.1
BUG FIXES
Changes in version 2.1.0
Changes in version 1.14.0 (2021-04-28)
New Features
Heatmaps can now be stored as PDF files.
Functions now show shorter and more clearer messages.
Package no longer requires Java Runtime Environment for storing excel files and it is compatible with older 32 bit operating systems.
log z-scores provided by cgdsr are used instead of z-scores.
Changes in version 2.4.0
New features
Bug fixes and minor improvements
Changes in version 0.1.1
New features
Improved usability by changing descriptions and adding interactive tours
Changes in version 0.1.0
New features
much of the functionality available, in a proof of concept format.
Changes in version 0.0.1
New features
Changes in version 1.7.7 (2021-04-12)
Added handling for sparse matrices
Changes in version 1.7.6 (2021-04-04)
Added functions for creating HTML reports
Fixed bug in decontX plotting
Changes in version 1.7.4 (2021-03-09)
Enable input of raw/droplet matrix into decontX to estimate ambient RNA
Changes in version 0.99.0
Changes in version 0.99.11 (2021-05-11)
Bug fixes and documentation update for Bioconductor release
Changes in version 0.99.0 (2020-09-02)
Changes in version 2.16.0
Changes in version 3.25.6
Fix a bug in estLibSize introduced by last push.
Changes in version 3.25.5
Add LazyDataCompression in description
Changes in version 3.25.4
Add choice endMinusStart to annotatePeakInBatch.
Changes in version 3.25.3
fix the missing link of documentation for rtracklyaer:import.
Changes in version 3.25.2
update documentation.
update findEnhancers to for known interaction data
Changes in version 3.25.1
fix the bug for genomicElementDistribution when the peak length is zero.
Changes in version 1.27.4
https://github.com/YuLab-SMU/ChIPseeker/pull/146
Changes in version 1.27.3
https://github.com/YuLab-SMU/ChIPseeker/pull/144
Changes in version 1.27.2
https://github.com/YuLab-SMU/ChIPseeker/issues/142
Changes in version 1.27.1
Changes in version 1.1.3
Major Changes
Support “multi-feature” analysis, e.g. parallel analysis of multiple features (bins, peaks or gene) on the same object.
New “Coverage” tab & functions generate_coverage_tracks() and plot_coverage_BigWig() to generate cluster coverage tracks and interactively visualise loci/genes of interest in the application.
New inter- and intra-correlation violin plots to vizualise cell correlation distribution between and within clusters.
New normalization method : TF-IDF combined with systematic removal of PC1 strongly correlated with library size.
Simple ‘Copy Number Alteration’ approximation & visualization using ‘calculate_CNA’ function for genetically re-arranged samples, provided one or more control samples.
New generate_analysis() & generate_report() functions to run a full-on ChromSCape analysis and/or generate an HTML interactive report of an existing analysis.
Supports ‘custom’ differential analysis to find differential loci between a subset of samples and/or clusters.
New pathway overlay on UMAP to visualize cumulative pathways signal directly on cells.
Now supports ‘Fragment Files’ input (e.g. from 10X cell ranger scATAC pipeline), using a wrapper around ‘Signac’ package FeatureMatrix() function.
New ‘Contribution to PCA’ plots showing most contributing features and chromosome to PCA.
Restructuration of the ChromSCape directory & faster reading/saving of S4 objects using package ‘qs’.
Minor Changes
RAM optimisation & faster pearson cell-to-cell correlations with ‘coop’ package, and use of ‘Rcpp’ for as_dist() RAM-efficient distance calculation.
Faster correlation filtering using multi-parallel processing.
plot_reduced_dim now supports gene input to color cells by gene signal.
All plots can now be saved in High Quality PDF files.
Changed ‘geneTSS’ to ‘genebody’ with promoter extension to better reflect the fact that mark spread in genebodies.
Possibility to rename samples in the application.
Downsampling of UMAPs & Heatmaps for fluider navigation.
Changed ‘total cell percent based’ feature selection to manual selection of top-covered features, as the previous was srongly dependent on the experiment size.
Faster sparse SVD calculation.
Faster differential analysis using pairWise Wilcoxon rank test from ‘scran’ package.
Changes in version 0.99.0
Overview:
New functionalities:
Input dataset read an creation
CIMICE analysis and CPMC inference
Output data visualization
Changes in version 1.5.3
Changes in version 1.29.1
Changes in version 3.99.1
Add new data set, DE_GSE8057, which contains DE genes obtained from GSE8057 (2020-03-08, Mon)
Changes in version 3.99.0
Add KEGG enrichment analysis of Human Gut Microbiome data (2021-02-20, Sat)
Changes in version 3.19.1
Changes in version 1.3.3 (2021-02-28)
Launch shiny app with run_clustifyr_app()
Plot and GO for most divergent ranks in correlation of query vs reference
Changes in version 1.3.2 (2021-02-25)
build_atlas()
for combining references
More Q&A
Changes in version 1.3.1 (2020-12-26)
Q&A section
Now defaults to top 1000 variable genes in Seurat (including v4)
Bug fixes
Changes in version 1.5.2
SIGNIFICANT USER-VISIBLE CHANGES
loadCNVcalls() does not check cnvs.file names by default when loading (read.csv()) the cnvs.file
loadCNVcalls() allows optional check.names.cnvs.file parameter
Vignette updated
MINOR
Added rmarkdown to Suggets in DESCRIPTION file
Changes in version 1.5.1
BUG FIXES
SIGNIFICANT USER-VISIBLE CHANGES
plotVariantsForCNV() allows two new parameters for customize legend visualization
plotAllCNVs() allows ‘genome’ parameter to work with different genomes
MINOR
Minor vignette fixes
Other minor fixes
Changes in version 0.99.3 (2021-04-14)
Changes in version 1.9.1
add uniquely_high_in_one_group
method in get_signatures()
.
add compare_partitions()
.
parallel computing is implemented with foreach + doParallel
Changes in version 1.9.0
use row/column* family functions in adjust_matrix()
to reduce the
memory
usage as well as improve the speed.
Changes in version 1.23.1 (2021-05-16)
Changes in version 2.7.10
anno_simple()
: text symbols can have nchar > 1.
anno_text()
: add show_name
argument.
Changes in version 2.7.9
add frequencyHeatmap()
.
add Heatmap3D()
.
Changes in version 2.7.8
add cluster_between_groups()
.
add graphics
argument in anno_block()
.
Changes in version 2.7.7
discrete numeric legend labels are in correct order now.
parallel is implemented with foreach + doParallel
expression is properly processed for discrete legends
adjust_dend_by_x()
: simplified the representation of units.
number of split can be the same as number of matrix rows/columns.
Changes in version 2.7.6
Legend()
: add a new argument grob
.
Changes in version 2.7.5
anno_block()
: add labels_offset
and labels_just
.
anno_lines()
: show_points
can be a vector.
pheatmap()
: support kmeans_k
.
Changes in version 2.7.4
add save_last
option in ht_opt()
.
Changes in version 2.7.1
normalize_comb_mat()
: add full_comb_sets
and complement_set
arguments to control
full sets of combination sets.
adjust the space of column title according to ggplot2.
Legend()
: for title_position == “lefttop”, the title position is
adjusted.
Legends are automatically adjusted according to the device size when resizing the device.
Legend()
: add interval_dist
to control the distance of two
neighbouring breaks.
Fixed a bug that it crashes Rstudio
make_comb_mat()
: print warning messages when there are NA values in
the matrix.
temporary solution for woking under retina display with Rstudio
add bin_genome()
and normalize_genomic_signals_to_bins()
print messages if directly sending anno_*()
functions to
top_annotation
or similar arguments.
pheatmap()
: set heatmap name to “ “ so that there is no legend
title by default.
also translate stats::heatmap()
and gplots::heatmap.2()
.
move all code for interactive heatmap to InteractiveComplexHeatmap package.
Changes in version 0.99.0
Changes in version 0.99.343 (2021-04-09)
Submission to Bioconductor
NAMESPACE
Exported new function conclusCacheClear()
DESCRIPTION
Removed LazyData: true.
DataFormatting.R
Updated documentation
loadDataset.R
Simplified nested “if” in loadDataset.R
methods-normalization.R
Modified the use of getBM to retrieve only genes of the count matrix (instead of the all database)
test_setters.R/test_getters.R
Simplified nested “if” in loadDataset.R
test_loadData.R
Adapted the unit tests to the new format of coldata and rowdata
test_scRNAseq-methods.R
Used tempdir() for output directory
inst
New data generated
vignette
Changes in version 0.99.0 (2021-03-01)
Changes in version 0.99.6
Update vignette: add MA plotting and DEA sections, and example of bad MA plot
Changes in version 0.99.5
depends: R4.1
updated BugReports link
Changes in version 0.99.4
negative input values are not allowed
warn and automatically replace zero input values by NA
bugfix in warning message formatting
Changes in version 0.99.3
bugfix in float specification of warning message
Changes in version 0.99.2
bugfix remove stray Rproj file
Changes in version 0.99.1
bugfix in documentation examples
Changes in version 0.99.0
initial release
Changes in version 0.99.2
Changes in version 1.26.0
filterWindowsGlobal() finally behaves correctly for variable-width data=.
normFactors() and normOffsets() accept DGEList inputs in their object= and se.out= arguments.
mergeResults() and friends now default to taking tab= from the mcols() of the inputs.
Changes in version 1.10.0
Improvements to graphical interface functions:
Major changes
Bug fixes and minor changes
Changes in version 1.1.0 (2020-10-02)
Changes in version 0.99.0 (2021-02-19)
Changes in version 1.3.6 (2021-04-23)
scaleImages accepts numeric vector value
Changes in version 1.3.5 (2021-04-07)
Added measureObjects function
Changes in version 1.3.4 (2021-03-20)
Support on disk representation of images
Changes in version 1.3.3 (2021-01-24)
Added snapshot tests for shiny
Support win32 again
Changes in version 1.3.2 (2021-01-12)
Updated citation
Changes in version 1.3.1 (2020-12-01)
Allow thick border contours
Changes in version 3.11
API Changes
Fixes/internal changes
Add CytoML XSD to installation
Changes in version 3.10
API Changes
Change handling of quad gates according to RGLab/cytolib#16
Renaming of methods:
compare.counts -> gs_compare_cytobank_counts
Renaming of classes:
flowJoWorkspace -> flowjo_workspace
Fixes/internal changes
Changes in version 1.29.1
Changes in version 1.3.1
change cmdscale.out for eigen.vectors in methyl_MDS_plot
Changes in version 1.3.0
update with new Bioc version
Changes in version 0.99.0 (2021-01-25)
Changes in version 2.7.3 (2021-04-01)
Fix: changed example for adjustSignaturesForRegionSet() to limit memory usage (previous example produced error during check on Windows for arch ‘i386’).
Fix: made sure that the extension of genomic regions by half the sequence pattern (needed for the adjustSignaturesForRegionSet() function) does not result in out-of-bounds regions.
Changes in version 2.7.2 (2021-03-26)
Updated readAlexandrovSignatures() to read the COMSIC signature format v3.2 (published in March 2021).
Changes in version 2.7.1 (2021-03-21)
Updated readAlexandrovSignatures() to add the possibility to read COSMIC signatures of version 3.1 directly from an Excel file (as provided on the COSMIC website).
Added possibility to adjust/normalize mutational signatures to specific subsets of the human genome (defined by means of GRanges objects). The adjustment/normalization is performed accoring to the nucleotide frequencies in the specified regions (with respect to nucleotide frequncies in the reference sequences, e.g., the whole genome, for which the signatures were derived in the first place).
Changes in version 0.99.0
New features
Methods
New decouple() integrates the various member functions of the decoupleR statistics for centralized evaluation.
New family decoupleR statists for shared documentation is made up of:
Converters
Changes in version 0.99.0 (2021-03-21)
Changes in version 1.99.3 (2013-07-25)
Updates
A few changes to shearwater vignette
Renamed arguments pi.gene and pi.backgr in makePrior()
Bugfixes
Fixed bug in bf2Vcf() when no variant is called
Changes in version 1.99.2 (2013-07-11)
Updates
Updated CITATION
Added verbose option to bam2R to suppress output
Changed mode() to “integer” for value of loadAllData()
Bugfixes
Fixed bug when only one variant is called in bf2Vcf()
Changes in version 1.99.1 (2013-06-25)
Updates
Using knitr for prettier vignettes
Including shearwater vignette
Bugfixes
fixed issues with deletions in bf2Vcf()
makePrior() adds background on all sites
Changes in version 1.99.0 (2013-04-30)
Updates
New shearwater algorithm
Including VCF output through summary(deepSNV, value=”VCF”)
Changes in version 1.27.1
Changes in version 0.18.0
NEW FEATURES
Implement ConstantArray objects. The ConstantArray class is a DelayedArray subclass to efficiently mimic an array containing a constant value, without actually creating said array in memory.
Add scale() method for DelayedMatrix objects.
Add sinkApply(), a convenience function for walking on a RealizationSink derivative and filling it with blocks of data.
rbind() and cbind() on sparse DelayedArray objects are now fully supported.
Delayed operations of type DelayedUnaryIsoOpWithArgs now preserve sparsity when appropriate.
SIGNIFICANT USER-VISIBLE CHANGES
BUG FIXES
Subsetting of a DelayedArray object now propagates the names/dimnames, even when drop=TRUE and the result has only 1 dimension (issue #78).
log() on a DelayedArray object now handles the ‘base’ argument.
Fix issue in is_sparse() methods for DelayedUnaryIsoOpStack and DelayedNaryIsoOp objects.
cbind()/rbind() no longer coerce supplied objects to type of 1st object (commit f1279e07).
Fix small issue in dim() setter (commit c9488537).
Changes in version 1.14.0
Fix for missing na.rm= argument in AvgsPerSet functions.
DelayedMatrixStats no longer has a hard requirement on HDF5Array or BiocParallel.
Correct handling of drop= by quantile functions (<URL: https://github.com/PeteHaitch/DelayedMatrixStats/pull/71>).
Fix 2 issues with how the center argument is handled (<URL: https://github.com/PeteHaitch/DelayedMatrixStats/pull/69>).
Changes in version 1.0.0
Changes in version 1.2.0
Changes in version 1.7.2 (2021-03-25)
Major internal changes to the depeche function, with two user consequences: o The dualDepeche option is deprecated, as it made the function very heavy to maintain and was not flexible enough to be of great use. o The interface to dAllocate is much improved, allowing for smooth allocation of new data to an established model, which makes large dualDepeche runs, constructed outside of the function, possible in a more versatile way than previously.
Changes in version 1.7.1 (2021-02-02)
Adding the option of not scaling the data within the depeche function
Condensing the code for the depeche scaling procedure
Changes in version 1.31.16
Turning off outlier replacement with glmGamPoi fitting.
Changes in version 1.31.15
Added ‘saveCols’ in results() and lfcShrink() to pass metadata columsn to output.
Changes in version 1.31.13
Allow additional arguments to be passed to data-accessing functions in integrateWithSingleCell().
Changes in version 1.31.2
Fixed interface with glmGamPoi so that normalizationFactors can be used. Thanks to Michael Schubert for spotting this and to Constantin Ahlmann-Eltze for pointing out the fix.
Changes in version 1.5.2 (2021-05-05)
Update vignette for dispersion estimation
Changes in version 1.4.2 (2020-09-30)
Fix figure scaling issue in vignette
Changes in version 0.99.0
Changes in version 1.3.18
Using data.table instead of data.frame for modify-in-place.
Added support for Metabolomics DIA data.
Hierarchical clustering based alignment.
Create a child run (features + chromatograms) from two parents.
Added support for sqMass files.
Changes in version 1.3.5
Supporting transition level intensity for SAINTq
Added support for pyopenms.
Added support for sqMass files.
Added parallelization using BiocParallel.
Using context-specific qvalues to determine reference.
Alignment is done over multipeptide instead of multiprecursor.
Precursors of a peptides are forced to have same feature-RT.
Savitzky Golay smoothing in C++.
Fast and light global alignment functions.
Changes in version 3.2
New type of plot: dba.plotProfile()
Can mix single-end and paired-end bam files
Various bug fixes
Changes in version 1.10.2
Added helper functions to compute the exact moments, so that the user can characterise the systematic biases in the diffusion scores
Changes in version 1.10.1
Changes in version 0.99.27 (2021-04-06)
major speed-up or gene-level calculations
fixed bug using the wrong default coefficient with DEXSeq
Changes in version 0.99.13 (2021-03-04)
fixed misnamed variable bug when creating annotation from ensembldb
formatting and renaming changes to conform with Bioc standards
Changes in version 0.99.10 (2021-02-05)
submitted to BioConductor
improvement on limma::diffSplice
differential 3’ UTR usage
Changes in version 1.4
Added 1 new Visualization function: ‘dittoFreqPlot()’.
Added interaction with ‘rowData’ of SE and SCEs via a ‘swap.rownames’ input, e.g. to simplify provision of ‘var’s via symbols vs IDs.
Improved & expanded ‘split.by’ capabilities by: 1- adding them to ‘dittoBarPlot()’, ‘dittoDotPlot()’, and ‘dittoPlotVarsAcrossGroups()’; 2- adding ‘split.adjust’ input to all functions for passing adjudstments to underlying ‘facet_grid()’ and ‘facet_wrap()’ calls; 3- adding ‘split.show.all.others’ input to ‘dittoDimPlot()’ and ‘dittoScatterPlot()’ to allow the full spectrum of points, rather than just points excluded with ‘cells.use’, to be shown as light gray in the background of all facets; 4- Bug fix: splitting now works with labeling of Dim/Scatter plots, with label position calculated per facet, and without affecting facet order.
Improved ‘dittoPlot()’-plotting engine (also effects ‘dittoPlotVarsAcrossGroups()’, and ‘dittoFreqPlot()’) by: for y-axis plotting, 1- extended geom dodging to also work on jitters when ‘color.by’ is used to add subgroupings & 2- added a ‘boxplot.lineweight’ control option; for x-axis / ridge plotting, 1- added an alternative histogram-shaping option (Try ‘ridgeplot.shape = “hist”’) & 2- improved use of white space via a new ‘ridgeplot.ymax.expansion’ input.
Standardized output logic so that ‘do.hover = TRUE’ will lead to plotly conversion even when ‘data.out = TRUE’.
‘dittoHeatmap()’: ‘order.by’ can also now accept multiple gene/metadata names to order by & bug fix: when given an integer vector, that vector will be used directly to set the order of heatmap columns.
‘dittoBarPlot()’: grouping & ‘var’ order control improved via addition of a ‘retain.factor.levels’ input.
Changes in version 3.17.1
Changes in version 1.12.0
Added BPPARAM= to read10xCounts() for parallelized reading of multiple samples.
Gave all the *Ambience() functions better names, and soft-deprecated the current versions.
Added ambientContribSparse() to estimate the ambient contribution under sparsity assumptions.
Added cleanTagCounts() to remove undesirable barcodes from tag count matrices.
Converted all matrix-accepting functions to S4 generics to support SummarizedExperiment inputs.
emptyDrops() will now coerce all DelayedArray inputs into wrapped SparseArraySeeds.
Setting test.ambient=TRUE in emptyDrops() will no longer alter the FDRs compared to test.ambient=FALSE. Added test.ambient=NA to retain back-compatible behavior.
Bugfix for correct use of redefined lower when by.rank= is set in emptyDrops().
Added a constant.ambient=TRUE option to hashedDrops() to better support experiments with very few HTOs.
Changes in version 0.99.0 (2021-01-11)
Changes in version 1.3.01 (2020-11-06)
Dune now accepts multiple metrics
Dune now uses the NMI by default
Changes in version 1.21.2
Changes in version 2.27.1
DESeq dependency removal
Added extra warning about RPKM usage
Removed Defunct functions
Changes in version 3.34.0
New function featureCounts2DGEList() that converts results from Rsubread::featureCounts() to DGELists.
Remove the “ndim” argument of plotMDS.DGEList().
read10X() now counts the number of comment lines in mtx files and skips those lines when reading in the data.
Fix a bug in voomLmFit() whereby zeros were sometimes incorrectly identified due to floating point errors.
The “bcv” method of plotMDS.DGEList() is scheduled to be deprecated in a future release of edgeR.
Changes in version 1.10
added functionality to parse expressions in labels via parseLabels (TRUE/FALSE)
over-rides ggrepel’s new default value for max.overlaps via introduction of maxoverlapsConnectors = 15
user can now specify a direction for connectors via directionConnectors
removed labhjust and labvjust
added pCutoffCol (via Andrea Grioni)
Changes in version 1.2.3
NEW FEATURES
Update vignettes.
This is a major release update for EnMCB package.
We add new options for selecting the correlation methods.
We add mboost algorithm in our ensemble prediction model.
SIGNIFICANT USER-VISIBLE CHANGES
Delete unused data.
Correct the parameters’ names.
BUG FIXES
Changes in version 1.21.1
Changes in version 2.22.0
hierarchical
for function getGenesets
) Changes in version 1.11.3
fix bug in gseaplot2(2021-1-28, Thu)
Changes in version 1.11.2
fix showCategory for cnetplot, emapplot, emapplot_cluster when showCategory is a vector of term descriptions
Changes in version 1.11.1
Changes in version 2.15.3
Fix missing declaration of rmarkdown.
Changes in version 2.15.2
Ensure remote gzipped files are handled properly by ensDbFromGtf
.
Changes in version 2.15.1
Add new field canonical_transcript to the gene table reporting the ID of the gene’s canonical transcript.
Changes in version 1.33.0
Changes in version 0.99.0 (2021-04-09)
R>=4.0 for submission
removed unused dependencies
correct work of generateVcfReport (although SNV only)
unmatched reads are at the end of generateBed* output now
compiles and works on Apple Silicon (native ARM64 R)
fully documented methods
fully covered with tests and examples
comprehensive vignettes
Changes in version 0.4.0 (2021-03-08)
going public
CX report now includes only the context present in more than 50% of the reads
generateVcfReport (capable of dealing with SNVs only for now)
added documentation to some of the methods
added several examples
added sample data for amplicon and capture NGS
added some tests based on sample data
README.md
Changes in version 0.3.9 (2021-01-19)
fast C++ CIGAR parser to lay queries in reference space
new method to extract base frequences: generateBaseFreqReport
Changes in version 0.3.7 (2021-01-12)
lots of refactoring again
CX report sub now uses boost::container::flat_map (additional 2x speedup)
removed dplyr as a dependence, whole package uses data.table now
Changes in version 0.3.5 (2021-01-09)
lots of refactoring
new method: preprocessBam() to save time on loading/preprocessing
new C++ sub for CX report with std::map summary (5-10x speedup)
Changes in version 0.3.2 (2021-01-06)
first attempt to stablilize API (generateCytosineReport and generateBedReport)
temporary method for ECDF (generateBedEcdf)
uploaded to GitHub
Changes in version 0.3.1 (2020-01-01)
heavy refactoring, many internal methods added
C++ functions for nearly all bottlenecks (pending fast: cigar, summary, genome loading)
Changes in version 0.2.1 (2020-12-21)
made this second iteration of epialleleR a usable package
Changes in version 0.99.0
First release of epigraHMM.
It is now possible to add normalizing offsets via addOffsets.
epigraHMM now uses hdf5 files to store all intermediate data during computation of the EM algorithm. Intermediate data include window-based HMM and mixture model posterior probabiltiies, and forward-backward probabilities. This change leads to a better memory utilization upon convergece.
Changes in version 1.0.1
Removed ggrepel, rlang, and factoextra dependencies.
Updated Seurat package switch
Switch the way counts are processed by first eliminating rows with 0 expression in the sparse matrix before converting to a full matrix
Changes in version 1.7.5
Minor bug fix for Mac compilation.
Changes in version 1.7.4 (2020-06-01)
Added ‘scale’ parameter to plotMetricsCluster method.
Changes in version 1.7.3 (2020-04-16)
Clusterboot interfaces can be set through ‘cbi’ parameter to quality methods. It takes one the following values: “kmeans”, “clara”, “clara_pam”, “hclust”, “pamk”, “pamk_pam”.
Changes in version 1.7.2 (2020-04-14)
Stability analysis and quality analysis will not stuck in bootstrap.
Changes in version 1.7.1 (2020-04-13)
Clusterboot interfaces can be set through ‘cbi’ parameter to stability methods. It takes one the following values: “kmeans”, “clara”, “clara_pam”, “hclust”, “pamk”, “pamk_pam”.
Changes in version 1.0.0
New Features
Deprecated & Defunct
Changes in version 1.3.7 (2021-03-10)
Parameter parallel
is changed in the exomePeak2() and
exomePeakCalling() functions; the parameter now enables user to
configure specific number of cores used in the parallel computation
(default = 1).
To avoid the potential confusion for the downstream analysis, the
default settings for the parameter log2FC_cutoff
in functions
exomePeak2() and exomePeakCalling() are changed from 1 to 0. The
adjustment should have very little effect on the peak calling result.
The naming of peaks in the output file is now sorted by their genomic order.
A maximum for peak width is added now, which is by default 100*fragment_length. Such a higher bound can significantly improve the results of DRACH motif finding for m6A-Seq.
Changes in version 1.3.5 (2021-02-07)
Improved the grammar and details in the DESCRIPTION file and the vignettes.
When performing the difference analysis using the function exomePeak2(), the sequencing depth of the interactive GLM will be estimated on the background features, which by default are the disjoint regions of the peaks detected on the exons. Tests on real data revealed that the background approach can make the differential methylation directions more in-line with the expectation of the perturbed protein regulator. Previously, the background sequencing depth estimation can only be realized in the multiple-step functions but not in exomePeak2().
Changes in version 1.3.4 (2021-02-05)
The options consistent_peak
, consistent_log2FC_cutoff
,
consistent_fdr_cutoff
, alpha
, and p0
are deprecated from the
functions exomePeak2() and exomePeakCalling(). The consistent_peak
option was implemented to reproduce the consistent peak calling
algorithm in the old package exomePeak, and its performance is
significantly lower than the NB GLM derived methods according to our
recent tests. Hence, the consistency related functionalities are
removed in the later versions of exomePeak2.
Changes in version 1.3.3 (2021-02-03)
Fix the bug of not merging the overlapping exons when the transcript annotation have no overlapping transcripts, this can happen when a very small annotation is provided.
Changes in version 1.99.0
MAJOR UPDATES
(1.99.0) The default caching location has changed. Instead of rappdirs::user_cache_dir using tools::R_user_dir. To avoid conflicting caches, a user will have to manage an old cache location before proceeding. Information for handling an old cache location is provided in the vignette.
(1.99.0) Another major change, a default caching location is automatically created in a non interactive session instead of using a temporary location. In an interactive session, a user is still prompted for permission.
Changes in version 1.17.0
(1.17.1) Removed vignette for creating annotation hub package. Reference and refer to single vignette in AnnotationHub
Changes in version 1.17.0
MODIFICATIONS
1.17.2 Removed vignette for creating annotation hub package. Reference and refer to single vignette in AnnotationHub
1.17.1 Tags for database now combination of biocViews and meta$Tags. Also checks for valid AnnotationHub or AnnotationHubSoftware biocViews.
BUG CORRECTION
Changes in version 1.1.0 (2021-05-09)
Changes in version 1.3.2
Changes in version 1.19.1
Changes in version 1.3.0 (2020-11-27)
Changes in version 1.5.5 (2021-05-12)
Changed fcoex object to store a dgCMatrix instead of a dataframe for expression and discretized expression.
Removed options of 3+ class multiclass discretize (may lead to backwards incompatibility) as they would be incompatible with better memory handling of dgCMatrix system.
Changes in version 0.99.0 (2021-04-01)
Changes in version 0.99.7 (2021-04-24)
added info to explain example data in README and vignettes
made minor changes to exported function example comments
plotFemap + parameterized several hard-coded variables
Changes in version 0.99.6 (2021-02-24)
Updated package data script paths
Changes in version 0.99.5 (2021-02-24)
Trimmed external data size
Changes in version 0.99.4 (2021-03-24)
updated package datasets (geneSingle, geneDouble, geneMulti)
created 3 vignettes to describe package implementation using each dataset
runFedup + runs analysis on an input list rather than single test vector + fold enrichment calculation evaluates 0 for 0/0 instances + enriched pathways defined as fold enrichment ≥ 1 (instead of > 1)
writeFemap + writes EM-formatted tables for list of fedup results
plotFemap + implements tryCatch() to return NULL if Cytoscape is not running locally
prepInput + new function to prepare input gene list
Changes in version 0.99.3 (2021-02-24)
Updating R version dependency to 4.1
Changes in version 0.99.2 (2021-02-24)
Untracking system files
Changes in version 0.99.1 (2021-02-23)
Version bump
Changes in version 0.99.0 (2021-02-17)
Submitted to Bioconductor
Changes in version 3.26
Changes in version 1.1.2 (2020-11-11)
Fix bugs in keeping extra reads when filtering with minMapBase
Allow skipping filtering with minMapBase
Changes in version 1.8.0
Changes in version 1.21.6
Changes in version 1.29.1
Changes in version 0.99.0 (2020-09-30)
Changes in version 2.1.16
PlotManualBars allows input of NewData function
Changes in version 2.1.15
Fixed warnings with ggtexttable in FlowSOMmary
Changes in version 2.1.13
Added RelabelMetaclusters
PlotFileScatters now has a parameter to change the y-axis label to markers and/or channels (yLabel)
Now TRUE/FALSE vector is accepted as input in GetMarkers/GetChannels
Changes in version 2.1.11
Added example to AddAnnotation
Added example to NClusters, NMetaclusters
Changed examples that used fsom to flowSOM.res
Added textColor and textSize to AddLabels and PlotNumbers, PlotLabels
PlotNumbers can plot clusters and metaclusters with parameter “level”
In GetFeatures, the population parameter is changed to level
Added GetCounts and GetPercentages to get counts or percentages respectively per cluster or metacluster
FlowSOMmary doesn’t crash anymore with a column with the same values in heatmap
Included a print function for FlowSOM class
Fixed bug in PlotManualBars
PlotMarker also accept multiple markers now
Changes in version 2.1.8
Solved issue when matrix with no column was given to the SOM function
Changes in version 2.1.5
Scale parameter in FlowSOM function defaults to FALSE.
FlowSOM wrapper function now returns the FlowSOM object instead of a list containing the FlowSOM object and a metaclustering
The metaclustering is now found as an element in the flowSOM object. Also the number of metaclusters and the MFI values are stored and can be accessed by the NMetaclusters() and GetMetaclusterMFIs() functions.
If you want to reuse FlowSOM objects generated by previous versions, you can use the UpdateFlowSOM function.
FlowSOM now uses nClus = 10 as default instead of maxMeta = 10
FlowSOM now makes use of ggplot2 for plotting. PlotFlowSOM provides the main structure, and has parameters to adapt nodeSize, view (grid, MST or some own layout matrix), … PlotStars etc build on this by adding additional layers to the ggplot object. This also allows to easily incorporate multiple plots in all layout-tools such as ggarrange, cowplot, patchwork, …
GetChannels/GetMarkers can now also take a FlowSOM object as input instead of a flowFrame. New functions:
To easily generate a clear summary of the model with multiple plots, you can now use the FlowSOMmary function, which creates a pdf file.
GetFeatures allows to map new files (internally using the NewData function) and can return cluster counts, percentages and MFI values for each individual sample.
PlotFileScatters can be useful to get an overview of potential batch effects before running the FlowSOM algorithm
Changes in version 0.99.56
Improvements to the annotate_foods() function.
Changes in version 0.99.41
Added new function named msea to perform GSEA using FOBI.
Changes in version 0.99.38
Addressing Vince Carey (package reviewer from Bioconductor) comments and suggestions.
Changes in version 0.99.35
Added FOBI table in package data.
Changes in version 0.99.29
Fix Bioconductor Single Package Builder errors and warnings:
Use TRUE/FALSE instead of T/F in ora.R
Changes in version 0.99.24
Submitted to Bioconductor!
Changes in version 0.99.18
Added a function called fobi_graph to generate FOBI graphs.
Changes in version 0.99.12
Changes in version 1.2.1
Add merging of external counts
Add publication
Minor bugfixes
Changes in version 1.28.0
NEW FEATURES
new function exist.gdsn()
new function is.sparse.gdsn()
UTILITIES
LZ4 updated to v1.9.3 from v1.9.2
XZ is updated to v5.2.5 from v5.2.4
apply.gdsn()
: work around with factor variables if less-than-32-bit
integers are stored
a new component ‘is.sparse’ in objdesp.gdsn()
options(gds.verbose=TRUE)
to show additional information
Changes in version 1.26.1
UTILITIES
apply.gdsn()
Changes in version 1.37.0
Changes in version 2.21.5
Added functions to compute variant-specific inflation factors.
Changes in version 2.21.4
Added the option to perform a fast approximation to the score standard error in assocTestSingle. New function nullModelFastScore prepares a null model to be used with this option.
Changes in version 2.21.1
Updated structure of fitNullModel objects. Null model objects
with the previous structure will be automatically updated with
a warning, but you may want to consider rerunning
fitNullModel
if you plan to use an older null model with the
current version.
Changes in version 1.4.0
New features
The main function GeneTonic() gains an extra parameter, gtl - this can be used to provided a named list object where a single parameter is passed (e.g. after loading in a single serialized object), while the functionality stays unaltered. The same gtl parameter is also exposed in other functions of the package - see the vignette for some examples, or check the documentation of each specific function. To create this object in a standardized manner, the function GeneTonic_list() is now available.
A new function to perform fuzzy clustering (following the implementation of DAVID) is added - see gs_fuzzyclustering(). It returns a table with additional information on the cluster of genesets and the status of each set in the group.
The ggs_backbone() function can extract the bipartite graph backbone from the Gene-Geneset graph, this can be further explored below the main element in the Gene-Geneset panel. Once the backbone is created, you are one step away from checking out the genes that act as “hubs” in the Gene-Geneset graph, and possibly identify the nodes playing an essential role based on their connectivity.
A new function, signature_volcano(), adds a signature volcano plot to the Gene-Geneset panel. This plot displays the genes of a chosen geneset in color, while the remaining genes of the data are shown as shaded dots in the background. The color and transparency of the displayed genes can be chosen by the user, as well as the option to display the gene names of all genes in the geneset.
gs_summary_overview() can also generate bar plots instead of the default segment-dot (lollipop) plots.
A new function, summarize_ggs_hubgenes(), builds a DT datatable for the Gene-Geneset panel. This table lists the individual genes of the input data and their respective degree in the Gene-Geneset graph. Furthermore, action buttons linking to the NCBI, GeneCards and GTEx databases are included for each gene.
gene_plot() gains the extra labels_display argument to control whether the labels are at all shown; now the display of the labels is also respecting the jitter of the points
Other notes
Changes in version 1.13.3
Changes in version 1.28.0
NEW FEATURES
SIGNIFICANT USER-VISIBLE CHANGES
DEPRECATED AND DEFUNCT
Changes in version 1.44.0
SIGNIFICANT USER-VISIBLE CHANGES
DEPRECATED AND DEFUNCT
Deprecate disjointExons() in favor of exonicParts().
Remove species() method for TxDb objects (was deprecated in BioC 3.3 and defunct in BioC 3.4).
Changes in version 1.5.1
Changes in version 1.44.0
SIGNIFICANT USER-VISIBLE CHANGES
DEPRECATED AND DEFUNCT
Changes in version 2.4.0
USER VISIBLE CHANGES
The gscores() function now returns the SeqInfo from the input GScores object.
Improvements on the shiny web app.
Changes in version 1.0.0
Changes in version 0.99.0
Submitted to Bioconductor
Changes in version 0.6.5
Changes in version 0.99.0 (2021-02-01)
Changes in version 2.5.3
https://github.com/YuLab-SMU/ggtree/pull/396
Changes in version 2.5.2
https://github.com/YuLab-SMU/ggtree/pull/379
Changes in version 2.5.1
Changes in version 1.1.12
import ggtree to pass BiocCheck. (2021-05-14, Fri)
Changes in version 1.1.11
https://github.com/YuLab-SMU/ggtreeExtra/issues/8
Changes in version 1.1.10
fix a bug for compute_aes ( This is to support mapping aesthetics (x, not y in aes of geom_fruit) to functions of variables). (2021-05-10, Mon)
Changes in version 1.1.9
support mapping aesthetics (x, not y in aes of geom_fruit) to functions of variables. (2021-05-07, Fri)
Changes in version 1.1.8
update vignettes. (2021-04-25, Sun)
Changes in version 1.1.7
update man and vignettes. (2021-04-06, Tue)
Changes in version 1.1.6
remove axis of first geom_tile of vignettes, since the axis of this layer is meaningless. (2021-02-24, wed)
Changes in version 1.1.5
don’t use svg dev. (2021-02-04, Thu)
Changes in version 1.1.4
specific position method for specific geom method automatically. (2021-01-27, Wed)
Changes in version 1.1.3
support multiple density plot from geom of ggridges. (2020-12-31, Thu) geom_density_ridges, geom_density_ridges2, geom_density_ridges_gradient, geom_ridgeline, geom_ridgeline_gradient.
Changes in version 1.1.2
Changes in version 4.9.1
Changes in version 2.17.1
Changes in version 0.99.0 (2021-03-30)
Changes in version 1.37.1 (2020-05-04)
Removed Biocarta and NCI pathways.
Added WikiPathways pathways.
Updated all pathway data.
Changes in version 1.54
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.40
USER VISIBLE CHANGES
The vignette has been rewritten in R Markdown to produce an HTML vignette page and make it shorter and faster to produce.
Development of a shiny app available through the function ‘igsva()’.
BUG FIXES
Replaced fastmatch::fmatch() by IRanges::match,CharacterList-method after disscussion at https://github.com/rcastelo/GSVA/issues/39 to avoid the row names of an input expression matrix being altered by fastmatch::fmatch() adding an attribute.
Fixed wrong call to .mapGeneSetsToFeatures() when gene sets are given in a GeneSetCollection object.
Changes in version 1.20.0
NEW FEATURES
Implement the H5SparseMatrix class and H5SparseMatrix() constructor function. H5SparseMatrix is a DelayedMatrix subclass for representing and operating on an HDF5 sparse matrix stored in CSR/CSC/Yale format.
Implement the H5ADMatrix class and H5ADMatrix() constructor function. H5ADMatrix is a DelayedMatrix subclass for representing and operating on the central matrix of an ‘h5ad’ file, or any matrix in its ‘/layers’ group.
Implement H5File objects. The H5File class provides a formal representation of an HDF5 file (local or remote, including a file stored in an Amazon S3 bucket).
HDF5Array objects now work with files on Amazon S3 (via use of H5File()).
BUG FIXES
Changes in version 2.99.4
Improved label- and axis handling for panels with continuous covariates
Formally deprecated heatmap_2 and heatmap_plus
Refactored to switch to devtools tool chain
Changes in version 1.1.1 (2021-02-19)
Changes in version 0.99.3 (2021-05-14)
Remove build/ folder.
Changes in version 0.99.2 (2021-04-20)
Remove some abundant files.
Changes in version 0.99.1 (2021-04-18)
Remove some abundant files.
Changes in version 0.99.0 (2021-04-10)
The first version 0.99.0 is submitted to Bioconductor
Changes in version 1.28.0
hlaPredict()
returns the dosage of HLA alleles when
type=”response+dosage”, and
hlaPredict()
returns the best guess and dosages by default
a new option “Pos+Allele” in hlaPredict()
, hlaGenoCombine()
,
hlaGenoSwitchStrand()
, hlaSNPID()
and hlaCheckSNPs()
for
matching genotypes
by positions, reference and alternative alleles; it is particularly
useful when
the training and test set are both matched to the same reference
genome,
e.g., 1000 Genomes Project
hlaGDS2Geno()
supports SeqArray GDS files
a new option ‘maf’ in hlaAttrBagging()
and
hlaParallelAttrBagging()
‘pos.start’ and ‘pos.end’ are replaced by ‘pos.mid’ in
hlaFlankingSNP()
and
hlaGenoSubsetFlank()
new function hlaAlleleToVCF()
for converting the imputed HLA
classical alleles
to a VCF file
Changes in version 1.26.1
the hlaAttrBagging object can be removed in garbage collection
without
calling hlaClose()
enable internal GPU API
improved multithreaded performance compared with v1.26.0
Changes in version 0.99.14 (2021-04-13)
Fixed bug that prevents passing negative values to .hic files
Changes in version 0.99.13 (2021-03-23)
Added RE-agnostic features into construct_features
Changes in version 0.99.12 (2021-02-19)
Submitted to Bioconductor
Changes in version 1.21.1
Changes in version 1.9
Changes in version 1.33
Changes in version 1.33.2
Changes in version 1.33.1
Changes in version 1.33.0
Changes in version 1.0.4
The first update in response to F1000 comments
Added dating arguments to HPA_data_downloader
Now when you have save_file set to true, the file will be stamped with the date it was downloaded. This allows for reproducible access tot he file you created.
To deal with dated files we now have the arguments
version_date_normal
and version_date
cancer, which allows users
to
select which downloaded HPA data they want to use based on the date
of
downloading. The default to “last” which will search for the latest
version of the files in the save_location
The last addition is force_download
, an argument that will overide
the function’s default usage of the local files in case you want to
download a more recent version of the HPA data.
Change to HPAStainR and by extension shiny_HPAStainR
Added Fisher’s Exact Test to HPAStainR’s main function
Due to the inconsistency that exists in using simulated p-values in
chisq.test
the default new test if Fisher’s exact and an argument
test_type
has been added so users can pick between the two.
Changed the output of the p-values to numeric instead of a character.
Changes in version 1.0.3 (2021-02-03)
Changed read.table() in HPA_data_downloader.R to data.table’s fread()
Changes in version 1.0.2 (2021-25-20)
Changed section of code crashing due to dplyr update.
Changes in version 1.0.1 (2020-11-20)
testthat() HPA_data_downloader.R failed due to spelling change
HPA changed “unfavourable” to “unfavorable”
The testthat() has been changed to reflect their change
More updates soon after all reviews from F1000 are in
Changes in version 1.31.1
Changes in version 0.99.0 (2021-04-23)
Changes in version 1.16.0
New features
Other notes
Changes in version 1.23.6
code cleaning
Changes in version 1.23.3
fixes a testthat misspelling
Changes in version 1.23.2
tolerance sufficience in cell-subclustering not required for first model refinement test
Changes in version 1.23.1
Changes in version 0.99.7 (2021-05-04)
Increased number of times to try to connect to webresource
Changes in version 0.99.6 (2021-05-04)
Adressed comments from Bioconductor review
included a mro.obo.gz file which can be read without unzipping
Changes in version 0.99.1 (2021-01-01)
Submitted to Bioconductor
Changes in version 1.7.2 (2020-05-05)
New dependencies : RANN, leiden, phyclust
Added new partition method for subclustering that uses the Leiden algorithm based on a K-nn adjacency matrix.
Changed the default subclustering method to leiden which is much faster than the random trees method.
Split Bayesian filtering step in two steps, one that runs the model, and one that applies the filter threshold. This allows updating the threshold without having to rerun the whole model.
Fix what groupings of references the subclustering is done on.
Updated expectations of the internally stored clustering information when plotting references to allow for results obtained with version between ~1.3 and this one to be plotted.
Fix add_to_seurat method to work when no seurat object is provided after the reordering fix.
Fix the random trees subclustering applying a different method of centering to the data between the hclust stored in infercnv_obj@tumors_subclusters$hc and the splits in infercnv_obj@tumors_subclusters$subclusters.
Make denoising step figure only be generated if plot_steps is true. (it is identical to final figure that is plotted based on a different option, making it redundant)
Changes in version 1.1.1 (2020-10-30)
Changes in version 1.99.4
update get_PAscore2.
Changes in version 1.99.3
add rmarkdown as suggests.
Changes in version 1.99.2
fix a bug if utr3 list is empty.
Changes in version 1.99.1
add dontrun for getGCandMappability doc.
Changes in version 1.99.0
merge Haibo’s code.
Changes in version 0.99.10
add response
argument so that the server can only respond to one
event from UI.
Changes in version 0.99.9
output can be floating along with mouse positions.
Changes in version 0.99.8
click and hover won’t conflict with brush.
Changes in version 0.99.7
In the sub-heatmap, it allows to remove rows and columns from the four sides.
Changes in version 0.99.0
Submit to Bioconductor
Changes in version 0.0.0.9000
Changes in version 2.26.0
NEW FEATURES
Add commonColnames() accessor to get or set the character vector of column names present in the individual DataFrames of a SplitDataFrameList object.
Implement unary + and - for AtomicList derivatives.
SIGNIFICANT USER-VISIBLE CHANGES
DEPRECATED AND DEFUNCT
BUG FIXES
Fix unplit() on named List objects.
Fix findOverlapPairs() for missing subject (fixes #35).
quantile() on an AtomicList object always returns a matrix (fixes #33).
Fix which.min()/which.max() for CompressedNumericList objects (fixes #30).
Export startsWith() and endsWith() methods for CharacterList/RleList objects (fixes #26).
Changes in version 0.99.8
Changes in version 1.1.11 (2021-05-11)
NEW FUNCTIONALITY
VISIBLE USER CHANGES
FIXES
Fixed a minor issue in CIS_volcano_plot that caused duplication of some labels if highlighted genes were provided in input
Changes in version 1.1.10 (2021-04-08)
FIXES
IMPROVEMENTS
MINOR
FOR DEVS ONLY
Complete rework of test suite to be compliant to testthat v.3
Changes in version 1.1.9 (2021-02-17)
FIXES
Fixed minor issues in internal functions with absolute file paths & corrected typos
Changes in version 1.1.8 (2020-02-15)
FIXES
Fixed minor issues in internal functions to optimize file system alignment
Changes in version 1.1.7 (2020-02-10)
FIXES
Fixed minor issues in import_association_file when checking parameters
Changes in version 1.1.6 (2020-02-06)
UPGRADES
FIXES
Simplified association file check logic in remove_collisions: now function blocks only if the af doesn’t contain the needed columns
Changes in version 1.1.5 (2020-02-03)
UPGRADES
Updated vignettes and documentation
Changes in version 1.1.4 (2020-11-16)
UPGRADES
General improvements for all widget reports
Changes in version 1.1.3 (2020-11-10)
FIXES
NEW
Added vignette “Using ISAnalytics without RStudio support”
Changes in version 1.1.2 (2020-11-05)
FIXES
Fixed missing restarts for non-blocking widgets
Changes in version 1.1.1 (2020-11-04)
FIXES
IMPORTANT NOTES
Changes in version 2.3.14
Bugfix for assignment of annotation colors in ComplexHeatmapPlot.
Changes in version 2.3.13
Deprecated iSEEOptions in favor of panelDefaults (for construction-time globals) and registerAppOptions (for runtime globals).
Changes in version 2.3.12
Added an .allowableColorByDataChoices generic for downstream panels to control ColorBy*Data choices.
Changes in version 2.3.11
Cleaned up tours for Tables and the ComplexHeatmapPlot.
Changes in version 2.3.10
Bugfix for the RowDotPlot color tour.
Changes in version 2.3.9
Bugfix for ordering of selected columns in ComplexHeatmapPlot.
Changes in version 2.3.8
Use shiny::MockShinySession$new() to simulate Shiny session objects.
Changes in version 2.3.7
Bugfix for missing import of geom_density_2d
Changes in version 2.3.6
Bugfix for graceful deprecation of old parameters in various constructors.
Changes in version 2.3.5
Deprecated ColumnSelectionType and ColumnSelectionSaved (ditto for rows) as all active/saved selections are now transmitted.
Changes in version 2.3.4
Fix wiring of button observer to open vignette.
Changes in version 2.3.3
Edge-case bugfix for correct cleaning of zero-row/column SummarizedExperiments.
Changes in version 2.3.2
Added the cleanDataset() generic to ensure all names in the SummarizedExperiment are present and unique.
Changes in version 2.3.1
Changes in version 1.3.5
Switch to registration for storing DE Panel options, via registerPValuePatterns and related functions.
Changes in version 1.3.4
Support in-memory feature set collections and their statistics via registerFeatureSetCollections.
Changes in version 1.3.3
Redistributed documentation from panel tours to UI-specific tours.
Changes in version 1.3.2
Tour-related patch to fix the builds for the time being.
Changes in version 1.3.1
Changes in version 1.13.07 (2021-05-06)
Update type: minor.
importGTF() and importRdata() was updated to handle the rare cases of mixed stranded and unstranded isoforms (unstanded are now discareded).
addORFfromGTF() was updated to better repport if no or only small number of ORFs were added.
Various maintainance updates.
Changes in version 1.13.06 (2021-04-09)
Update type: minor.
The runtimes repported by isoformSwitchTestDEXSeq() was updated to also consider the number of transcripts analysed.
analyzeORF() was updated to enable analysis with analyzeNovelIsoformORF() when no overlaps were found.
switchPlot was fixed so the alphas argument now work.
Various updates of warning, descriptions and error messages.
extractSequence() was updated to remove the terminal stop codon if it is included in the annoation.
extractSequence() was updated to produce evenly sized files when alsoSplitFastaFile=TRUE.
analysORF no longer allows identification of truncated ORFs.
Changes in version 1.13.05 (2021-01-07)
Update type: Major.
analyseORF was updated with the orfMethod “longest.AnnotatedWhenPossible” a hybrid between “longes” and “longestAnnotated”. See ?analyseORF for details.
importGTF, importRdata and analyseORF was updated to also annoate the source of the ORF annoations. analyzeCPAT and analyzeCPC2 was updated to also changes these if removeNoncodinORFs = TRUE.
To enable better ORF analysis the addORFfromGTF() and analyzeNovelIsoformORF() functions were added to IsoformSwitchAnalyzeR. These should be used instead of analyzeORF(). These function also annotate the source of the ORF annoations. See vignette for description of why these are preferable.
analyseORF() was updated with an additional method for ORF detection: “longest.AnnotatedWhenPossible”.
the getCDS() function and CDSSet class was removed for the user as addORFfromGTF() + analyzeNovelIsoformORF() provides a better way to analyse ORFs.
Downstream functions relying on ORF data now checks that all isoforms have been assessed for ORFs. These are extractSequence(), analyzeSwitchConsequences(), switchPlotTranscript() and switchPlot().
isoformSwitchAnalysisPart1() and isoformSwitchAnalysisPart2() was also updated to support the new ORF annotation scheme.
The usage of isoformSwitchAnalysisPart1() and isoformSwitchAnalysisPart2() was made less complex by removing many arguments passed to sub-functions thereby relying more on default arguments.
importRdata() was updated to import the “refrence gene_ids” instead of StringTie gene_ids (for all annotated genes).
the StringTie annotation rescue in importRdata() was updated to use “refrence gene_ids” instead of “refrence gene_names” thereby fixing problems with closely spaced genes, that have the same gene name, which was merged by StringTie.
importGTF() now also imports ref_gene_id from StringTie gtf to enable the above mentioned updates to importRdata(). If not pressent it will duplicate gene_name instead.
extractGeneExpression() was updated to allow easy output of gene annoation.
isoformToGeneExp() was updated to use rowsum() instead of a tidyverse implementation as it is much faster for large datasets.
The result of importRdata()’s estimateDifferentialGeneRange option now repports the condition names in accordance with the rest of IsoformSwitchAnalyzeR.
Removed mentions of StringTie2 as it has been merged into StringTie.
Documentation and vignette was updated accordingly.
Changes in version 1.13.04 (2020-12-10)
Update type: minor.
Vignette update.
Changes in version 1.13.03 (2020-12-08)
Update type: minor.
Vignette update.
Changes in version 1.13.02 (2020-12-07)
Update type: minor.
Description update.
Update of vignette with regards to running on analysis Gallaxy.
Changes in version 1.13.01 (2020-10-29)
Update type: minor.
Version bump due to Bioconductor release.
Fixed an error in importRdata() that could cause trouble when fixing StringTie annotation. Thanks to @yaccos for identifying the problem.
Fixed an edge-case senario where the estimation of DTU in importRdata() caused an error.
analyseSignalP() was updated to handle cases where no signal peptides were found with a warning instead of an error.
Changes in version 1.19.2
FIX
Add affiliation of GE
Remove funs from dplyr to avoid future errors
Fix column_to_names error when there are rownames
Changes in version 1.26.0
release as part of Bioconductor 3.13
Changes in version 1.25.2
removed ‘register’ from macro in ksort.h in order to avoid warnings on Mac OS
Changes in version 1.25.1
minor fix in MismatchC.cpp
Changes in version 1.25.0
new branch for Bioconductor 3.13 devel
Changes in version 1.4.5 (2021-04-15)
SVA batch effect correction improved
MAD outliers detection fixed
KnowSeq report updated to include the changes of SVA and MAD modifications
Cross-Validation DEGs Extraction implemented Further versions
Incorporation of RUV to batch effect methods
Changes in version 3.48.0
New functionality
Explicitly setting weights=NULL
in a call to lmFit() no
longer over-rides the weights
value found in object
.
Default settings in lmFit() changed from ndups=
and
spacing=1
to ndups=NULL
and spacing=NULL
, although this
doesn’t change function behavior from a user point of view.
A number of improvements to duplicateCorrelation() to make the
results more robust and to make the interface consistent with
lmFit(). duplicateCorrelation() sets weights
same as
lmFit(). Setting weights=NULL
in the function call no longer
overwrites weights found in object
. duplicateCorrelation()
now checks whether the block factor is spanned by the design
matrix. If so, it returns intrablock correlations of zero with
a warning. Previously this usage error was not specifically
trapped and could lead to correlations that were or NA or close
to 1 depending on floating point errors.
duplicateCorrelation() now issues a simplified message when
design is not of full rank and uses message() to do so instead
of cat(). In terms of output, duplicateCorrelation() now
bounds the genewise correlations away from the upper and lower
bounds by 0.01 so that the correlation matrix will always be
positive-definite. There is also a fix to the value returned
by duplicateCorrelation() when no blocks or duplicates are
present.
New argument fc
for treat() so that the fold-change threshold
can optionally be specified on the fold-change scale rather
than as a log2-fold-change.
plotMDS() no longer calls cmdscale() but instead performs the
necessary eigenvector computations directly. Proportion of
variance explained by each dimension is now computed and is
optionally added to the dimension labels. The ndim
argument
is now removed. All eigenvectors are now stored so that
plotMDS.MDS does not need to recompute them when different
dimensions are plotted.
New arguments path
and bgxpath
for read.idat().
read.idat() now checks for gzipped IDAT files and, if detected,
gives an informative error message. read.idat() now checks for
existence of input files before calling illuminaio read
functions.
Other code improvements
Documentation
Additional documentation for the design
argument of lmFit
using the term “samples” instead of “arrays” and mentioning
that the design matrix defaults to object$design
when that
component is not NULL.
duplicateCorrelation() help page revised including new code example.
Help page for voom() now explains that the design matrix will
be set from the group
factor of the DGEList object if
available.
coolmap() help page now clarifies which heatmap.2() arguments are reserved and which can be included in the coolmap call.
Bug fixes
Changes in version 1.10.0
BUG FIXES
Changes in version 0.99.7
Fix typo
Changes in version 0.99.6
Edit Vignette
Changes in version 0.99.5
Add PBMC dataset from ExperimentHub
Changes in version 0.99.4
Edit description
Changes in version 0.99.3
R>=3.6 generates a warning
Changes in version 0.99.2
change LRcell dependency to R>=3.6
Changes in version 0.99.1
change the .gitignore file
Changes in version 0.99.0
Changes in version 1.5.1
Update log from base 10 to base 2.
ZICP is now deprecated (https://cran.r-project.org/web/packages/cplm/NEWS)
SLM is removed in favor of a future R2 functionality for all models
Fitted values are returned along with residuals
Extracted random effects are also returned
Changes in version 2.8.0
NEW FUNCTIONS
ENHANCEMENTS
BUG FIXES
Changes in version 1.1.2
added feature-label output on May 10, 2021
added feature-label output on April 27, 2021
added cutoff value on March 06, 2021
Changes in version 1.1.1
hard-coding of alpha fixed on December 21, 2020.
Changes in version 1.2.1
Changes in version 0.99.12 (2021-05-18)
replace xlsx by openxlsx
Changes in version 0.99.11 (2021-05-10)
rename function normalize to normalizeAssay
rename function transform to transformAssay
rename function batch to batchCorrectionAssay
rename function impute to imputeAssay
Changes in version 0.99.10 (2021-05-06)
bump version to trigger building
Changes in version 0.99.9 (2021-04-29)
add hexbin in Suggests
fix bug in MAplot that plot is displayed properly
Changes in version 0.99.8 (2021-04-28)
set required version for S4Vectors to >= 0.29.15
Changes in version 0.99.7 (2021-04-28)
add version number of dependencies in Description file
Changes in version 0.99.6 (2021-04-27)
add MatrixQCvis to Watched Tags on the Bioconductor support site
Changes in version 0.99.5 (2021-04-27)
reduce file size of vignette by using partial_bundle for driftPlot
Changes in version 0.99.4 (2021-04-26)
add explained variance for PCoA
add se argument in create_boxplot that allows for ordering the samples
use ggplotly for driftPlot
allow flexible addition of samples in MA-plot based on a supplied character vector of sample names
return SummarizedExperiment when exiting the shiny application
add function maxQuant that allows for creation of SummarizedExperiment objects from maxQuant output (.xlsx files)
Changes in version 0.99.3 (2021-03-18)
reduce file size of vignette by using partial_bundle for plotly figures
Changes in version 0.99.2 (2021-03-18)
reduce resolution of images in vignette to reduce file size
Changes in version 0.99.1 (2021-03-17)
reduce file size of vignette
Changes in version 0.99.0 (2021-03-12)
write functions for data manipulation and plots
write tests for these functions
create UI and server modules for shinyQC
write tests for UI and server modules
load different UI elements depending on the type of data (if the data contains missing values or is complete)
Changes in version 1.17.1 (2020-11-27)
BUG FIXES
Changes in version 1.1.5
NEW FEATURES
Changes in version 0.99.11
Add support for STREME with runStreme(). STREME will supercede DREME in a future MEME Suite release.
Changes in version 0.99.10
Fixed a bug where paths weren’t correctly expanded when used as database entry under certain conditions
Changes in version 0.99.8
Removed inline r call in integrative_analysis vignette to fix issue on bioc build machine
Changes in version 0.99.7
Version bump to force pkg rebuild
Changes in version 0.99.6
updated ChIP-seq vignette to demonstrate this
Changes in version 0.99.5
renamed ame_plot_heatmap -> plot_ame_heatmap for consistency
Changes in version 0.1.2
runTomTom() dist default is now ed (changed from pearson).
Changes in version 0.1.0
Changes in version 1.1.3
Package additions
metabCombine(): main package workflow wrapper function
Parameter list functions for loading defaults of main workflow methods
Changes to labelRows
“conflict” argument replaced with “delta”, with default value (0.2)
default value of “minScore” argument increased to 0.5
Changes to calcScores
Changes to fit_loess
Changes to selectAnchors
default for “tolrtq” argument changed from 0.5 to 0.3
Changes in version 1.1.2 (2020-12-28)
Changes to fit_gam()/ fit_loess
new outlier detection method based on boxplot / IQR added
new argument: outlier, which accepts “MAD” or “boxplot” as a value
altered argument names: “frac” to “prop”, “ratio” to “coef”
documentation and minor code changes to main and supporting functions
Changes to plot_fit
new options for showing, hiding, or highlighting (with a legend) outliers
new arguments: outlier, which accepts “show” / “s”, “remove” / “r”, or “highlight” / “h” as arguments ; ocol, outlier point color if outlier argument set to “highlight” / “h”
Other changes
new test case for fit_gam()
Changes in version 1.1.1 (2020-12-02)
Bug Fixes
combinedTable check for missing group values (Issue #7)
calcScores / evaluateParams groups bug (Issue #8)
Warning for column names with bracket characters “{ ( [ ] ) }” (Issue #9)
QCol bug (Issue #10)
New Functionality
Changes in version 0.99
MetaboCoreUtils 0.99.1
MetaboCoreUtils 0.99.0
Changes in version 1.1.1
bug fixes
added some helper functions
Changes in version 1.7.2 (2020-12-17)
Changes in version 1.0.0
Changes in version 1.3.13 (2021-02-20)
NEW FEATURES
BUG FIXES
Changes in version 1.9.1
Changes in version 1.15.1
BUG FIXES AND IMPROVEMENTS
Changes in version 1.17.5
IMPROVEMENTS AND BUG FIXES
fread.gzipped: skip header rows in tabix file to fix https://github.com/al2na/methylKit/issues/226
Changes in version 1.17.4
IMPROVEMENTS AND BUG FIXES
.setMethylDBNames: correct possible methylDBclass from “methylDB” to “methylRawDB”
fread.gzipped: disable skipping of decompression in fread.gzipped
can cause serious issues as investigated in https://groups.google.com/g/methylkit_discussion/c/UruFjvX89B4/m/vV2Qnd8NEAAJ and explained in https://github.com/al2na/methylKit/issues/222
Changes in version 1.17.3
IMPROVEMENTS AND BUG FIXES
export methylRawListDB and methylRawList constructors
Changes in version 1.17.2
IMPROVEMENTS AND BUG FIXES
add faq section about merging methylRaw into methylRawList update vignette
Changes in version 1.17.1
IMPROVEMENTS AND BUG FIXES
readmethylDB: check if file exists before trying to read
loading tabix files:
Changes in version 1.9.5 (2021-05-04)
add functionality to adjust for multiple testing in correlation
return symmetric matrices when returning ppm ranges in structural
Changes in version 1.9.4 (2021-04-30)
add font in mz_vis to mono
split the example on how to use filter in mz_summary from the visualisation
Changes in version 1.9.3 (2021-04-28)
introduce AdjacencyMatrix S4 class, derived from SummarizedExperiment, to store the adjacency matrices. AdjacencyMatrix objects can be of type structural, statistical, and combine
adjust the documentation and tests for AdjacencyMatrix objects
add the functions mz_summary and mz_vis (contribution of Liesa Salzer)
Changes in version 1.9.2 (2021-03-24)
add section on structual matrix generation for directed=FALSE
Changes in version 1.9.1 (2021-02-20)
fix typos in the vignette
Changes in version 0.99.0 (2021-03-19)
Changes in version 0.99.0 (2021-03-19)
Changes in version 2.1.2 (2020-07-01)
Core heatmap labeling improved
aggregate_top_taxa deprecated
bimodality and potential_analysis functions fixed
Changes in version 2.1.1 (2020-04-06)
Added overlap function
Changes in version 1.0.3
added option to split a taxonomy column (as obtained via qiime) within the application
Changes in version 1.0.2
fixed bug in correlation analysis on phenotypes with NaN values
prevented application crash on loading ill-formatted feature data
Changes in version 1.0.1
adjusted to be compatible with shinyjs 2.0.0
Changes in version 1.3.11
fill ggclust bug to map color and shape. (2021-05-12, Wed)
Changes in version 1.3.9
more layouts for ggdiffclade. (2021-04-16, Fri)
https://github.com/YuLab-SMU/MicrobiotaProcess/issues/23
Changes in version 1.3.8
add aliases for ggbartaxa and ggdiffbartaxa. (2021-03-23, Tue)
Changes in version 1.3.7
update import_qiime2 to avoid error when only feature table is provided. (2021-02-26, Fri)
Changes in version 1.3.6
convert svg dev to pdf dev. (2021-02-04, Thu)
Changes in version 1.3.5
fix an error for example of ggrarecurve. (2021-01-07, Thu) factorNames=”Group” to factorNames=”group”
Changes in version 1.3.4
geom_tiplab also support circular layout, so remove geom_tiplab2. (2020-11-26, Thu)
Changes in version 1.3.3
add as.treedata for taxonomyTable class. (2020-11-23, Mon)
Changes in version 1.3.2
rareres <- get_rarecurve(obj, chunks=400) p <- ggrarecurve(rareres)
Changes in version 1.3.1
Changes in version 0.99.15
fixes bug causing LRT to report wrong number of tested residues.
Changes in version 0.99.13
Changes in version 0.99.1 (2021-03-13)
Fix model normalisation bug - now using TMM normalisation by default. Log(M_s) offset can be used by passing norm.method=”logMS” to testNhoods.
Changes in version 0.99.0 (2021-03-04)
Submitted to Bioconductor
Changes in version 0.99.9 (2021-03-24)
Added new function, plotMetrics
mixtureModel now throws a warning if flexmix has not identified two distributions
Vignette includes instructions for assessing whether to use miQC on a dataset
Changes in version 0.1.0 (2021-02-10)
Submitted to Bioconductor
Changes in version 1.9.4-1.9.5
Add citation information <2021-04-15, Thus>
Changes in version 1.9.3
Add sponge module (SM) method <2021-02-02, Thue>
Changes in version 1.9.1-1.9.2
Update miRSM.R <2020-11-26, Thus>
Changes in version 1.17.1-1.17.2
Changes in version 0.99.11 (2021-05-14)
Changed News file
Changes in version 0.99.10
shrunk repo
Changes in version 0.99.9
updated with suggested notes
Changes in version 0.99.8
fixed check error
Changes in version 0.99.7
dealt with download issue
Changes in version 0.99.6
dealt with vignette issue
Changes in version 0.99.5
fixed documentation warnings for datasets and warnings for R CMD file size
Changes in version 0.99.4
fixed documentation warnings for datasets
Changes in version 0.99.3
fixed documentation warnings for code
Changes in version 0.99.2
initiated
Changes in version 0.99.11
Cleaner cache control.
Changes in version 0.99.9
Changes in the mistyR vignette to reflect changes to insilico evaluation from the paper.
Changes in version 0.99.0
Version with vignettes ready to submit to Bioconductor.
Changes in version 0.1.0
Changes in version 1.71.1
rda is defunct and all references are removed
bug in hclustWidget forbade use of more features than samples – this is fixed
Changes in version 1.39.1
use Imports rather than Depends
use Authors@R
KEGG.db (not available from BioC >= 3.13) -> KEGGREST
getGeneSets: only return descriptions for selected pathways
update example gene set
Changes in version 0.99.0 (2018-05-15)
Changes in version 1.1.9
integrated MEFISTO into MOFA2
Improve interoperability with Seurat and SingleCellExperiment
Improve memory usage and training time by optionally replacing float64 arrays with float32 arrays
mofapy2 has been updated to version 0.6.0 and now it has its own repository
Changes in version 1.1.4
integrated MEFISTO into MOFA2
Improving Python interface with basilisk
Sample metadata can be incorporated to the MOFA object before and after training
Changes in version 1.35.2
Changes in version 1.12.1
Fix argument checking in MPRASet construction to allow users to not have to specify barcode or eseq.
Fix bug with ordering of eids in log ratio object with aggregate=”none” option
Changes in version 0.99.4
Added the function PlotProteinPeptideRatio() to visualize a comparison between the proteins identified and the ratio Peptide/Proteins among Experiments.
Changes in version 0.99.3
Removed LazyData: TRUE.
Changes in version 0.99.2
Changes in version 1.24.0
release as part of Bioconductor 3.13
Changes in version 1.23.1
updated texshade.sty to newest version
Changes in version 1.23.0
new branch for Bioconductor 3.13 devel
Changes in version 0.99
Changes in 0.99.4
Changes in 0.99.3
Changes in 0.99.2
Changes in 0.99.1
Changes in 0.99.0
Changes in version 0.99
Changes in 0.99.3
Changes in 0.99.2
Changes in 0.99.1
Changes in version 1.3
Changes in 1.3.3
Changes in 1.3.2
Changes in 1.3.1
Changes in 1.3.0
Changes in version 2.17
Changes in 2.17.7
Changes in 2.17.6
Changes in 2.17.5
Changes in 2.17.4
Changes in 2.17.3
Changes in 2.17.2
Changes in 2.17.1
Changes in 2.17.0
Changes in version 1.1.1 (2020-03-27)
Changes in version 1.17.2
Update dev to match bug fixes in master
Changes in version 1.17.1
Update dev to match bug fixes in master
Changes in version 1.16.2
Author list updated
Changes in version 1.16.1
Update of createDatabase to record all intra average spectra in database
Add license and copyright info to code
Add github workflow CI (and subsequent formatting updates to pass tests)
Changes in version 0.99.6
Update authors in Description file
Changes in version 0.99.5
Fix standard errors on the model parameter estimates by msqrobLmer when using doQR = TRUE
Changes in version 0.99.4
Minor update vignette. Replace eval=FALSE in one R chunk so that the code is evaluated.
Changes in version 0.99.3
Avoiding sapply and 1:…
Changes in version 0.99.2
Changes in version 0.99.1
Changes in version 1.1.2
Updated Spectronaut converter to allow annotation in input file.
Changes in version 1.1.1
Changes in version 2.0.0 (2021-05-14)
Refactor the pacakge to make it modulized
Changes in version 1.8.2 (2020-12-17)
Update progress bar
Update groupComparisonTMT to avoid reusing the local function copied from lmer pacakge
Changes in version 1.8.1 (2020-12-10)
Add citation of the MSstatsTMT paper
Fix the bug in groupComparisonTMT() due to the update of dependent pacakge
Fix the bug in MedianPolish summarization
proteinSummarization(): replace the zero values with NA before and after peptide normalization
Changes in version 1.18.0
New features
Bug fixes and minor improvements
Changes in version 1.1.7 (2021-05-10)
Remove Biocarta database from vignette - it’s no longer supported by graphite R package.
List all available databases in error message.
Changes in version 1.1.5 (2021-04-19)
Typos fixed.
Add information and reference about metrics to calculate the feature ranks.
Changes in version 1.1.4 (2020-12-09)
Add the correct citation of the BMC Bioinformatics article.
Changes in version 1.1.3 (2020-11-19)
Prevent warning due to import of two different select() functions.
Changes in version 1.1.2 (2020-11-07)
Speed-up the metabolite ID mapping between different ID formats
Changes in version 1.1.1 (2020-11-05)
Bug fix. Prevent duplicated pathway title to cause an error.
Include new function to enumerate duplicted pathway titles with trailing numbers.
Changes in version 2.0.0 (2020-11-23)
Update selection of significant results in the ‘topDirs’ function. Major change, results will be more strict compared to pre-2.0.0 version
Removed ‘BLMA’ and ‘metap’ dependency, added ‘aggregation’ dependency
P-values are aggregated using ‘max’ by default. I.e., for a region differentially interacting with multiple regions, a maximum p-value will be selected. Fisher, Lancaster, and Sidak methods are also available
Harmonize counting of significant regions using ‘p.adj_cutoff’ only (‘alpha’ cutoff removed)
The ‘manhattan’ function is harmonized with ‘topDirs’
The ‘perm_test’ function is harmonized with ‘topDirs’
Update vignettes to match functions
Changes in version 0.99.0
Changes in version 1.0.0
New Features
Changes in version 1.5.2
added edgeR::calcNormFactors() step in prepSim()
added argument ‘dd’ to simData() specifying whether or not to simulate 2 groups
prepSim() and simData() now support simulation of “singular” design (no samples, no clusters), as well as only samples/clusters
simData() defaults to simulating as many samples as available in order to avoid re-use (duplication) of reference samples
Changes in version 1.5.1
significant speed-up of aggregateData() by replacing usage of rowX() over a list with scuttle::summarizeAssayByGroup()
added options use “prop.detected” and “num.detected” as summary statistic (argument ‘fun’) in aggregateData()
added parallelization support in aggregateData() and pbDS() through argument BBPARAM (passed to scater::sumCountsAcrossCells() and BiocParallel::bplapply, respectively)
aggregateData() now stores the number of cells that went into aggregation under int_colData(.)$n_cells (vs. metadata(.)$n_cells) to support automated subsetting
replaced argument n_threads with BPPARAM throughout all parallelizable functions (aggregateData(), pbDS(), mmDS())
bug fix in prepSim(): the function previously failed when cluster/sample/group_id cell metadata columns were non-factors
bug fix in resDS(): cpm = TRUE previously didn’t handle missing cluster-sample combinations correctly
Changes in version 1.1.2 (2021-02-03)
RELEASE
Changes in version 3.1.4
Fixed spelling mistakes.
Changes in version 3.1.2
Plot_lesion_segregation has been improved. It can now plot multiple samples at the same time. Users can also specify which chromosomes they want to plot. The plot now also contains colour and the ratio of the mutations on the chromosomal strands is visualised by a horizontal line per chromosome.
Changes in version 3.1.2
Changes in version 2.25.5
Fix compile error on clang-11 reported (and fixed!) by Kurt Hornik, closes #244
Changes in version 2.25.4
Add dependency “rmarkdown” to “suggests:”
Changes in version 2.25.3
Ensure header
for CDF returns columns with correct data type.
Changes in version 2.25.2
Fix issue #238: ensure header
call returns the same columns for all
backends.
Changes in version 2.25.1
Bump version to trigger new build using latest Rcpp
Changes in version 2.25.0
New Bioc devel version
Changes in version 1.15.2
add rmarkdown as suggest package.
Changes in version 1.15.1
fix the missing link for rtracklayer::export.
Changes in version 1.1.4
Added plot_granges_heatmap() function to use GRanges for plotting heatmaps
Changes in version 1.1.3
Fixed error when reads overlap in name and position for internal function StatLM()
Changes in version 1.1.2
Added unit tests.
Changes in version 1.1.1
Changes in version 0.99.5 (2020-12-21)
Fix the issues from the build report
Changes in version 0.99.4 (2020-12-18)
Fix the issues from the build report
Changes in version 0.99.3 (2020-12-18)
Fix the issues from the build report
Changes in version 0.99.2 (2020-12-18)
Fix the issues from the build report
Changes in version 0.99.1 (2020-12-17)
Fix the issues from the build report
Changes in version 0.99.0 (2020-12-15)
Submitted to Bioconductor
Changes in version 1.1.1 (2021-05-04)
Changes in version 1.1.4
Changes
Changes in version 1.99.4 (2021-04-15)
Removal of MCUPGMA-dependencies for smaller networks.
Changes in version 1.99.0 (2021-03-08)
Fully rank based extension (netboost(…,robust_PCs=TRUE,filter_method=”spearman”,method=”spearman”)).
Full support of the non-parametric version.
Changes in version 1.7.3
Simplifed version of plotFastqcPCA. Now groups are an optional factor. No clustering is performed
Changes in version 1.7.2
Deprecated runFastQC
Changes in version 1.7.1
Added macs2 callpeak logs to importNgsLogs
Changes in version 1.6.1
Added asPercent to plotAlignmentSummary
Added the ability to assign new values via fqName<-
Changes in version 1.9.1
Changes in version 1.99.0
Included predNuPoP_chem function which predicts the nucleosome positioning based on profiles trained based on chemical maps
Vignette file has been created with R markdown
Added NEWS file to document version bump
Changes in version 2.7.1 (2020-11-11)
Changes in version 2.99.19
Fixed an error which resulted value 1 in the n_references columns even for records without references
Changes in version 2.99.17
Improved quality filtering of intercell networks
Changes in version 2.99.16
Quality filtering of intercell networks
Changes in version 2.99.13
More robust access to UniProt (in case of network failures)
Changes in version 2.99.11
Improved downloader backends
Changes in version 2.99.8
Database manager
Changes in version 2.99.7
Fixed many caching bugs
Changes in version 2.99.6
New resources: Human Phenotype Ontology and Gene Ontology annotations
Changes in version 2.99.0 (2021-03-08)
Changes in version 3.0
MAJOR change: frequency dependent fitness available.
Removed v.1 functionality.
Multiple initMutants.
Added MAGELLAN’s sources and functionality from MAGELLAN.
Changes in version 2.99.93 (2021-04-30)
Fixed bugs and improved testing of rfitness with three-element scale vector.
Changes in version 2.99.92 (2021-04-27)
rfitness: scale can take a three-element vector.
Vignette: examples (not run) for deviations from SSWM.
Changes in version 2.99.9 (2021-04-22)
Fixed date typo in one citation.
We were inconsistent, allowing some examples of one gene.
Readme: nem.
Changes in version 2.99.8 (2021-01-01)
Removing unused code.
Long tests: no longer using v.1.
Changes in version 2.99.7 (2020-12-30)
Vignette: fixed two missing refs and add seed in two examples.
Changes in version 2.99.6 (2020-12-30)
No longer v.1 functionality.
Slightly faster vignette.
Changes in version 2.99.5 (2020-12-18)
Random timeouts when building in tokay2 (Windows, BioC); fixing seed in vignette.
Changes in version 2.99.4 (2020-12-17)
Clean up of C++ code.
Changes in version 2.99.3 (2020-12-13)
Remove unnecessary (and cluttering) output and irrelevant warnings when running tests.
Decrease execution time of longer running examples in man (Rd) files.
Decrease time of vignette.
Changes in version 2.99.2 (2020-12-11)
Failed on test on Mac.
Changes in version 2.99.1 (2020-12-10)
Bump version number for BioC, so it will become version 3.0.0 in next release.
Latest version of exprtk.
Changes in version 2.21.995 (2020-12-09)
Vignette: rewrote most FDF examples using names (not numbers) for fitness specification.
Changes in version 2.21.994 (2020-12-08)
Can start simulation from arbitrary configuration: multiple init mutants (and multispecies functionality).
Freq-dep-fitness does not need to have a WT in fitness tables.
Bumped version (to 2.21.xyz) for new BioC devel.
Changes in version 2.0.0
MODIFICATIONS
added Rcpp code for online testing algorithms
added online batch algorithms of Zrnic et al. [2020]
added Storey-BH algorithm
added setBound function
updated vignette and pkgdown site
updated unit tests
updated references
Changes in version 3.11
Enhancements
Bug Fixes
Added a fix to the density estimate used by gate_tautstring
Changes in version 3.10
API Changes
Simple renaming
Classes and methods no longer exported
Bug Fixes
Changes in version 2.9.1
Changes in version 1.31.3
fix bug in mol.sum with single gene data reported by easygsea. Similarly add drop=F to a few other lines in mol.sum and node.map.
Changes in version 1.31.2
solve the check error due to the change related to KEGGEdgeSubtype in KEGGgraph package (version 1.51.1).
Changes in version 1.31.1
korg now include 6833 KEGG species or 1588 new species beyond 2017.
Changes in version 2.4.0
added DESeq2 section to vignette intro
permit that users can now specifiy just a single PC for plotloadings()
improved ellipse functionality with addition of parameters ellipseType, ellipseLevel, and ellipseSegments
over-rides ggrepel’s new default value for max.overlaps via introduction of maxoverlapsConnectors = 15
user can now specify a direction for connectors via directionConnectors
removed labhjust and labvjust
Changes in version 1.5.4 (2021-04-15)
mzML parameter parsing optimized
Changes in version 1.5.3 (2021-03-30)
Additional calculation of raw peak area without smoothing (peakArea Raw)
Alignment of peak integration algo across functions, integrateFIR() is now resilient to missing scans
Changes in version 1.5.2 (2021-01-31)
Change package alias due to overwritten .rd file warning on Windows (non case-sensitive name and path)
Move to Github Actions continuous integration
Changes in version 1.5.1 (2021-01-19)
Unittests updated to comply with new r-devel all.equal() environment checks behaviour
Changes in version 1.9.1 (2021-01-14)
CI TOOLS
BUG CORRECTION
Changes in version 0.99.12
Remove bugs in *.R, date: 2020.12.19.
Changes in version 0.99.11
We update the package after the first review.
Changes in version 0.99.7
Add pathway_info_hsa.rdata to data.
Changes in version 0.99.6
We update the package after the first review.
Changes in version 0.99.5
We update R version dependency from 4.0.0 to 4.1.
Changes in version 0.99.4
We subscribe to the bioc-devel mailing list
Changes in version 0.99.3
Remove bugs in *.R, date: 2020.11.19.
Changes in version 0.99.2
Remove bugs in *.R, date: 2020.11.8.
Changes in version 0.99.1
Remove bugs in *.R
Changes in version 0.99.0
Changes in version 0.99.0 (2021-02-17)
Changes in version 1.1.9
Fixes in documentation, examples and styles to meet BioC requirements
Changes in version 1.1.8
Fixes in documentation and examples to meet BioC requirements
Changes in version 1.1.7
Fixed getSPS to expect residue information.
Changes in version 1.1.6
Included warning signs to creating PhosphoExperiment object without attributes.
Changes in version 1.1.5
Fixed the warning message generated in plotQC for dendrograms. (Warning message: Vectorized input to element_text() is not officially supported.).
Changes in version 1.1.4
Fixed the parameter of plotQC from cols to grps
Changes in version 1.1.3
Changes in version 1.4.12
Changes in version 1.17.10 (2021-02-02)
Bug Fixes
Changes in version 0.99.4
Updated references
Changes in version 0.99.3
Accepted into bioconductor, will be released in next cycle
Changes in version 0.99.2
Lazydata set to false
Changes in version 0.99.0
Rewrote many data man pages
Changes in version 0.3.0
Changes in version 1.3.4
Added option to constrain spar values for QCRSC.
Changes in version 1.3.2
Added inputs/outputs for pqn.
Fixed glog plot bug.
Changes in version 1.2.1
Account for missing injections in QCRSC.
Changes in version 0.99.6 (2021-04-06)
Updated NEWS.Rd)
Changes in version 0.99.5 (2020-12-09)
Corrected name of an author)
Changes in version 0.99.4 (2020-12-09)
Changed R version dependency to >= 4.1 (R-devel)
Changes in version 0.99.3
Minor code changes as suggested by Bioconductor reviewer
Changes in version 0.99.2 (2020-11-11)
Changed arguments in examples to run faster
Changes in version 0.99.1 (2020-11-11)
Removed .Rproj file from repository
Changes in version 0.99.0 (2020-11-10)
Submitted to Bioconductor
Changes in version 1.24.0
release as part of Bioconductor 3.13
Changes in version 1.23.2
updates and documentation to vignette in order to adapt to newer version of Illumina’s TruSeq DNA Exome library prep kit
changed default col.names in readRegionsFromBedFile(); corresponding update of help page
Changes in version 1.23.1
re-created genome data objects (old data objects had become incompatible with newer ‘BSgenome’ version)
Changes in version 1.23.0
new branch for Bioconductor 3.13 devel
Changes in version 1.99.3
NB function now exported
note that version 1.99.3 on GitHub was version 1.1.0 on Bioconductor.
Changes in version 1.99.2
bug fix in fragment generation (last 2 bases of transcript were never sequenced)
Changes in version 1.1.15
Bug fixed in PomaMultivariate
Changes in version 1.1.8
POMA 1.0.0
Changes in version 0.99
NEW FEATURES
Initial review.
Changes in version 0.99.0
Revise required files and format the code style.
Changes in version 1.1.22
Update package-down site
Update NEWS for Bioconductor 3.13
Changes in version 1.1.18
Gene expression data is now stored in the assays of the summarizedExperiment object returned by proActiv to facilitate easier filtering of the summarizedExperiment object. The metadata slot is now empty.
Plotting promoter activity: Implementation of boxplotPromoters function to plot boxplots of absolute promoter activity, relative promoter activity, and gene expression.
Identification of alternative promoters: Implementation of getAlternativePromoters, used to identify promoters that may exhibit alternative usage.
Changes in version 1.1.15
Implement getAlternativePromoters for identifying alternative promoters
Implement boxplotPromoters for visualizing promoter usage
Changes in version 1.1.6
Enforce condition vector to following naming conventions
Changes in version 1.31
Changes in version 1.31.3
Changes in version 1.31.2
Changes in version 1.31.1
Changes in version 1.31.0
Changes in version 1.23.9
Added new uniqueMsLevel generic <2021-04-08 Thu>
Changes in version 1.23.8
Added new filterPrecursorCharge generic <2021-04-06 Tue>
Changes in version 1.23.7
Add ProcessingStep object and related methods (moved from Spectra)
Changes in version 1.23.6
Added quantify generics (moved from MSnbase) <2021-01-02 Sat>
Changes in version 1.23.5
new alignRt generic.
Changes in version 1.23.4
new virtual Param class.
Changes in version 1.23.3
new filterIntensity generic.
Changes in version 1.23.2
new compounds generic.
Changes in version 1.23.1
new calculateFragments generic (moved from MSnbase)
Changes in version 1.18.0
Bug fixes and minor changes
Changes in version 0.99.5 (2021-04-08)
folder src-x64 and src-i386 in gitignore
Changes in version 0.99.4 (2021-04-08)
vignette: eval=FALSE in installation chuck
Changes in version 0.99.3 (2021-04-01)
added BugReports to description files
changed in vigette: remove echo = FASLSE and added installation section
remove dontrun from examples
avoided slot() and created generic method for slot access and setters
modyfied the two datSet documentaion
added documentation in inst/script
Changes in version 0.99.0 (2021-03-12)
Submitted to Bioconductor
Changes in version 1.22.0
NEW FEATURES
calculateNormalDatabase now suggests an off-target interval width that minimizes noise while keeping the resolution as high as possible
Added support for GATK4 CollectAllelicCounts output as alternative to Mutect
Added segmentationGATK4 to use GATK4’s segmentation function ModelSegments
SIGNIFICANT USER-VISIBLE CHANGES
Added min.total.counts filter to filterIntervals to remove intervals with low number of read counts in combined tumor and normal. Useful especially for off-target filtering in highly efficient assays where standard filters keep too many high variance regions.
Changed default of min.mappability in preprocessIntervals for on-target intervals to 0.6 (from 0.5)
Added min.mappability also to filterIntervals so that more conservative cutoffs can be tested after normalDB generation
PSCBS: 1.20.0 two-step segmentation slightly tweaked in that only high quality on-target intervals (high mappability and low PoN noise) are used in the first segmentation
Added –skipgcnorm flag to Coverage.R to skip GC-normalization
Added AF.info.field option to calculateMappingBiasGatk4 for non-standard GenomicsDB imports
If segmentation functions add breakpoints within baits, these breakpoints are now moved to the beginning or end of that bait to avoid that a single bait is assigned to two segments
Dx.R now always generates a _signatures.csv file with –signatures, even if insufficient number of mutations
Removed defunct calculateIntervalWeights function
BUGFIXES
Fix for nonsensical error message when VCF does not contain germline variants (#166).
Fix for various issues related to the seqlevelsStyle function (e.g. #171)
Fix for crash in calculateMappingBiasGatk4 when not all samples had a single variant call on a particular chromosome (chrY)
Fix related to annotating mapping bias with triallelic sites and GenomicsDB
Fixed an issue in Mutect 1.1.7 data in which good SNPs were ignored (#174)
Changes in version 1.29
qcmetrics 1.29.1
qcmetrics 1.29.0
Changes in version 1.1.0
QFeatures 1.1.4
QFeatures 1.1.3
QFeatures 1.1.2
Manually install preprocessCore (see https://github.com/Bioconductor/bioconductor_docker/issues/22 for details) to use quantile normalisation in vignette and tests.
Update vignette to show normalize() and logTransform() directly on a QFeatures object and reference the QFeaturesWorkshop2020 workshop and WSBIM2122 chap 8.
QFeatures 1.1.1
QFeatures 1.1.0
Changes in version 1.9.4
Added documentation for new data set.
Changes in version 1.9.3
New Feature: Added the function IRSnorm, which performs normalisation of intensities across multiple TMT runs using a common reference sample (Internal Reference Scale).
Changes in version 1.9.2
Bug Fix: Update getContrastResults test reference
Fix bug in is_validScalingFunction - switch from are_equal
to
identical
Changes in version 1.9.1
Fix bug in getContrastRestults where file was written to .txt
, file
name
should now be correctly formed from the contrast name
Changes in version 0.99.0
All set for Bioconductor submission!
Changes in version 0.0.1
Added a NEWS.md file to track changes to the package.
Changes in version 1.0.1
Changes in version 1.16.0
New features
Changes in version 0.99.2 (2020-12-21)
code cleanup for bioconductor
initial submission to bioconductor
Changes in version 1.3.3 (2020-11-18)
Defunct function df_estimate()
Changes in version 1.2.1 (2020-11-17)
pFdr adapted according to (Phipson and Smyth 2010)
Changes in version 1.31.1
NEW FEATURES
Changes in version 1.11
Changes in version 1.7.12 (2021-02-24)
Use Basilisk to manage python dependencies (cwltool).
Rename of ‘cwlParam’ into ‘cwlProcess’, ‘cwlStepParam’ into ‘cwlWorkflow’.
Add the support of wrapping R functions into Rcwl tools.
Changes in version 1.7.7 (2021-02-24)
Moved all recipes to https://github.com/rworkflow/RcwlRecipes.
Added new core functions: cwlUpdate, cwlSearch and cwlLoad.
Core functions return a ‘cwlHub’ object.
Changes in version 2.12.0
Consistent interchangeable handling of node|edge|network names and SUIDs
createNetworkFromDataframes plays nice with tibbles
Changes in version 1.0
Changes in version 0.0.0.9000
Changes in version 1.5.2 (2021-04-16)
Added check to ensure that clustering was performed in Seurat objects prior to the pathway analysis.
Changes in version 1.5.1 (2021-04-01)
Fixed bug in perform_reactome_analysis: Error messages were not displayed correctly.
Changes in version 1.2.0
Added the rmd2id() function to easily determine the ID for each chapter.
Link to the originating chapter for the set-up code in extractCached().
Expose collapseStart() and collapseEnd() for manual creation of collapsible chunks.
Added scrapeReferences() to scrape a bookdown book for references for external use.
Added configureBook() to configure a Bioconductor package as a book deployment.
Added link() to rapidly link to references in a configured Bioconductor book package.
Added extractFromPackage() to extract objects from Rmarkdown files in installed packages.
Added createRedirects() to redirect from old, deprecated pages to their new locations.
Changes in version 1.1.7
BUG FIXES
read_counts() now reads the gene/exon counts for every sample as numeric instead of integer in order to support count values that exceed the 32bit integer threshold (such as 2447935369). Previously, read_counts() would report tiny fractions for such large numbers. This bug was reported by Christopher Wilks.
Changes in version 1.1.6
BUG FIXES
Now project_homes() reads in a text file from recount3_url/organism/homes_index which enables support for custom URLs such as http://snaptron.cs.jhu.edu/data/temp/recount3test.
Changes in version 1.1.4
BUG FIXES
Fixed project_homes(), available_projects() and available_samples() to support using non-standard recount3_urls where the user knows that are the project_homes() for their organism of choice. This fix enables users to create their own custom recount3-like webservers and access their data using the functions in this package. This fix introduces the argument available_homes to both available_projects() and available_samples(). This bug was reported by Christopher Wilks.
Changes in version 1.1.3
NEW FEATURES
Added expand_sra_attributes() that was contributed by Andrew E Jaffe. This function expands the SRA attributes stored in a given SRA study, which makes it easier to use that data. However, it makes it harder to merge studies and thus should be used with caution.
Changes in version 1.1.1
BUG FIXES
Changes in version 1.1.4
Improves User’s Guide, fixes typos, new citation, adds disclaimer text, updates servermatrix() chunk.
Updates examples to further limit downloads. Function get_rmdl() uses download = FALSE,
Updates Data Analyses vignette. Uses reduced dpi for images in Data Analyses vignette to limit package size. Uses updated metadata file name.
Compresses new metadata v.0.0.2 files to limit package size.
Uses uniform metadata file label for v.0.0.1 file.
Changes in version 1.1.3
Added v.0.0.2 database compilation files to server (recount.bio/data) and revised recountmethylation functions for cross-platform support. The new files reflect IDAT downloads completed in Nov 2020 from GEO/GDS, including the first compilations of EPIC/HM850K arrays.
Added platform
argument in relevant getdb
functions.
Added which.platform
argument to get_rmdl
Added new function smfilt
to filter server data table for
newest compilation files, accounting for platform in the
filename.
Cleaned up and shoretened the servermatrix
function. This now
handles RCurl call for “dn” (originally from get_rmdl
) when
handling condition dn = NULL
.
Updated the User’s Guide to fix typos, reflect v.0.0.2 samples, and add a download troubleshoot section. Added numeric citations format, removed evaluation of validation section due to possible package build failure from bad internet connection.
Updated ExperimentHub file metadata script and table to add new v.0.0.2 compilation files.
Renamed sample metadata directory to “gsm_metadata” to avoid confusion with “metadata.csv” file table for hubs.
Changes in version 1.3.2
SIGNIFICANT USER-VISIBLE CHANGES
The link provided inside connect_database() has been updated in order to connect with the latest version of regulonDB v10.8.
Changes in version 1.3.1
BUG FIXES
Changes in version 1.11.1
NEW FEATURES
SIGNIFICANT USER-VISIBLE CHANGES
removed is_GMQL from read_gmql function The entire dataset must have the right folder structure in order to works correctly <dataset_name> —> <files>
Swap order of arguments ‘dir_out’ and ‘name’ of the collect() function so now the latter comes before the former.
DEPRECATED AND DEFUNCT
BUG FIXES
Changes in version 2.36.0
NEW FEATURES
Added additional hyberslab selection functions introduced in HDF5 1.10.7 (H5Scombine_hyperslab, H5Scombine_select, H5Sget_select_npoints).
Support for read access to files in S3 buckets now includes Windows.
Added function h5deleteAttribute().
BUG FIXES
Changes in version 1.4.0
USER VISIBLE CHANGES
Compression libraries updated:
zstd: 1.3.8 🠪 1.4.5
BUG FIXES
Changes in version 1.14
New features
Bug fixes
CPPFLAGS used to build R are now used during HDF compilation.
Package configure script will now stop if it encounters errors when compiling HDF5. This should make diagnosing issues easier.
Changes in version 0.99.3
Renamed R functions by following bioconductor convention
Changes in version 0.99.2
Initial version for review
Changes in version 1.3.4
add rmarkdown as suggest package.
Changes in version 1.3.3
add github action.
Changes in version 1.3.2
fix the issue if there is softclip in the mapping reads and the reads length is smaller than shfit range.
Changes in version 1.3.1
keep the raw counts for countReads.
Changes in version 0.99.0 (2020-10-21)
Changes in version 1.7.1
Changes in version 1.5.4 (2021-01-23)
add plot type “points” to plotCompare functions
Changes in version 1.5.3 (2021-01-12)
bugfix for zero-length annotation
bugfix for settings function
bugfix for plotting functions
Changes in version 1.5.2 (2020-12-12)
bugfix for names plotted by plotCompareByCoord
Changes in version 1.5.1 (2021-01-12)
Changes in version 1.5.1 (2021-01-12)
Changes in version 2.9.3
Changed some of the default options values
Some bugfixes
Changes in version 2.9.2
Implemented function rnb.execute.pOOBAH. Thanks to Nathan Steenbuck.
Update of contact information in DESCRIPTION
Changes in version 2.9.1
Improved function intensities.by.color. Thanks to Nathan Steenbuck.
Changes in version 2.19
CHANGES IN VERSION 2.19.4
CHANGES IN VERSION 2.19.3
CHANGES IN VERSION 2.19.2
CHANGES IN VERSION 2.19.1
Changes in version 1.23.12
MINOR MODIFICATION
minor documentation update
Changes in version 1.23.8
MINOR MODIFICATION
vignette: minor update
Changes in version 1.23.6
MINOR MODIFICATION
‘view’ method: minor update of documentation
Changes in version 1.23.4
BUG FIXED
‘view’ method: display of row names
Changes in version 1.23.2
BUG FIXED
Changes in version 1.99.01
Changes in version 1.99.0
rpx 1.99.8
rpx 1.99.7
rpx 1.99.6
rpx 1.99.5
rpx 1.99.4
rpx 1.99.3
rpx 1.99.2
rpx 1.99.1
Changes in version 1.27.0
Changes in version 1.3.1
Changes in version 2.8
NEW FEATURES
Changes in version 2.6.0
Improved the speed of cellCounts and also reduced its memory use.
Added a parameter ‘umi.cutoff’ to cellCounts to call all the cells that had a total UMI count greater than the specified threshold.
Added support for FASTQ-format read input in CellCounts.
Changes in version 1.3.2 (2020-11-24)
Changes in version 2.22.0
New features
Bug fixes and minor improvements
Changes in version 2.16.0
Changes in version 1.16.0
Changes in version 1.16.0
Changes in version 1.1.2 (2020-11-27)
Changes in version 1.12.0
Removed getColoredPathway
New function: writeGMT
Updated URLs to BridgeDb datasources.tsv (again)
Bug fix: downloadPathwaysArchive works with redirected urls
Changes in version 1.11.4
Updated URLs to BridgeDb datasources.tsv
Changes in version 1.11.3
New features
Changes in version 0.30.0
NEW FEATURES
Add combineRows(), combineCols(), and combineUniqueCols() for DataFrame objects. These are more flexible versions of rbind() and cbind() that don’t require the objects to combine to have the same columns or rows.
Add unname() generic and a method for Vector objects.
DEPRECATED AND DEFUNCT
BUG FIXES
Fix long-standing bug in rbind() method for DataFrame objects. The bug was causing rbind() to return an incorrect result when the columns of the DataFrame objects to combine were a mix of ordinary lists and other list-like objects like IntegerList objects (defined in the IRanges package).
Fix issues in DataFrame printing (commits 735c6b7f and 89b045e7).
Fix bug in expand() when the DataFrame object to expand has one or none unselected columns (commit a8f839bb).
Changes in version 1.6.0
seqFitNullGLMM_SPA()
can use imputed dosages directly without
converting the dosages to the best-guess genotypes
new function glmmHeritability()
for approximate heritability
estimates
Changes in version 1.16.0
BUG FIXES
Changes in version 0.99.0 (2021-01-06)
Changes in version 0.99.0
NEW FEATURES
SIGNIFICANT USER-VISIBLE CHANGES
BUG FIXES
Changes in version 1.5.1
Changes in version 1.5.60 (2021-05-18)
Changes in version 1.0.0
Changes in version 1.20.0
runMDS can use user-supplied function for calculating the distance matrix. runMDS can optionally store the distance matrix computed. runMDS also stores the eig and GOF fields of the object returned.
Made the handling of center, scale, color and limits similar in plotDots, plotHeatmap, and plotGroupedHeatmap
Add use_fitsne argument to runTSNE allowing the use of fast interpolated t-SNE in place of Barnes-Hut t-SNE.
Changes in version 0.99.5 (2020-12-30)
Changes in version 1.5.11 (2021-01-19)
scDblFinder now provides doublet enrichment tests
doublet generation and default parameters have been further optimized
Changes in version 1.15.1 (2021-05-06)
Add PsiNorm normalization method and its wrapper.
Add vignette that describes how to use PsiNorm.
Changes in version 1.1
scp 1.1.6
scp 1.1.5
scp 1.1.4
scp 1.1.3
scp 1.1.2
scp 1.1.1
scp 1.1.0
Changes in version 1.5.3 (2021-03-14)
Moving ScaledMatrix to “imports” section of DESCRIPTION.
Changes in version 1.5.2 (2020-12-21)
Adding LTLA/ScaledMatrix to “Remotes” section of DESCRIPTION.
Changes in version 1.5.1 (2020-12-17)
Changes in version 1.20.0
All deprecated functions from the previous release are now defunct.
Added a simplify= option to quickSubCluster() to get the cluster assignments directly.
Deprecated combinePValues() as this is replaced by metapod::combineParallelPValues().
getClusteredPCs() now uses bluster::clusterRows() by default.
decideTestsPerLabel() now automatically detects pval.field= if not supplied.
Added the clusterCells() wrapper around bluster functionality.
Removed the option to pass a matrix in design= from pseudoBulkDGE().
Migrated all normalization-related functions (computeSumFactors(), calculateSumFactors(), cleanSizeFactors() and computeSpikeFactors()) to a better home in scuttle. Soft-deprecated existing functions.
Modified getTopHVGs() to accept a SingleCellExperiment and compute the DataFrame with modelGeneVar().
Added fixedPCA() to compute a PCA with a fixed number of components, a la scater::runPCA() (but without requiring scater).
Modified denoisePCA() so that it now complains if subset.row= is not provided.
Modified all pairwise* functions so that the p-value from direction=”any” is derived from the two p-values from the one-sided tests. This is necessary for correctness with all choices of lfc= and block=, at the cost of conservativeness when block=NULL and lfc is large.
Changes in version 1.2.3
Changed the access of the sample data to github.io repo: readRDS(url(“https://ncborcherding.github.io/vignettes/scRepertoire_example.rds”))
Changes in version 1.2.2
Removed Startrac-based functions in order to pass build on Bioconductor. DEPRECATED AND DEFUNCT
Deprecate StartracDiversity()
Changes in version 1.2.0
SUBMITTED
SIGNIFICANT USER-VISIBLE CHANGES
DEPRECATED AND DEFUNCT
Deprecate combineSeurat in favor or combineExpression().
Deprecate seurat2List in favor of expression2List().
Changes in version 1.1.2
Clonal Overlap Coefficient issue fixed, was comparing unique barcodes and not clonotypes
Added function checkBlanks to remove list elements without clonotypes, this prevents errors for visualizations
Re-added Startrac metrics by stripping down the package and adding it piecemeal
Heavily modified dependencies to reduce total number
Changes in version 1.2.0
Migrated whichNonZero() to beachmat.
Bugfixes for factor-based colData aggregation in aggregateAcrossCells(). Added proper support for Vectors.
Bugfix for correct response to use.altexps= in perCellQCMetrics(), perFeatureQCMetrics().
Added a normalize.all= option to normalizeCounts(). Removed unnecessary warning when down.target= is not specified. Exposed the default size.factors= in the SingleCellExperiment method.
Modified the SingleCellExperiment method of logNormCounts() so that manually specified size factors do not apply to alternative Experiments. Only relevant if size.factors= and use.altexps= are specified.
Deprecated use.altexps= in favor of applySCE() in logNormCounts() and aggregateAcrossCells().
Renamed addPerCellQC() and addPerFeatureQC() to addPerCellQCMetrics() and addPerCellFeatureMetrics(), for consistency. Soft-deprecated the old functions.
Moved most of quickPerCellQC() functionality into the new perCellQCFilters() function. Repurposed the former to directly return a filtered SummarizedExperiment object.
Migrated scran’s normalization-related functions into this package. Added pooledSizeFactors(), computePooledFactors(), cleanSizeFactors() and computeSpikeFactors().
Added transform=”asinh” to normalizeCounts() and logNormCounts() for inverse hyperbolic transformations of CITE-seq data.
Modified isOutlier() to now return outlier.filter objects. These are simply logical vectors that preseve the “thresholds” attribute upon subsetting.
Migrated correctGroupSummary() from scater, to compute corrected versions of group-level summary statistics.
Changes in version 1.11.5
return beta and gamma as matrix
Changes in version 1.11.4
update vignette with SC RNA sequencing data
Changes in version 1.11.2
re-upload files under Version 1.11.1
Changes in version 1.11.1
update reference manual with single-cell RNA sequencing data
Changes in version 1.0.0 (2021-04-19)
sechm moved from the SEtools package
generalized annotations
Changes in version 1.23.1
Upgraded datasets to hg38 and mm10
Upgraded python script from python2 to python3
Changes in version 1.32.0
NEW FEATURES
new option ‘ret.idx’ in seqSetFilter()
for unsorted sample and
variant
indices
new option ‘ret.idx’ in seqSetFilterAnnotID()
for unsorted variant
index
rewrite the function seqSetFilterPos()
: new options ‘ref’ and
‘alt’,
‘multi.pos=TRUE’ by default
new option ‘packed.idx’ in seqAddValue()
for packing an indexing
variable
new option ‘warn’ in seqSetFilter()
to enable or disable the
warning
new functions seqNewVarData()
and seqListVarData()
for
variable-length data
UTILITIES
allow no variant in seqApply()
and seqBlockApply()
the list object returned from seqGetData()
always have names if
there
are more than one input variable names
BUG FIXES
seqGDS2VCF()
should output “.” instead of NA in the FILTER column
seqGetData()
should support factor when ‘.padNA=TRUE’ or
‘.tolist=TRUE’
fix seqGDS2VCF()
with factor variables
seqSummary(gds, "$filter")
should return a data frame with zero row
if
‘annotation/filter’ is not a factor
Changes in version 1.1.1 (2021-01-22)
Changes in version 1.31.1 (2020-11-30)
Changes in version 1.5.3 (2021-04-19)
Changes in version 1.5.0
shareAttributes
and minLength
in package optionsdepth
in function is.shared Changes in version 0.99.9 (2021-01-07)
Bug fixed: DMR plot group colors
Changes in version 0.99.0 (2020-11-13)
Submitted to Bioconductor
Changes in version 1.50
NEW FEATURES
(v 1.49.1) as(., "QualityScaledDNAStringSet")
propagates
names. See https://github.com/Bioconductor/ShortRead/issues/3.
(v 1.49.1) implementas(., "DNAStringSet")
. See
https://github.com/Bioconductor/ShortRead/issues/3.
BUG FIXES
Changes in version 1.5.3 (2021-04-12)
Supported setReadable function and readable argument in TSEA functions to convert Entrez id to gene Symbols in the itemID column in the enrichment result table.
Supported dtnetplot on Reactome pathway
Changes in version 1.5.2 (2021-02-22)
Changes in version 1.3.1 (2020-11-11)
Speed up
Modified more defaults based on publicaly available FFPE samples
Added new parameter sameChrProp for a better simulation
Modified the model of SRCR distance for adjcent ss-DNA combination: from normal distribution to log-normal distribution
Renaming of parameters
Fixed the bug of producing chimeric reads without SRCR
Fixed the bug of producing read duplicates at low coverage
Changes in version 1.1.4
add export_to_shiny_app()
add simplifyGOFromMultipleLists()
Changes in version 1.1.2
add anno_word_cloud()
function
Changes in version 1.14.0
Added the unsplitAltExps() function to reverse the effect of splitting alternative experiments.
Added a mainExpName() getter and setter to remember the name of the main experiment.
splitAltExps() will now store the chosen ref as the mainExpName.
swapAltExp() now discards the promoted experiment from the list of alternative experiments in the output. It will also exchange the colData between the swapped experiments when withColData=TRUE. These changes assist in achieving reversibility of the output.
Added applySCE() to conveniently apply a function to the main and alternative Experiments.
Added withDimnames= to reducedDim<-() and reducedDims<-(). If TRUE, these methods now emit warnings on observing incompatible row names in value.
Respect any metadata passed in with value in reducedDims<-() and altExps<-().
Added the reduced.dim.matrix class to preserve attributes inside the reducedDims during subsetting/combining.
Setting withColData=TRUE in altExp() and altExps() will now prepend colData(x) to the output colData.
Added withDimnames= to altExp<-() and altExps<-(). If TRUE, these methods now emit warnings on observing incompatible column names in value. Also added withColData=, which will now reverse the prepending in the getter if the left-most columns are the same as colData(x). (If not the same, a warning is emitted.)
Changes in version 2.1.2 (2021-05-13)
Added diffAbundanceFET and plotClusterAbundance function
Linked Shiny UI help buttons to new online help pages
Expanded convertSCEtoSeurat() function to copy additional data
Updated and merged pkgdown documentation
Added HTML reports for Seurat curated workflow and marker finding
Refactor of Normalization UI
Added tagging system for matrix type
Several bug fixes
Added generic wrapper functions for normalization, dimensionality reduction and feature selection
Changes in version 2.0.1 (2021-01-07)
Added cell type labeling functional, wrapping SingleR method
Added cell type labeling UI under differential expression tab
Added marker identification in Seurat workflow
Changes in version 1.6.0
Relaxed the requirements for consistent row names in combineRecomputedResults().
Support sparse DelayedArray inputs in classifySingleR().
Parallelize over labels instead of rows in aggregateReference(), with minor changes in the setting of the seed. Restrict the PCA to the top 1000 most highly variable genes, for speed.
Changes in version 0.0.1 (2021-02-07)
NEW FEATURES
Changes in version 1.7.8
Change default ‘minSNP’ value for ‘parallelSites’ function.
Changes in version 1.7.7
Fix: Add ‘rmarkdown’ in ‘Suggests’.
Only plot paths with duplication in number for ‘sneakPeek’ function.
Changes in version 1.7.6
Bug fix: empty groups produced by ‘groupTips’ function.
Create ‘paraFixSites’ and ‘fixationIndel’ functions.
Changes in version 1.7.5
Treat ‘phyMSAmatched’ object as ‘lineagePath’ class for simplicity.
Improved multiprocessing.
Fix: repeated cluster name by ‘.assignClusterNames’ internal function.
Rename ‘allSitesPos’ to ‘allSitesName’.
Add ‘plotMutSites’ support for ‘lineagePath’ and ‘fixationSites’ objects.
Changes in version 1.7.4
‘cl.cores’ option for turning multiprocessing on and off.
Changes in version 1.7.3
Fix: inability to get position of all the sites.
Changes in version 1.7.2
Fix: missing export for ‘plot.phyMSAmatched’ function.
Fix: ‘addMSA’ function unable to handle ‘treedata’ object.
Multiprocess for ‘addMSA’ and ‘sitesMinEntropy’ function.
Changes in version 1.7.1
Guess sequence type based on ATCG proportion for ‘addMSA’.
Changes in version 2.0.0
Added a NEWS.md file to track changes to the package.
Changed default output of most functions from SlingshotDataSet to PseudotimeOrdering and added conversion functions between them and SingleCellExperiment.
getLineages now relies on createClusterMST
Removed plotGenePseudotime
added as.df option to slingCurves and slingMST, which provide relevant information for plotting as data.frame objects. This should help those plotting Slingshot results with ggplot, especially for our traffic package.
updated all documentation.
Changes in version 1.5.0
New Features
Bug Fixes
Changes in version 1.1.700 (2021-05-05)
updating roles in DESCRIPTION file
adding GeneExpression biocVie
switching LazyData to false
fixing return value in show method documentation
Changes in version 1.1.434 (2021-26-02)
fixing subset method according to SummarizedExperiment generic definition
Changes in version 1.1.432 (2021-15-02)
fixing documentation on latex errors
Changes in version 1.1.430-1.1.431 (2021-12-02)
adding BumpyMatrix as suggests
update documentation
Changes in version 1.1.429 (2021-12-02)
adding cd_keep = TRUE binds all the colData to the spatialData
fixing bug for cd_keep with multiple elements
Changes in version 1.1.428 (2021-12-02)
adding itemize to assays vignette item
correcting typo cd_keep->cd_bind in spatialData documentation
Changes in version 1.1.427 (2021-09-02)
fixing tenx vignette
Changes in version 1.1.426 (2021-09-02)
fixing read10xVisium example with data parameter
Changes in version 1.1.425 (2021-08-02)
restoring data parameter in read10xVisium
missing itemize in combine documentation
Changes in version 1.1.424 (2021-07-02)
cleaning documentation
removing spatialImage-methods.R file
Changes in version 1.1.423 (2021-02-02)
fixing documentation issues on imgData
Changes in version 1.1.422 (2021-29-01)
removing ve data because of local image problem (using example(read10xVisium) instead)
Changes in version 1.1.421 (2021-28-01)
fixing ve data local image problem
Changes in version 1.1.42 (2021-21-01)
implementing new SpatialExperiment class
spatialData slot
imgData and image handling methods (HLC)
Changes in version 1.1.6 (2021-02-04)
removed additional slots in the SPE class definition (i.e. spatialData and spatialCoordsNames)
spatialCoords stored in int_colData() = numeric matrix
spatialDataNames stored in int_metadata() = character vector specifying a subset of colData()
the SPE show method does not include spatialData in colData; instead, spatialData/CoordNames are printed separately
the SPE constructor now allows specification of spatialData/Coords/-Names where -Names can be a subset of the supplied colData(); spatialData/Coords are thus optional
cbind() now allows duplicated sample_ids, which are made unique with a message
consistent usage of “spe” for SpatialExperiment objects across all examples (previously, both ve and se were used as well)
fixed cache/path-related error on windows in SpatialImage unit tests
added unit-tests of SpatialExperiment class validity
imgData field in int_metadata is now required to exist (but can be an empty DFrame)
colData<- protects sample_id & spatialDataNames fields; spatialData<- protects colData
Changes in version 1.1.5 (2021-31-03)
version bump to x.y.z format with .z increment
general code-style revision to keep to Bioc guidelines including, e.g.
usage of accessors (and not @)
keeping to a 80-character limit
spaces around logical operators (but not function arguments)
in-line { for function definitions, if-else statements etc.
re-ordering of roxygen2 documentation to be consistent across scripts
Changes in version 1.1.6 (2021-05-17)
The spatial heatmaps are able to maitain outline (stroke) widths defined in aSVG. The stroke widths can be updated with “update_feature”.
Text in aSVGs can be independent from features, i.e. have separate colors, stroke widths, etc.
Only “g”, “path”, “rect”, “ellipse”, “use”, and “title” elements are allowed in aSVG files, and other elements will raise errors or warnings. The “use” element should not in “g”, and “g” elements should not have the “transform” attribute with a “matrix” value.
Extraction of shape coordinates includes two alternative methods. If one fails, the other will be used by default. So in most cases though some coordinates are missing due to irregular shapes, spatial heatmaps can still be created.
Added spatiotemporal example of rice coleoptile to Shiny app and vignette.
Downloaded gene expression data are cached.
In “spatial_hm”, the “tis.trans” argument was replaced by “ft.trans”; width and height arguments were removed, since the spatial heatmap aspect ratio is set the same with original aSVG.
Shiny app: the function “shiny_all” was renamed to “shiny_shm”; the app is able to take HDF5 database backend, which contains data and aSVG files, and the HDF5 database can also be uploaded on the user interface; re-organized user interface: Landing Page (includes links to different app instances), Spatial Heatmap (includes sub-tabs of Image, Interactive, Video, Matrix Heatmap, Interactive Network), Spatial Enrichment (identifies spatial feature-specific genes), About; new functionality introduced: auto-completion search box, URLs of specific app states can be bookmarked, full screen, scrolling height, tooltip, one-to-multiple re-matching of spatial features, fixed aspect ratio (SHMs are not squeezed in the case of multiple aSVGs), metadata column and link column in the data matrix; code was organized in modules, etc.
Changes in version 1.1
Changes in 1.1.20
Changes in 1.1.19
Changes in 1.1.18
Changes in 1.1.17
Changes in 1.1.16
Changes in 1.1.15
Changes in 1.1.14
Changes in 1.1.13
Changes in 1.1.12
Changes in 1.1.11
Changes in 1.1.10
Changes in 1.1.9
Changes in 1.1.8
Changes in 1.1.7
Changes in 1.1.6
Changes in 1.1.5
Changes in 1.1.4
Changes in 1.1.3
Changes in 1.1.2
Changes in 1.1.1
Changes in 1.1.0
Changes in version 1.7.1 (2020-11-22)
Changes in version 1.16.0 (2020-05-20)
• Added ability to simulate data with complex multiplexed sequencing designs
• Added simulation of “conditional” effects, where a subset of DE and eQTL effects are applied to only a subset of individuals (e.g. disease vs. healthy samples)
• Added the ability to simulate different numbers of cells for each sample, sampled from a gamma distribution.
• Updates to the splatPop vignette describing these changes
Logical matrices should now be handled correctly when minimising output SingleCellExperiment objects
Other minor fixes
Changes in version 0.99.18
R version update
Changes in version 0.99.17
Minor corrections
Changes in version 0.99.16
Minor corrections
Changes in version 0.99.15
Adjust sample dataset creating script to the use of SummarizedExperiment
Changes in version 0.99.14
Documentation updates
Changes in version 0.99.13
Documentation and example updates
Changes in version 0.99.12
Documentation and example updates
Changes in version 0.99.11
Vignette updates
Changes in version 0.99.10
Example dataset updates
Changes in version 0.99.9
Documentation and code formatting updates.
Changes in version 0.99.8
Examples for diversity calculation functions.
Changes in version 0.99.7
Formatting corrections.
Changes in version 0.99.5
Added verbose argument for functions.
Changes in version 0.99.4
Updates to example dataset.
Changes in version 0.99.3
SummarizedExperiment input type updated in calculate_diversity, new argument: SE_assay.
Documentation, vignette updated.
Changes in version 0.99.2
SummarizedExperiment input type instead of ExpressionSet.
Changes in version 0.99.1
Correction: unnecessary file removed.
Changes in version 0.99.0
Changes in version 1.21.1
Changes in version 1.4.0
added VIP summary chart
add ellipse plotting options to pca_scores_plot
mv_sample_filter can be used in train/predict mode
Documentation updates
Minor issue fixes
Add median method for fold change
Update PQN with new inputs/outputs due to changes in pmp package
Update SBC with new inputs/outputs due to changes in pmp package
Update MTBLS79 due to changes in SBC
Changes in version 1.22.0
NEW FEATURES
Changes in version 0.99.9 (2021-02-19)
Fixed NEWS text formatting
Changes in version 0.99.8 (2021-02-11)
Fixed multiple issues in review for development
No longer using for loops in function code
Changes in version 0.99.0
Changes in version 1.21.2
BUG FIXES
add rmarkdown to DESCRIPTION
Changes in version 1.21.1
UPDATE
update axis.text and theme_bw
Changes in version 1.21.0
NEW FEATURES
Changes in version 1.3.15
Changes to concensus score in PairSummaries.
Changes in version 1.3.14
Major changes to the PairSummaries function and minor changes to NucleotideOverlaps, ExtractBy, and FindSets. Adjustments to the model that PairSummaries calls on to predict PIDs.
Changes in version 1.3.13
DisjointSet function added to extract single linkage clusters from a PairSummaries object.
Changes in version 1.3.12
PairSummaries now computes 4-mer distance between predicted pairs.
Changes in version 1.3.11
The function FindSets has been added and performs single linkage clustering on a pairs list as represented by vectors of integers using the Union-Find algorithm. Long term this function will have a larger wrapper function for user ease of access but will remain exposed.
Changes in version 1.3.10
NucleotideOverlap now passes it’s GeneCalls object forward, allowing PairSummaries to forego inclusion of that object as an argument.
Changes in version 1.3.9
Minor vignette and suggested package changes.
Changes in version 1.3.8
PairSummaries now allows users to fill in specific matching gaps in blocks of predicted pairs with the arguments AllowGaps and OffSetsAllowed.
Changes in version 1.3.7
PID prediction models in PairSummaries adjusted.
Changes in version 1.3.6
Contig name matching has been implemented. Scheme expects users to follow NCBI contig naming and gff formats, accepting contig names from gffs directly, and removing the first whitespace and everything thereafter from FASTA headers. Contig name matching can be disabled if users wish, using the argument AcceptContigNames, but ensuring that the correct contigs in GeneCalls objects are matched to the appropriate contigs in Synteny objects are then the user’s responsibility.
Changes in version 1.3.5
gffToDataFrame now parses out the transl_table attribute
Changes in version 1.3.2
Changes in version 1.1.40
Major change
Add is_demo option: only affect workflow module right now. Lock users inside a temp folder when using the WF module and give users a new temp folder every time they refresh. This will prevent directory exist problem if many users are using a same deploy instance.
Add welcome_guide option: whether to enable the welcome guide which highlights the guide dropdown menu.
Rewrite welcome tab with a gallery to show all SPS features.
loadDF, dynamicFile and dynamicFileServer added back to this mainframe work package instead of spsComps, because these dependencies have already been using in SPS. Leave these functions in spsComps will introduce extra dependencies, and these functions are not too frequently used outside the framework.
Minor change
Option warning_toast now also checks if you are on “local” mode.
Deleted some unwanted entries in reference generating yaml file.
Fix some typos.
More informative error message when the config file cannot be found for spsOptions
Add some .onLoad methods so users can use the spsOption to get default values on package load.
Updated essquise functions
Add more guides.
Removed the scroll to top button by shinyDashboardPlus, we have our own “go top” button.
Add assertions to spsInit.
Add some screenshots to readme.
Bug fix
Fix a warning in vroom due to the column type problem
Changes in version 1.1.35
Major change
Login feature added:
Users can choose whether to enable the login or not in global.RSPS options.
There are also the login loading screen feature which can be turned on and off.
There are 3 different login loading screens right now and users can interact with them.
Website updated. https://systempipe.org/sps
Updates on the admin panel:
App information: added live charts for CPU, temperature, and RAM
User control: admins now can add/delete/change users directly from this tab, instead of only from command line.
Minor change
Bug fix
FIx bugs due to login page caused server not loading
Add 1s delay in javascript after login to resize the page so the dashboard can be displayed normal.
Fix a table rendering bug in workflow cwl step markdown text.
Changes in version 1.1.30
Major change
new spsAccount class. This class is associated with login management , which allows users to create/delete user/admin accounts, change password, change roles.
Deprecated the spsPlotContainer class since we rewrite the Canvas feature and move to a separate package {drawer}.
New spsCoreTabReplace, which allows users to overwrite the default core tabs.
A lot more SPS options.
Users can now choose whether to load or not load certain tabs on start, even for default core tabs. Combining the spsCoreTabReplace function, now users can customize everything of the original app.
Users can change the app title, and logo image.
Admin panel added to app. Users now can visit the admin panel by adding “?user_definded_string” to the end of the url. Default is “admin”. Login with an admin account is required. Users can use the spsAccount class to add/change an admin account before starting the app.
App information: a tab displays current SPS app server information, like CPU, RAM, size, etc.
User control: a tab to see account information of current SPS app.
Changed the way to install modules. Default modules, workflow, RNAseq and quick ggplot dependency packages are not installed by default, unless you use dependency = TRUE in installation command. It means all these dependencies are moved from Imports to the Suggests class. This helps to save quite some time on SPS package installation. Users install these packages based on their needs. When users loads these modules but depend packages are not fully installed, app will not crash, instead, a warning message with install instructions will be displayed on both console and app UI.
Based on the module installation change, module loading methods are also changed. Module server functions are only called if users set the option to load them. In previous versions, the server functions are still loaded, just hide the unloaded module UI. This saves a lot of time on app starting time, roughly from > 10s to < 3s if none of the default modules are loaded.
Bug fix
update all links to our new website: https://systempipe.org/sps
Fix some bugs in the guide system
Changes in version 1.1.20
Major change
https://systempipe.org/sps{.uri}.
Separation of SPS smaller functions into 3 different packages. We hope these packages can help people in their own Shiny app, or other R projects.
{spsComps}: SPS components, all new Shiny custom components and utility functions that are used in Shiny server side.
{drawer}: the redesign of Canvas, purely front-end image editing tool.
{spsUtil}: SPS utilities, general useful utility functions that can be used with/without Shiny.
Redesigned the new tab feature. Now users use spsNewTab function to create their new custom visualization tab. The old newSpsTab function is deprecated. Easier syntax and templates are used. By default it will use the “simple” template which wraps 90% of the shiny code from users so they can focus on the plotting code. There is also the “full” template which expose all the Shiny code to users.
New spsEzUI and spsEzServer functions are used in the “simple” template to wrap complex Shiny code.
New spsOptDefaults, which prints out all the default SPS options and current values of these options on console.
New notification system. Developers can write some notifications which stores in a remote location and when app starts, it will try to download and parse this file to notifications messages to broadcast to users. This way, developers can send messages to users often without re-deploy the app. The notification will appear on the top right corner.
The interactive guide is back. After a few versions of tests, we added the guide system back. This time, developers can customize their own guides. A guide_content.R file is created when a SPS project initialize. It is stored in R of folder relate to the project root. The guide will also be displayed on the app top right corner.
Minor change
Bug fix
fix bugs due to shiny updates to 1.6.0
Fix all bugs caused by {shinydashboardPlus} v2.0 updates.
Changes in version 1.1.10
Changes made from 1.1.0 to 1.1.05
Workflow module R session
RNAseq module
General UI
Workflow module CWL tab
Workflow module fully functioning
Now you can run a full example workflow in SPS by choosing the “Example” option on workflow setup step.
Other systemPipeR preconfiged workflows will cause problems because formatting issues that will cause errors in systemPipeR::runWF function, not introduced by SPS. Please wait the updates on systemPipeR to fix this. You can still use SPS to prepare files for all workflows. That means, step 1-4 will work, step 5 will give you errors if you choose a workflow which is not “Example”.
Rework on the workflow part
All 3 tabs merged into the main tab
changed config tab to CWL tab
added support for the running wf as a sub tab
Now the main tab has 5 subtabs, they are all connected.
Better guidelines for users, step-like usage, can’t reach other steps if a previous step is not completed.
Original snapshot management drop down page changed to running workflow session. This session will lock users to a unique page, they can’t interactive other app parts on the page(working directory changed), to prevent app crash due to wd change.
Other changes
A new UI component spsTimeline : horizontal timeline UI unit, has status, can be updated on server by updateSpsTimeline.
A new UI bsHoverPopover: enhanced high level function of bsPlus::HoverPopover, additional JS used to make the popover work on buttons as well.
Fixed some link problems in renderDesc. Better links in renderDesc, enlarged and spacing animation for links.
Change on about page
The news is now rendered on about tab in the app
reduced developer content on about page.
changed developer emails to github links.
Change on visualization
RNAseq part is now only in one tab as big module: users upload the targets file and a raw count table, and make different plots in subtabs.
This introduced a lot of dependencies, will decide later if we keep as it is or separate it to spsBio.
Changes in version 1.48.0
NEW FEATURES
Function ri_data_extract
allows for a time range for each searched
m/z,
instead of a single range for all masses.
Man-pages typos and clarifications. No more user-significant changes.
BUG FIXES
Add extra assertions on ncdf4_convert
.
The dependency package ncdf4
should be on Imports
rather than
Depends
on the DESCRIPTION file.
Changes in version 1.31.1
changed default DE estimation method from exactTest to GLM-based test (glmQLFit, glmQLFTest) when using edgeR.
removed DE estimation method for no-replicates dataset.
Changes in version 1.12.0
New features
Minor changes and bug fixes
Changes in version 1.35.1
Changes in version 1.11.1
Changes in version 1.5.1
Changes in version 1.13
Changes in version 1.13.1
Changes in version 1.1.0
Changes in version 1.27.15
Update documentation of geneModelFromTxdb
Changes in version 1.27.14
Add rmarkdown into Suggests
update importScSeqScore.
Changes in version 1.27.13
Fix the size by number when read from file.
Changes in version 1.27.12
add decontructor to hic.cpp.
Changes in version 1.27.10
split the vignette to multiple files.
Changes in version 1.27.9
plot back to back Interaction Data Track
Changes in version 1.27.8
fix the typo in hic.cpp
Changes in version 1.27.7
figure out the error in dyn.load trackViewer.so
Changes in version 1.27.6
add support for .hic and .cool for importGInteraction
Changes in version 1.27.5
add importGInteraction
Changes in version 1.27.4
change the re-sample method for viewTracks
Changes in version 1.27.3
Update documentation.
add label_on_feature for lolliplot
Changes in version 1.27.2
Fix the bug for pie plot of dandelion.plot when introduce label.parameters.
Changes in version 1.27.1
Fix the bug that if all scores are greater than 10 and all scores are integer.
Changes in version 1.5.02 (2021-01-21)
Changes in version 0.99.12
Major changes
Removed top level doc/ folder
Changes in version 0.99.11
Major changes
depends R changed to >= 4.1
Changes in version 0.99.10
Major changes
Changes in version 1.1.2
SIGNIFICANT USER-VISIBLE CHANGES
Separation of only one function into two
Introducing new input data formats to the above functions
Changes in version 0.99.13
No changes
Changes in version 0.99.12
minor bugs fixed
Changes in version 0.99.11
T/F changed by TRUE/FALSE
Changes in version 0.99.10
More evaluated chunks in the vignettes
Changes in version 0.99.9
xlsx files changed to html, library removed
generate_cliques structure has changed, new plot function.
Changes in version 0.99.8
Logfile added to runrewiring method.
Message structure of preparerewiring method has been cleaned.
Changes in version 0.99.7
Rewiring method outputs every supermodule
Changes in version 0.99.6
vignette files are locally available (instead of github)
SummarizedExperiment objects are allowed to use
code style and structure has been improved
Changes in version 0.99.5
openxlsx to xlsx library changed
Changes in version 0.99.4
class() functions removed.
Changes in version 0.99.3
excel_generation function added
vignette file updated
Changes in version 0.99.2
rewiring method part parallelized
Changes in version 0.99.1
vignette file added
minor bugs fixed
Changes in version 0.99.0
automatic modules selection in rewiring test.
rewiring html report file added.
unit tests added.
Changes in version 0.99.0 (2020-12-30)
Changes in version 1.15.6
optimized read.nhx for large tree file (2021-03-12, Fri)
https://github.com/YuLab-SMU/treeio/pull/51
Changes in version 1.15.5
https://github.com/YuLab-SMU/treeio/pull/50
Changes in version 1.15.4
https://github.com/YuLab-SMU/treeio/pull/46/files
Changes in version 1.15.3
https://github.com/YuLab-SMU/treeio/pull/44
Changes in version 1.15.2
https://github.com/YuLab-SMU/treeio/pull/40
Changes in version 1.15.1
Changes in version 0.99.3
Changes in version 0.99
Initial release.
Added preprint to CITATION, vignette.
Changes in version 1.30.0
Migrated createClusterMST() to the TrajectoryUtils package.
Modified orderCells() to return a more informative PseudotimeOrdering object.
Handle pseudotime matrices in testPseudotime() by testing each path separately. Support inclusion of custom row.data= in each output DataFrame.
Changes in version 0.99.0 (2020-09-30)
Changes in version 1.10.0
Added more tximeta() messaging about specifying the ‘source’ in linkedTxome. Essentially, this triggers GTF processing behavior that users may want to avoid, and so specifying a string other than “Ensembl” may be preferred. Also added note to vignette.
Added note to vignette about alevin import with tximeta where the ‘tgMap’ step requires gene IDs and not gene symbols.
Added hashes for: GENCODE 38 (H.s.), M27 (M.m), and Ensembl 104; GENCODE 37 (H.s.), M26 (M.m), and Ensembl 103; GENCODE 36 and Ensembl 102.
Fixed a bug where multiple parsed Ensembl GTF TxDb would be added to the BiocFileCache with the same rname.
Changes in version 1.9.11
Added hashes for GENCODE 38 (H.s.), M27 (M.m), and Ensembl 104.
Changes in version 1.9.6
Using tools::R_user_dir instead of rappdirs, in line with changes in BiocFileCache..
Changes in version 1.9.5
Added hashes for GENCODE 37 (H.s.), M26 (M.m), and Ensembl 103.
Changes in version 1.9.2
Changes in version 1.19.4
Changes in version 1.1.9
Allow selection of number of reads to load from FastQ file in prep and split functions. Default: 1M reads (1e9).
Minor fixes BiocCheck.
Changes in version 1.1.8
Use query_regions
to select the restriction fragments to use for
differential
testing in diffWaldUMI4C
.
Changes in version 1.1.7
Fixed bug with limits of the log2 OR values when plotting differential windows.
Changes in version 1.1.6
Fixed bug in creation of gene annotation when an exon belongs to more than one transcript.
Fixed bug when only one sample is provided (no name in assay columns).
Changes in version 1.1.5
Fixed bug in selection of reference UMI4C sample when more than 1
sample
has the same number of total UMIs. Now it will select the first one.
The selection of sample to use as reference can be overriden by the
ref_umi4c
argument.
Changes in version 1.1.4
Fixed bug in domainogram plotting where white color was not aligned with 0 log2 FC.
Changes in version 1.1.3
Fixed bug when providing a reference sample to use for normalizing UMI counts.
Changes in version 1.1.2
Fixed bug when cut_pos
!=0 that generated a gap in the digested
genome
object (see issue #8)
Changes in version 1.10.0
NEW FEATURES
A new data structure, universalmotif_df, has been made available. This allows for motifs to be manipulated as one would a data.frame object. The to_df() function is used to generate this stucture from lists of motifs. The update_motifs() function is used to apply changes to the actual motifs, and to_list() returns the actual motifs. Note that this is only meant as an option for more conveniently manipulating motif slots of multiple motifs simultaneously before returning them to a list; the universalmotif_df structure cannot be used in the various universalmotif functions. Additionally, requires_update() can be used to ascertain whether motifs are out of date in a universalmotif_df object. Many thanks to @snystrom for discussions and significant contributions.
view_motifs(): the universalmotif package now relies entirely on its own code to generate the polygon data used by ggplot2 to plot motifs, meaning the ggseqlogo import has been dropped. A number of new options are now available, including plotting multifreq logos and finer control over letter spacing. An effort has been made to ensure that the default behaviour of the function be unchanged from previous versions. This change should also allow for easier fixing of bugs and flexibility for future additions or changes.
New function, merge_similar(): identify and merge similar motifs in a list of motifs. Essentially, a wrapper around compare_motifs(), hclust(), cutree(), and merge_motifs().
New function, view_logo(): plot logos with matrix input instead of motif object input. Arbitrary column heights and multi-character letters are allowed.
New function, average_ic(): calculate the average information content for a list of motifs.
trim_motifs(…, trim.from): trim from both directions or just one.
shuffle_sequences(…, window, window.size, window.overlap): shuffle sequences iteratively in windows of specified size.
scan_sequences(…, return.granges): optionally return a GRanges object.
scan_sequences(…, no.overlaps, no.overlaps.by.strand, no.overlaps.strat): remove overlapping hits after scanning, preventing overlapping hits by the same motifs from being returned. This can optionally be done per strand. Either the first hit or the highest scoring hit can be preserved per set of overlapping hits. These new arguments can also be used in enrich_motifs().
scan_sequences(…, respect.strand): whether to scan the sequence strands according to the motif strand slot. Only applicable for DNA/RNA motifs. This option is also available in enrich_motifs().
MINOR CHANGES
Some additions and clean-up to documentation and vignettes.
Support for MotIV-pwm2 formatted motifs has been dropped, as the package is no longer a part of the current Bioconductor version.
read_matrix()/write_matrix(): the sep argument can now be NULL (no seperators.)
The Rdpack dependency has been dropped.
merge_motifs(): single-motif input now simply returns the motif instead of throwing an error.
view_motifs(…, dedup.names): now TRUE by default. Furthermore, the make.unique() function is now used to deduplicate names.
compare_motifs(…, method): the default comparison method has been changed back to PCC.
BUG FIXES
Using create_motif() with a single character no longer throws an error.
Generating random motifs with filled multifreq slots now works.
Changes in version 0.99.0 (2018-05-15)
Changes in version 1.2.5 (2021-02-14)
Changed package name to VaSP from vasp.
Changes in version 1.2.1 (2021-01-16)
Added the citation and improved some codes.
Changes in version 1.1.6
Move sanity check vignette to inst/.
Changes in version 1.1.5
Add Michael Stadler to package authors.
Changes in version 1.1.4
Fix typo in documentation.
Changes in version 1.1.3
Add vignette subdirectory with sanity checks.
Changes in version 1.1.2
Add functions plotVelocity and plotVelocityStream.
Changes in version 1.1.1
Refresh cached environments.
Changes in version 1.1.0
Bioconductor release 1.1.0.
Changes in version 0.99.0
Changes in version 0.99.8 (2021-05-07)
Changes in version 3.13.8
Fix plotQC() for XCMSnExp objects
Changes in version 3.13.7
Add featureArea
function to extract the m/z-rt region for features.
Fix featureSpectra
function.
Re-add the LC-MS/MS vignette.
Feature: plotQC() supports XCMSnExp objects now
Changes in version 3.13.6
Fix issue #545: skip second centWave run with CentWavePredIsoParam in regions of interest with undefined peak boundaries/scan ranges.
Temporarily remove the LC-MS/MS vignette (until MsBackendMgf is added to Bioconductor).
Changes in version 3.13.5
Add filterChromPeaks
method to filter chromatographic peaks in a
XChromatogram
or XChromatograms
object.
Add filterChromPeaks
method for XCMSnExp
(issue #541).
Support return of Spectra
objects by chromPeakSpectra
,
featureSpectra
and reconstructChromPeakSpectra
.
Support extraction of MS1 spectra with chromPeakSpectra
.
Support extraction of the spectrum with the largest total signal or
largest
base peak signal in chromPeakSpectra
.
Add support for extraction of spectra for selected/individual
peaks/features
using the peaks
and features
parameter in chromPeakSpectra
and
featureSpectra
, respectively.
Changes in version 3.13.4
Import Param
object from ProtGenerics
.
Import filterIntensity
, normalize
and alignRt
for
Chromatogram
and
MChromatograms
from MSnbase
.
Changes in version 3.13.3
align,Chromatogram
gains new method "none"
which will only keep
values
with identical retention times. For method = "matchRtime"
the (much
faster)
matching function closest
from the MsCoreUtils
package is used.
Method correlate,Chromatogram
gains parameter useIntensitiesAbove
to
perform the correlation only with values larger than this threshold
(avoiding thus high correlation because of many 0-values).
Add method filterIntensity,Chromatogram
that allows to filter a
chromatogram
object keeping only data points with an intensity above a user
provided
threshold.
Changes in version 3.13.2
Add new function manualChromPeaks
allowing to manually add and
integrate
chromatographic peaks.
Changes in version 3.13.1
Support subsetting of XChromatograms
with drop = FALSE
.
Changes in version 1.17.2
We provide a new function LCD_extractCohort_callPerPID() which also belongs to the LCD family and which performs the detection of signatures at cohort-wide level, but re-runs the actual computation of the exposures per-PID with only the signatures identified in the cohort-wide calling. The ovall wrapper function LCD_complex_cutoff_combined() now also calls the new function and stores the result in the returned list with item name extractCohort_callPerPID
Changes in version 1.17.1
Introduction of an input parameter minimumNumberOfAlterations for the functions LCD_complex_cutoff_perPID(), LCD_complex_cutoff_consensus() and LCD_complex_cutoff_combined(). If a sample has less mutations than this cutoff, a warning is issued. By default, this values is set to 25 and may be a good choice for analysis of SNV mutational signatures. For analysis of Indel mutational signatures, a better choice is 20.
Changes in version 1.2.0
Update anndata and other Python dependencies, now using anndata v0.7.6
Improved conversion checks for all slots in AnnData2SCE()
Enable return conversion of the varm slot in AnnData2SCE()
Avoid converting obsp and varp to dense matrices in AnnData2SCE()
AnnData2SCE() should now always return dgCMatrix matrices when assays are sparse
More consistent conversion of metadata to uns in SCE2AnnData()
Handle conversion of list columns in colData and rowData in SCE2AnnData()
Better support for converting anndata SparseDataset arrays
Improved support for conversion of HDF5 backed AnnData objects
Better support for writing DelayedArray assays in writeH5AD()
Store X_name in AnnData2SCE() for use by SCE2AnnData() and add an X_name argument to AnnData2SCE() and readH5AD()
Add a compression argument to writeH5AD()
Export zellkonverterAnnDataEnv for use by other packages
Changes in version 0.99.3
NEW FEATURES
Changes in version 2.16.0
Changes in version 3.0.0
curatedMetagenomicData now contains 20,283 samples from 86 studies
A total of 10,084 samples added since Bioconductor 3.10 (October 2019)
Studies added since Bioconductor 3.10 (October 2019):
The curatedMetagenomicData()
method has been refactored for efficiency
mergeData()
method has been refactored for accuracy and efficiencyreturnSamples()
method has been added for returns across studiessampleMetadata
object replaces the combined_metadata
objectcombined_metadata
object will be removed in the next releaseA number of methods have moved directly to defunct status:
cmdValidVersions()
getMetaphlanTree()
ExpressionSet2MRexperiment()
ExpressionSet2phyloseq()
All named accessors (e.g. HMP_2012.pathcoverage.stool()
) have become defunct
curatedMetagenomicData()
method replaces all named accessors Changes in version 1.14.0
New features
The version argument now allows users to select either 1.1.38 or 2.0.1.
Version 2.0.1 includes RNASeq2Gene data as RSEM TPM gene expression values (#38, @mherberg).
Genomic information updated for RaggedExperiment type data objects where ‘37’ is now ‘GRCh37’ (#40, @vjcitn).
Datasets (e.g., OV, GBM) that contain multiple assays that could be merged are now provided as merged assays (#27, @lwaldron).
The vignette now includes sections on how to use the TCGAprimaryTumors and getWithColData functions.
Bug fixes and minor improvements
Changes in version 1.5.1
crispr
, copyNumber
, TPM
, mutationCalls
and metadata
datasets. Newer versions for the other datasets were not released. Changes in version 0.99.0
Changes in version 1.3.2 (2021-03-09)
Added pancancer regulons for application in cancer.
Changes in version 1.3.1 (2021-02-08)
Fixed bug in Seurat’s related unit tests due to Seurats package update to version 4.0. s@assays$dorothea@misc is now list(), before it was NULL.
Changes in version 0.99.0
Changes in version 0.99.11
Changes in version 1.8.0
Other notes
Changes in version 0.99.9 (2021-04-20)
Added data from Zanotelli et al. Mol Syst Biol 16:e9798(2020)
Changes in version 0.99.8 (2021-04-15)
Allow on disk storage of images and masks
Changes in version 0.99.7 (2021-03-24)
Improved documentation
pkgdown website
Changes in version 0.99.6 (2021-03-23)
Added data from Jackson, Fischer et al. Nature 578,615–620(2020)
Changes in version 0.99.0 (2020-11-12)
Extended vignette
Added function documentation
Added dataset documentation
Formatted the package for Bioconductor submission
Changes in version 0.1.0 (2020-11-02)
Initial commit
Creation of the imcdatasets package
Added the damond-pancreas-2019 dataset
Changes in version 0.99.3
Add human PBMC data
Add LRcell related information in vignettes
Changes in version 0.99.2
change dependency back to R >=4.1
R>=3.6 triggers warning
Changes in version 0.99.1
change dependency to R >=3.6
Changes in version 0.99.0
version 0.99.0 released
Submitted to Bioconductor
Changes in version 0.99.0 (2021-01-21)
Changes in version 0.99.5 (2021-04-21)
Changes in version 0.99.0
Changes in version 0.99.8 (2021-04-08)
ptairData Watched Tags added to Bioconductor Support Site User Profile
Changes in version 0.99.7 (2021-04-01)
extended description
added BugReports to description files
completed NEWS file
added inst/script/script.R
added section installation and sessionInfo to the vignette
Changes in version 0.99.0 (2021-03-05)
Submitted to Bioconductor
Changes in version 1.29.2
Suggest rpx version 1.99.2 or later (to make use of caching and avoid repeated downloads).
Remove the shinyMA function.
Delete the code from the rTANDEM section, and only mention the package.
Remove synapter(data) suggestion.
Changes in version 1.29.1
Specify MSnID::peptides() (see also https://github.com/vladpetyuk/MSnID/issues/12).
Changes in version 1.5.1
Changes in version 0.99.3
Added liang2020_hela
datasets <2020-04-23>
Changes in version 0.99.2
Removed remaining tilde (U+223C) in man pages <2020-01-09>
Changes in version 0.99.1
Removed tilde (U+223C) in man pages <2020-01-09>
Changes in version 0.99.0
Changes in version 2.6.0
Added the Bacher T cell dataset.
Added the Bhaduri organoid dataset.
Added the Darmanis brain dataset.
Added the Ernst spermatogenesis dataset.
Added the Fletcher olfactory dataset.
Added the Giladi HSC dataset.
Added the He organ atlas dataset.
Added the Jessa brain dataset.
Added the Nowakowski cortex dataset.
Added the Pollen glia dataset.
Added the Zeisel nervous system dataset.
Added the Zhao immune liver dataset.
Added the Zhong prefrontal cortex dataset.
Added the Bunis HSPC dataset (Dan Bunis).
Changes in version 0.99.1 (2021-03-05)
Changes in version 1.4.0
New features
SingleCellMultiModal function allows the combination of multiple multi-modal technologies.
GTseq data from Macaulay et al. (2015) now available (@lgeistlinger)
SCoPE2 data from Specht et al. now available thanks to @cvanderaa (#26)
scMultiome provides PBMC from 10X Genomics thanks to @rargelaguet
Bug fixes and minor improvements
Metadata information (function call and call to technology map) included in SingleCellMultiModal
scNMT includes the original call in the MultiAssayExperiment metadata
Improved and edited Contributing Guidelines for clarity
Changes in version 1.3.19
SIGNIFICANT USER-VISIBLE CHANGES
spatialLIBD has been updated to work with SpatialExperiment version 1.1.701 which will be released as part of Bioconductor 3.13. This changes internal code of spatialLIBD which will work with any objects created with SpatialExperiment version 1.1.700.
Changes in version 1.3.16
SIGNIFICANT USER-VISIBLE CHANGES
The citation information has changed now that spatialLIBD has a bioRxiv pre-print at https://www.biorxiv.org/content/10.1101/2021.04.29.440149v1.
Changes in version 1.3.15
SIGNIFICANT USER-VISIBLE CHANGES
We now use plotly::toWebGL() to make the web application more responsive.
Changes in version 1.3.14
SIGNIFICANT USER-VISIBLE CHANGES
The documentation and help messages shown in the web application have been revamped and improved.
Changes in version 1.3.12
NEW FEATURES
We added a new vignette that shows how you can use spatialLIBD with any 10x Genomics Visium dataset processed with spaceranger. The vignette uses the publicly available human lymph node example from the 10x Genomics website.
Changes in version 1.3.3
NEW FEATURES
Overall the package has been updated to use SpatialExperiment version 1.1.427 available on Bioconductor 3.13 (bioc-devel). Several functions were re-named such as sce_image_gene_p() now has a shorter name vis_gene_p(). This update also changes these visualization functions to ONLY support SpatialExperiment objects instead of the original modified SingleCellExperiment objects.
Updated citation information to reflect that https://doi.org/10.1038/s41593-020-00787-0 is now public. Also added a link on the README to https://doi.org/10.6084/m9.figshare.13623902.v1 for the manuscript high resolution images.
Changes in version 0.99.0 (2021-03-28)
Changes in version 0.99.0
Changes in version 1.1.2
Data is now hosted on FigShare.
Added new datasets: GSE150430, GSE154778, GSE125969, GSE134520, GSE123366
Changes in version 1.1.1
Now uses BiocFileCache to download data.
Changes in version 0.99.11
Sixty Five software packages were removed from this release (after being deprecated in Bioc 3.12): adaptest, ArrayTV, BioSeqClass, CHARGE, chimera, CNVtools, CorMut, DESeq, explorase, flowFit, flowSpy, flowType, focalCall, FourCSeq, FunciSNP, GeneticsDesign, GenRank, GGBase, GGtools, GOFunction, gQTLBase, gQTLstats, hicrep, ImpulseDE, ImpulseDE2, joda, JunctionSeq, LINC, Logolas, mcaGUI, metaArray, metaseqR, methVisual, methyvim, Mirsynergy, MmPalateMiRNA, MOFA, MotIV, NarrowPeaks, netbenchmark, netReg, OGSA, OmicsMarkeR, pathprint, PathwaySplice, PGA, PGSEA, plrs, prada, Prize, Rariant, reb, Roleswitch, rTANDEM, sapFinder, scsR, shinyTANDEM, sigaR, signet, simpleaffy, spotSegmentation, Starr, SVAPLSseq, TxRegInfra, xps
Forty nine software are deprecated in this release and will be removed in Bioc 3.14: AffyExpress, affyQCReport, AnnotationFuncs, ArrayTools, bigmemoryExtras, BiocCaseStudies, CancerMutationAnalysis, CexoR, ChIPSeqSpike, CompGO, CoRegFlux, CrossICC, cytofast, DBChIP, dexus, EasyqpcR, EDDA, eisa, ELBOW, ExpressionView, FlowRepositoryR, genoset, HCABrowser, HCAExplorer, HCAMatrixBrowser, Imetagene, IntramiRExploreR, mdgsa, metagenomeFeatures, methyAnalysis, MSEADbi, OutlierD, pcot2, PCpheno, Polyfit, POST, RchyOptimyx, RDAVIDWebService, RNAither, RNAprobR, rnaSeqMap, SAGx, samExploreR, seqplots, simulatorZ, SSPA, ToPASeq, XBSeq, yaqcaffy
Fourteen experimental data packages were removed this release (after being deprecated in BioC 3.12): flowFitExampleData, FunciSNP.data, geuvPack, geuvStore2, GGdata, methyvimData, mitoODEdata, Mulder2012, pathprintGEOData, pcaGoPromoter.Hs.hg19, pcaGoPromoter.Mm.mm9, pcaGoPromoter.Rn.rn4, waveTilingData, yriMulti
Eleven experimental data packages are deprecated in this release and will be removed in Bioc 3.14: ceu1kg, ceu1kgv, ceuhm3, cgdv17, dsQTL, facsDorit, gskb, hmyriB36, JctSeqData, MAQCsubsetAFX, yri1kgv
Fourteen annotation packages were removed from this release (after being deprecated in Bioc 3.12): hom.At.inp.db, hom.Ce.inp.db, hom.Dm.inp.db, hom.Dr.inp.db, hom.Hs.inp.db, hom.Mm.inp.db, hom.Rn.inp.db, hom.Sc.inp.db, KEGG.db, MeSH.Eco.55989.eg.db, MeSH.Eco.ED1a.eg.db, MeSH.Eco.IAI39.eg.db, MeSH.Eco.UMN026.eg.db, MeSH.Eqc.eg.db
Eighty seven annotation packages are deprecated in this release and will be removed in Bioc 3.14: 12 LRBase.XXX.eg.db packages (replaced with AHLRBaseDbs), MafDb.gnomAD.r3.0.GRCh38, MafH5.gnomAD.r3.0.GRCh38, 73 MeSH.XXX.eg.db packages (replaced with AHMeSHDbs)
No workflow packages were removed in this release.
One workflow package is deprecated in this release to be removed in 3.14: eQTL