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

# Getting Started with Bioconductor 3.13

To update to or install Bioconductor 3.13:

1. Install R 4.1.0. Bioconductor 3.13 has been designed expressly for this version of R.

2. Follow the instructions at Installing Bioconductor.

# New Software Packages

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.

# New Data Experiment Packages

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.

# New Annotation Packages

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.

# New Workflow Packages

There is 1 new workflow package in this release of Bioconductor.

• ExpHunterSuite The ExpHunterSuite implements a comprehensive protocol for the analysis of transcriptional data using established R packages and combining their results. It covers all key steps in DEG detection, CEG detection and functional analysis for RNA-seq data. It has been implemented as an R package containing functions that can be run interactively. In addition, it also contains scripts that wrap the functions and can be run directly from the command line.

# New Books

There are no new online books.

# NEWS from new and existing Software Packages

## ACE

             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

## airpart

                   Changes in version 0.0.99

• Submitting to Bioconductor…

## AlpsNMR

                    Changes in version 3.1.5

• Removed warning about future_options deprecation

                  Changes in version 3.1.4


           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)

• Package accepted in bioconductor

## ANCOMBC

             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.

## AnnotationDbi

                   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

• organismKEGGFrame() provides a data.frame of species names and KEGG orgs

MODIFICATIONS

• Conversion from KEGG.db (which was deprecated) to KEGGREST
• This involved changing how KEGG Ids checked when creating a KEGGFrame object.
• Removal of any use of Inparanoid data/structures since the hom.*.inp.db packages have been removed. The data is extremely outdated and there is no replacement for it.
• Removed the InparanoidDb object and all of the methods

## AnnotationForge

                   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

## AnnotationHub

                   Changes in version 2.99.0


• (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

• (2.23.2) Create a new all encompassing vignette that references both ExpeirmentHub and AnnotationHub. Reference this one vignette in all four related packages instead of trying to maintain multiple vigenttes that were essentially the same. This also involves removing CreateAnAnnotationPackage

MODIFICATIONS

• (2.23.1) Fixed ERROR message to better indicate vignette troubleshooting document and fixed reference in Troubleshooting vignette. These ERRORs are triggered by both AnnotationHub and ExperimentHub so clarified the Troubleshooting document is in AnnotationHub.

## AnnotationHubData

                   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

• Moved objects into objects$hclust for HclustParam() when full=TRUE. • Added clusterRMSD() to compute the root-mean-squared-deviation for each cluster. ## BridgeDbR  Changes in version 2.1.2  BUG FIXES • Fixed the link to the webpage to download mapping files ## BUSpaRse  Changes in version 1.5.2 (2020-12-04)  • Updated code that queries Ensembl for new version of biomaRt. • Make sure there’re no NAs in tr2g_intron when chrs_only = TRUE. • Bypassed issue in BSgenome::getSeq in get_velocity_files when genome is DNAStringSet. ## CAGEr  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. ## CAMERA  Changes in version 1.47.1  BUG FIXES • Thanks to Lain (INRAe) and Team W4M for this PR, fixing argument passing to annotatedDiffreport() ## CARNIVAL  Changes in version 2.1.0  • Changed API: introduced separate functions for different flavours of CARNIVAL. • A more convenient way to work with CARNIVAL parameters. Parameters setup is possible through jsons and function calls. • Better file naming to prevent concurrent file writing when running several instances of CARNIVAL. • An easy way to add more solvers. • A possibility to tune CARNIVAL setups for cplex: manual addition of parameters is possible through jsons. • General improvements and refactoring of the code, removed multiple duplications. • Removed multiple experimental conditions in a matrix form (was not used). • Inputs are transformed to vectors (except prior knowledge network). • MetaInfo is saved: runId, parsed data (internal data representation), start time, CARNIVAL flavour. • Reading from preparsed data and/or lp file is possible. ## cbaf  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. ## cBioPortalData  Changes in version 2.4.0  New features • Vignettes now include additional information (#38, @lwaldron) • getDataByGenePanel deprecated for getDataByGenes which handles input of both gene panels and genes • cBioPortalData now allows for gene inputs as either Entrez IDs or Hugo symbols (#24, @jucor) and sampleIds input • When gene inputs are provided, the by argument has to agree with the type of genes provided (either be entrezGeneId or hugoGeneSymbol). Bug fixes and minor improvements • Fixed an issue where the labels in the metadata from cBioDataPack were missing (‘LICENSE’ and ‘Fusion’; #37) • loadStudy allows cleanup=TRUE for removing files after untar-ing • Published article now available with citation(“cBioPortalData”) ## cbpManager  Changes in version 0.1.1  New features • ‘Validation’ tab allows to validate created files. • 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 • backbone of the project started! ## celda  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 ## CelliD  Changes in version 0.99.0  • Submitted to Bioconductor ## cellmigRation  Changes in version 0.99.11 (2021-05-11)  • Addressed all points from the Bioconductor review • Bug fixes and documentation update for Bioconductor release  Changes in version 0.99.0 (2020-09-02)  • Submitted to Bioconductor ## chipenrich  Changes in version 2.16.0  • Transition to Kai Wang as maintainer. ## ChIPpeakAnno  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. ## ChIPseeker  Changes in version 1.27.4  • bug fixed in determine downstream gene (2021-04-27, Thu) • https://github.com/YuLab-SMU/ChIPseeker/pull/148 • getBioRegion now supports ‘3UTR’ and ‘5UTR’ (2021-03-30, Tue) • https://github.com/YuLab-SMU/ChIPseeker/pull/146  Changes in version 1.27.3  • add two parameter, cex and radius, to plotAnnoPie (2021-03-12, Fri) • https://github.com/YuLab-SMU/ChIPseeker/pull/144  Changes in version 1.27.2  • bug fixed of getGenomicAnnotation (2021-03-03, Wed) • https://github.com/YuLab-SMU/ChIPseeker/issues/142  Changes in version 1.27.1  • Add support for EnsDb annotation databases in annotatePeak. • https://github.com/YuLab-SMU/ChIPseeker/pull/120 ## ChromSCape  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. ## CIMICE  Changes in version 0.99.0  Overview: • First commit. New functionalities: • Input dataset read an creation • CIMICE analysis and CPMC inference • Output data visualization ## circRNAprofiler  Changes in version 1.5.3  • Removed citr package from DESCRIPTION ## cleanUpdTSeq  Changes in version 1.29.1  • rewrite the package by Haibo ## clusterProfiler  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  • setting default timeout to 300 for downloads (2021-02-05, Fri) • fixed download method setting • capable of setting KEGG download method via options(clusterProfiler.download.method = METHOD) (2020-12-31, Thu) ## clustifyr  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 ## CNVfilteR  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 • Bug fixed: SNVs were not being correctly loaded after last Bioconductor update 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 ## CNViz  Changes in version 0.99.3 (2021-04-14)  • Added a NEWS.md file to track changes to the package • Submitted to Bioconductor ## cola  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. ## coMET  Changes in version 1.23.1 (2021-05-16)  • Update datasets with Biomart For example: data(allIG) allIG@biomart@httr_config <- list() save(allIG,file=”XXXX”) ## ComplexHeatmap  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. ## ComPrAn  Changes in version 0.99.0  • Submitted to Bioconductor ## conclus  Changes in version 0.99.343 (2021-04-09)  • Submission to Bioconductor • NAMESPACE • Imported BiocFileCache package and R_user_dir() of tools package • Exported new function conclusCacheClear() • DESCRIPTION • Removed LazyData: true. • DataFormatting.R • Added a caching system for retrieveFromGEO() • Created conclusCacheClear() to delete the cache • Updated documentation • loadDataset.R • Updated documentation • 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 • Modified documentation for loadDataOrMatrix() • 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 • Changed the experiment name to “Light_Experience” • Used tempdir() for output directory • inst • Added inst/script to generate data on inst/extdata • New data generated • vignette • Used tempdir() for output directory • Specified other parameters in the first example of runCONCLUS for the very small dataset • Replaced old paths by new ones ## condiments  Changes in version 0.99.0 (2021-03-01)  • Submitted to Bioconductor ## CONSTANd  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 ## cosmosR  Changes in version 0.99.2  • Submitted to bioRxiv • Release of github page • Submitted to Bioconductor ## csaw  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. ## cTRAP  Changes in version 1.10.0  Improvements to graphical interface functions: • New launchDrugSetEnrichmentAnalysis() function to analyse drug set enrichment and visualize respective results • launchCMapDataLoader(): • Now allows to load multiple CMap perturbation types simultaneously • Keep selected timepoint, dosage and cell line options when selecting another perturbation type • Add bubble plot of CMap perturbation types • launchResultPlotter(): • Now allows to view tables below specific plots and drag-and-select those plots to filter data in those same tables • When plotting targeting drugs and similar perturbations, update available columns and correctly use user-selected column to plot • launchMetadataViewer() now correctly parses values from Input attributes as numeric Major changes • prepareCMapPerturbations(): directly set perturbation type, cell line, timepoint and dosage conditions as arguments • rankSimilarPerturbations() and predictTargetingDrugs(): • Avoid redundant loading of data chunks, slightly decreasing run time • Lower memory footprint when using NCI60’s gene expression and drug sensitivity association (now available in HDF5 files) by loading and processing data in chunks • Faster GSEA-based score calculation (up to 4-7 times faster) • New threads argument allows to set number of parallel threads (not supported on Windows) • New chunkGiB argument allows to set size of data chunks when reading from supported HDF5 files (decreases peak RAM usage) • New verbose argument allows to increase details printed in the console • prepareDrugSets(): allow greater control on the creation of bins based on numeric columns, including the setting of maximum number of bins per column and minimum bin size • analyseDrugSetEnrichment() and plotDrugSetEnrichment(): allow to select columns to use when comparing compound identifiers between datasets Bug fixes and minor changes • filterCMapMetadata(): allow filtering CMap metadata based on multiple perturbation types • prepareDrugSets(): fix issues with 3D descriptors containing missing values • plot(): • Fix wrong labels when plotting targetingDrugs objects • Avoid printing “NA” in labels identifying metadata for perturbations • plotTargetingDrugsVSsimilarPerturbations(): • Fix highlighting of plot points depending whether drug activity is directly proportional to drug sensitivity • Include rug plot • When subsetting a perturbationChanges or an expressionDrugSensitivityAssociation object, passing only one argument extracts its columns as in previous versions of cTRAP (similarly to when subsetting a data.frame) • analyseDrugSetEnrichment(): for the resulting table, the name of the first column was renamed from pathway to descriptor ## customCMPdb  Changes in version 1.1.0 (2020-10-02)  • Initial version ## CytoGLMM  Changes in version 0.99.0 (2021-02-19)  • Submitted to Bioconductor ## cytomapper  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 ## CytoML  Changes in version 3.11  API Changes • Rename argument sampNLoc -> sample_names_from in open_flowjo_xml • All parsers (flowjo/cytobank/diva_to_gatingset) now return GatingSet based on cytoset rather than ncdfFlowSet • Add trans argument to cytobank_to_gatingset to allow overriding of transformations from gatingML file (#76) • gatingset_to_flowjo now uses a docker image with a compiled converter: hub.docker.com/r/wjiang2/gs-to-flowjo • Some updates to how flowjo_to_gatingset searches for FCS files (#77) • Add include_empty_tree option to flowjo_to_gatingset to include samples without gates • Allow gatingset_to_flowjo to take a path to a GatingSet archive directory • Add gating_graphGML to replace gating.graphGML method for openCyto::gating generic • Filter samples by panel when parsing cytobank experiment and add ce_get_samples, ce_get_panels Fixes/internal changes • Automatic time scaling of samples from FlowJo workspaces now handled by flowjo_to_gatingset RGLab/cytolib#33 • Handle change to default stringsAsFactors=FALSE in R 4.0 • Eliminated extra intermediate files left in temp directory during workspace parsing • Switch usage of GatingSetList to merge_gs_list • Solve some Windows build issues • Switch from experimental::filesystem to boost::filesystem in C++ FlowJo parser • 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: • openWorkspace -> open_diva_xml, open_flowjo_xml • cytobankExperiment -> open_cytobank_experiment • cytobank2GatingSet -> cytobank_to_gatingset • parseWorkspace -> flowjo_to_gatingset, diva_to_gatingset • getSampleGroups -> fj_ws_get_sample_groups, diva_get_sample_groups • getSamples -> fj_ws_get_samples, diva_get_samples • getKeywords -> fj_ws_get_keywords • getCompensationMatrices -> ce_get_compensations • getTransformation -> ce_get_transformations • compare.counts -> gs_compare_cytobank_counts • Renaming of classes: • divaWorkspace -> diva_workspace • flowJoWorkspace -> flowjo_workspace • Add CytoML.par.set, CytoML.par.get for setting parameters in CytoML namespace Fixes/internal changes • Make gatingset_to_cytobank export cytobank ML with attribute namespaces • Allow diva_to_gatingset to use compensation matrix from xml • Pass … args from cytobank_to_gatingset appropriately down to FCS parser • Fix some issues with scaling of gates parsed from Diva workspace (#64) • Guard against unsupported transformations being added to GatingSet during Diva parsing • Switch diva_to_gatingset to using flowjo_log_trans instead of logtGml2_trans • Fix ported flowUtils::xmlTag to enable self-closing tags • Make gating.graphGML lookup tailored gates by FCS name as well as file id • Add some flexibility to getSpilloverMat used in gatingset_to_flowjo  Changes in version 1.29.1  • add citation. ## DAMEfinder  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 ## dce  Changes in version 0.99.0 (2021-01-25)  • Submitted to Bioconductor ## decompTumor2Sig  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). ## decoupleR  Changes in version 0.99.0  New features • All new features allow for tidy selection. Making it easier to evaluate different types of data for the same method. For instance, you can specify the columns to use as strings, integer position, symbol or expression. 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: • New run_gsva() incorporate a convinient wrapper for GSVA::gsva(). • New run_mean() calculates both the unnormalized regulatory activity and the normalized (i.e. z-score) one based on an empirical distribution. • New run_ora() fisher exact test to calculate the regulatory activity. • New run_pscira() uses a logic equivalent to run_mean() with the difference that it does not accept a column of likelihood. • New run_scira() calculates the regulatory activity through the coefficient$\beta_1of an adjusted linear model. • New run_viper() incorporate a convinient wrapper for viper::viper(). Converters • New functions family convert_to_ variants that allows the conversion of data to a standard format. • New convert_to_() return the entry without modification. • New convert_to_gsva() return a list of regulons suitable for GSVA::gsva(). • New convert_to_mean() return a tibble with four columns: tf, target, mor and likelihood. • New convert_to_ora() returns a named list of regulons; tf with associated targets. • New convert_to_pscira() returns a tibble with three columns: tf, target and mor. • New convert_to_scira() returns a tibble with three columns: tf, target and mor. • New convert_to_viper() return a list of regulons suitable for viper::viper() ## DeepPINCS  Changes in version 0.99.0 (2021-03-21)  • submission to Bioconductor ## deepSNV  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”) ## DEGreport  Changes in version 1.27.1  • Fix: Export n() from dplyrs.#27 ## DelayedArray  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. • Proper support for dgRMatrix and lgRMatrix objects as DelayedArray object seeds: • is_sparse() now returns TRUE on dgRMatrix and lgRMatrix objects. • Support coercion back and forth between SparseArraySeed objects and dgRMatrix/lgRMatrix objects. • Add extract_sparse_array() methods for dgRMatrix and lgRMatrix objects. • These changes bring the treatment of dgRMatrix and lgRMatrix objects to the same level as dgCMatrix and lgCMatrix objects. For example, wrapping a dgRMatrix or lgRMatrix object in a DelayedArray object will trigger the same sparse-optimized mechanisms during block processing as when wrapping a dgCMatrix or lgCMatrix object. • rbind() and cbind() on sparse DelayedArray objects are now fully supported. • Delayed operations of type DelayedUnaryIsoOpWithArgs now preserve sparsity when appropriate. • Implement DummyArrayGrid and DummyArrayViewport objects. SIGNIFICANT USER-VISIBLE CHANGES • Rename viewportApply()/viewportReduce() -> gridApply()/gridReduce(). 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). ## DelayedMatrixStats  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>). ## DelayedRandomArray  Changes in version 1.0.0  • New package DelayedRandomArray, for delayed generation of random numbers. ## densvis  Changes in version 1.2.0  • Use umap-learn python library instead of densmap-learn. Add umap function for non density-preserving umap. • Add normalize argument to dens-SNE. ## DepecheR  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 ## DESeq2  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. ## DEWSeq  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 ## DExMA  Changes in version 0.99.0  • DExMA release. ## DIAlignR  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. ## DiffBind  Changes in version 3.2  • New type of plot: dba.plotProfile() • Can mix single-end and paired-end bam files • Various bug fixes ## diffuStats  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  • Fixed issue in Rcpp code, due to deprecation in arma, info here • Updated some warnings and notes from BiocCheck() • Updated readme to link to bioconductor and the journal publications ## diffUTR  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 ## dittoSeq  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. ## DOSE  Changes in version 3.17.1  • support setting seed for fgsea method if e.g. gseGO(seed = TRUE) (2020-10-28, Wed) • https://github.com/YuLab-SMU/DOSE/issues/45 ## DropletUtils  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. ## drugTargetInteractions  Changes in version 0.99.0 (2021-01-11)  • Submitted to Bioconductor ## Dune  Changes in version 1.3.01 (2020-11-06)  • Dune now accepts multiple metrics • Dune now uses the NMI by default ## dupRadar  Changes in version 1.21.2  • New pkgdown documentation ## easyRNASeq  Changes in version 2.27.1  • DESeq dependency removal • Added extra warning about RPKM usage • Removed Defunct functions ## edgeR  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. ## EnhancedVolcano  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) ## EnMCB  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 • Some fixes related to test functions. ## EnrichedHeatmap  Changes in version 1.21.1  • if the matrix is non-negative, after smoothing, negative values are reset to zero. ## EnrichmentBrowser  Changes in version 2.22.0  • GO gene sets: option for hierarchical annotation (new argument hierarchical for function getGenesets) ## enrichplot  Changes in version 1.11.3  • Reconstruct the emapplot function and replace emapplot_cluster by emapplot(group_category = TRUE) • fix bug in emapplot_cluster.enrichResult when the number of cluster is 2 (2021-2-24, Wed) • fix bug in treeplot: The legend is not the right size (2021-2-6, Sat). • fix dotplot for label_format parameter doesn’t work(2021-2-3, Wed). • fix bug in gseaplot2(2021-1-28, Thu)  Changes in version 1.11.2  • update document (2021-1-7, Thu) • update dotplot: replace ggsymbol::geom_symbol with ggstar::geom_star(2021-1-6, Wed) • add parameter shadowtext for three functions: emapplot, emapplot_cluster and cnetplot. (2021-1-5, Tue) • update dotplot: supports the use of shapes and line colors to distinguish groups (2021-1-3, Sun) • add treeplot function (2020-12-29, Tue) • rename function get_ww to get_similarity_matrix (2020-12-29, Tue) • move the emapplot related functions to emapplot_utilities.R • fix bug in emapplot and cnetplot when enrichment result is one line (2020-12-26, Sat) • fix pairwise_termsim for the bug of repeated filtering of showCategory(2020-12-23, Wed) • fix showCategory for cnetplot, emapplot, emapplot_cluster when showCategory is a vector of term descriptions  Changes in version 1.11.1  • add orderBy and decreasing parameters for ridgeplot() (2020-11-19, Thu) • https://github.com/YuLab-SMU/enrichplot/pull/84/ • update emapplot_cluster() to label cluster in center by default and use ggrepel if setting repel = TRUE (2020-11-08, Mon) • https://github.com/YuLab-SMU/enrichplot/pull/81 • add a label_format parameter to support formatting label (2020-10-28, Wed) • if provided with a numeric value will simply string wrap by default • if provided with a function will instead set labels = user_defined_function() within the scale function • https://github.com/YuLab-SMU/enrichplot/pull/73 ## ensembldb  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. ## ensemblVEP  Changes in version 1.33.0  • add support for Ensembl release 102/103/104 ## epialleleR  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 ## epigraHMM  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. ## escape  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 ## evaluomeR  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”. ## EWCE  Changes in version 1.0.0  New Features • EWCE v1.0 on Bioconductor replaces the defunct EWCE v1.3.0 available on Bioconductor v3.5. • EWCE has been rendered scalable to the analysis of large datasets • drop_uninformative_genes() has been expanded to allow the utilisation of differential expression approaches • EWCE can now handle SingleCellExperiment (SCE) objects or other Ranged SummarizedExperiment (SE) data types and as input as well as the original format, described as a single cell transcriptome (SCT) object. Deprecated & Defunct • The following functions have been renamed to use underscore in compliance with Bioconductor nomenclature: check.ewce.genelist.inputs cell.list.dist bootstrap.enrichment.test bin.specificity.into.quantiles bin.columns.into.quantiles add.res.to.merging.list prepare.genesize.control.network prep.dendro get.celltype.table calculate.specificity.for.level calculate.meanexp.for.level generate.celltype.data generate.bootstrap.plots generate.bootstrap.plots.for.transcriptome fix.bad.mgi.symbols fix.bad.hgnc.symbols filter.genes.without.1to1.homolog ewce.plot cells.in.ctd drop.uninformative.genes ## exomePeak2  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. ## ExperimentHub  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 ## ExperimentHubData  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 metaTags. Also checks for valid AnnotationHub or AnnotationHubSoftware biocViews.

BUG CORRECTION

• 1.17.1 Fixed bug to run make*HubMetadata using “.”. Fixed in AnnotationHubData. bumped dependency

## ExperimentSubset

             Changes in version 1.1.0 (2021-05-09)

• Added support for TreeSummarizedExperiment and SpatialExperiment classes

## ExploreModelMatrix

                    Changes in version 1.3.2

• Enable MathJax in tour

## FamAgg

                   Changes in version 1.19.1


## famat

             Changes in version 1.3.0 (2020-11-27)

• Rshiny modifications for figure in paper

## fcoex

             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.

## FEAST

             Changes in version 0.99.0 (2021-04-01)

• Submitted to Bioconductor

## fedup

             Changes in version 0.99.7 (2021-04-24)


• 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

## FGNet

                    Changes in version 3.26

• Queries to DAVID are no longer supported.

## FilterFFPE

             Changes in version 1.1.2 (2020-11-11)

• Fix bugs in keeping extra reads when filtering with minMapBase

• Allow skipping filtering with minMapBase

## fishpond

                    Changes in version 1.8.0

• Added note in vignette about how to deal with estimated batch factors, e.g. from RUVSeq or SVA. Two strategies are outlined: either discretizing the estimate batch factors and performing stratified analysis, or regressing out the batch-associated variation using limma’s removeBatchEffect. Demonstation code is included.

## flowAI

                   Changes in version 1.21.6

• the flow rate check is less sensitive now. Note that the default value of alphaFR is now 0.1 instead of 0.01

## flowCL

                   Changes in version 1.29.1

• Endpoint no longer exists. Please email Justin at justinmeskas@gmail.com to request a fix.

## flowGraph

             Changes in version 0.99.0 (2020-09-30)

• Submitted to Bioconductor

## FlowSOM

                   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


• 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 NClusters, NMetaclusters

• Changed examples that used fsom to flowSOM.res

• 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

## fobitools

                   Changes in version 0.99.56

• New package vignette “Use case ST000291”.
• New package vignette “Use case ST000629”.
• Remove AppVeyor and Travis CI and move codecov to GitHub Actions.
• Updated vignette “Dietary text annotation”.
• Improvements to the annotate_foods() function.

                 Changes in version 0.99.41

• Switch from the sigora CRAN package to the fgsea Bioconductor package to perform enrichment analysis.
• Added new function named msea to perform GSEA using FOBI.

                 Changes in version 0.99.38


                 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:

• fobitools.Rproj file removed
• Rd line widths set to less than 100 characters
• R version dependency updated from 3.6.0 to 4.1.
• Use TRUE/FALSE instead of T/F in ora.R

                 Changes in version 0.99.24

• pkgdown updated.
• 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

• Added a NEWS.md file to track changes to the package.

## FRASER

                    Changes in version 1.2.1

• Add merging of external counts

• Minor bugfixes

## gdsfmt

                   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

• comply with the R devel (> v4.0.3) to work with factor variables in apply.gdsn()

## GeneExpressionSignature

                   Changes in version 1.37.0

• Add a NEWS.md file to track changes to the package.
• Add inst/CITATION file to customise the citation.
• Add a new function PGSEA from the PGSEA to remove the dependency on PGSEA, since PGSEA was deprecated in Bioconductor version 3.12 and removed from 3.13.
• Format code using styler and biocthis, redocument package using Roxygen2.
• Rewrite vignette using knitr, rmarkdown, BiocStyle.
• Fix BiocCheck errors and warnings.

## GENESIS

                   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.

## GeneTonic

                    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

• gs_heatmap() has now the possibility to set the arguments to the call to heatmap generating function, via ellipsis
• gs_heatmap() handles the colors in a consistent way over the different executions, without relying on the random palettes provided by the Heatmap’s annotation functionality - could have been misleading if encountering too similar hues are randomly picked
• the plots obtained via gs_mds() and gs_volcano() now always display the line segments for the data points to be labeled (increasing the readability - as “matching back the label to the drawed circle” - thanks for the suggestion!)

## GENIE3

                   Changes in version 1.13.3

• The regulators can now be provided as a list with different regulators for each gene/feature.

## GenomeInfoDb

                   Changes in version 1.28.0


NEW FEATURES

• Small improvement to the Seqinfo() constructor: if the user doesn’t supply the ‘seqnames’ argument when calling the Seqinfo() constructor, now we try to infer the seqnames from the other arguments.

SIGNIFICANT USER-VISIBLE CHANGES

• Improve seqinfo<- documentation and error messages.

DEPRECATED AND DEFUNCT

• fetchExtendedChromInfoFromUCSC() is now defunct (was deprecated in BioC 3.11)

## GenomicFeatures

                    Changes in version 1.44.0


SIGNIFICANT USER-VISIBLE CHANGES

• Several improvements to makeTxDbFromGFF():
• More GFF3 feature types are recognized as transcripts or genes (commit d7f5980f).
• Improve handling of GFF3 files from Ensembl (commit c1e3fb92).
• Handle exons with no Parent attribute. GFF3 files that contain exons with no Parent attribute no longer break makeTxDbFromGRanges() or makeTxDbFromGFF(). Orphan exons are dropped.

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).

## GenomicOZone

                    Changes in version 1.5.1

• Fixed a bug caused by a deprecated function sjstats::eta_sq().
• In ‘MD_perform_zoning.R’, function ‘sjstats::eta_sq()’ is deprecated. Use ‘lsr::etaSquared()’ instead.
• In ‘DESCRIPTION’, removed ‘sjstats’ from ‘Imports’; added ‘lsr’ into ‘Imports’; removed ‘GEOquery’ from ‘Suggests’.
• No longer downloading the data from GEO in the vignettes. Added the data file in inst/extdata. The data is only used by the vignettes.
• ‘NAMESPACE’ is re-auto-generated by Roxygen.

## GenomicRanges

                    Changes in version 1.44.0


SIGNIFICANT USER-VISIBLE CHANGES

• Replace KEGG.db usage with KEGGREST in vignettes and examples.

DEPRECATED AND DEFUNCT

• The GenomicRangesList() constructor is now defunct (got deprecated in BioC 3.10).

## GenomicScores

                    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.

## GenomicSuperSignature

                    Changes in version 1.0.0

• Initial release of the ‘GenomicSuperSignature’ package

## GEOfastq

                   Changes in version 0.99.0

• Submitted to Bioconductor

                  Changes in version 0.6.5

• Resolved NOTES from BiocCheck::BiocCheck()
• replaced sapply with vapply
• shortened lines
• added a NEWS.md file to track changes to the package.

## geva

             Changes in version 0.99.0 (2021-02-01)

• Submitted to Bioconductor

## ggtree

                    Changes in version 2.5.3

• optimize text angle in geom_cladelab (2021-05-10, Mon)
• https://github.com/YuLab-SMU/ggtree/pull/396

                  Changes in version 2.5.2

• extend ‘continuous’ parameter to support 4 possible values, i.e., ‘none’ to disable continuous transition, ‘color’ (or ‘colour’) to enable continuous color transition, ‘size’ to enable continuous size (branch thickness) transition and ‘all’ to enable continuous color and size transition (2021-04-07, Wed)
• https://github.com/YuLab-SMU/ggtree/pull/385
• https://github.com/YuLab-SMU/ggtree/pull/387
• extendto argument for geom_hilight now compatible with ‘inward_circular’ and ‘dendrogram’ layouts (2021-02-25, Thu)
• https://github.com/YuLab-SMU/ggtree/pull/379

                  Changes in version 2.5.1

• update man file of geom_rootpoint (2021-01-08, Fri)
• label and offset.label introduced in geom_treescale layer (2020-12-23, Wed)
• https://github.com/YuLab-SMU/ggtree/pull/360
• geom_rootedge supports reversed x (2020-12-17, Thu)
• https://github.com/YuLab-SMU/ggtree/pull/358
• geom_nodelab() now supports circular layout (2020-11-26, Thu)
• https://github.com/YuLab-SMU/ggtree/issues/352
• https://github.com/YuLab-SMU/ggtree/pull/353
• branch size can be grandualy changed (2020-10-29, Thu)
• https://github.com/YuLab-SMU/ggtree/pull/349

## ggtreeExtra

                   Changes in version 1.1.12

• import ggtree to pass BiocCheck. (2021-05-14, Fri)

                 Changes in version 1.1.11

• fix a bug to solve the problem (variable of x has NA). (2021-05-13, Thu)
• 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

• don’t inherit aes (global aes from the ggtree). (2021-04-28, Wed)
• subset in mapping also supports the data from ggtree object. (2021-04-29, Thu)
• 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

• add new position functions: position_jitterx and position_jitterdodgex. (2021-04-23, Fri)
• 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

• check whether the value of x is numeric to avoid warnings when x is factor. (2021-02-24, Wed)
• 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

• support title of panel. (2021-02-03, Wed)
• https://github.com/YuLab-SMU/ggtreeExtra/issues/7
• add citation info. (2021-02-04, Thu)
• don’t use svg dev. (2021-02-04, Thu)

                  Changes in version 1.1.4

• add position_points_jitterx and position_raincloudx for geom of ggridges. (2021-01-20, Wed)
• supports geom_msa of ggmsa. (2021-01-21, Thu)
• 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

• support subset in mapping, but the data should also be provided. (2020-11-30, Mon)
• add default position methods for common geometric functions. (2020-12-18, Fri)

## GlobalAncova

                    Changes in version 4.9.1

• removed function ‘GAKEGG’ for testing collection of pathways, due to deprecation of KEGG package

## GOSemSim

                   Changes in version 2.17.1

• bug fixed according to the update of GO.db (2020-10-29, Thu)
• https://github.com/YuLab-SMU/GOSemSim/issues/32

## granulator

             Changes in version 0.99.0 (2021-03-30)

• Submitted to Bioconductor

## graphite

             Changes in version 1.37.1 (2020-05-04)

• Removed Biocarta and NCI pathways.

• Updated all pathway data.

## GSEABase

                    Changes in version 1.54


SIGNIFICANT USER-VISIBLE CHANGES

• orgDb no longer provide Unigene information. Remove support.

## GSVA

                    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.

## HDF5Array

                   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

• Remove “global counter” files at unload time (commit f7913043).

## Heatplus

                   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

## Herper

             Changes in version 1.1.1 (2021-02-19)

• Include option to build miniconda in import from yml, and update vignette

## HGC

             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

## HIBAG

                   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

## HiCDCPlus

            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

## HilbertCurve

                   Changes in version 1.21.1

• add “max_freq” method for mean_mode so that in each interval, value corresponded to the maximal frequency is selected.

## HPAanalyze

                     Changes in version 1.9

• Changes in version 1.9.5
• hpaVisTissue now plots all tissues by default
• Bug fixes and performance improvements
• Changes in version 1.9.4
• hpaVisPatho can now facet by cancers or genes
• Vignettes update
• Bug fixes and performance improvements
• Changes in version 1.9.3
• Update the default color to be more accessible (based on viridis::magma)
• Updated hpaSubset and hpaListParam to work properly with new data
• Changes in version 1.9.2
• Update built-in data to HPA version 20.1.
• Changes in version 1.9.1
• Update built-in data to HPA version 20.
• Changes in version 1.9.0
• Starting devel for Bioconductor 3.13

## hpar

                    Changes in version 1.33


Changes in version 1.33.2

• Update to HPA release 20.0 <2020-11-24 Thu>

Changes in version 1.33.1

• Update to HPA release 19.3 (2020.03.06) <2020-10-26 Mon>
• New release for Bioconductor devel 3.12 <2020-10-26 Mon>
• Updated R version (>= 3.5.0) in Depends field <2020-10-26 Mon>
• Added the Secretome data <2020-10-28 Wed>
• Updated the documentation and docs folder for pkgdown <2020-10-29 Thu>
• Automated allHparData() <2020-10-29 Thu>

Changes in version 1.33.0

• New Bioc devel version

## HPAStainR

                    Changes in version 1.0.4

• The first update in response to F1000 comments

• 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)


           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)


• HPA changed “unfavourable” to “unfavorable”

• The testthat() has been changed to reflect their change

• More updates soon after all reviews from F1000 are in

## HTSFilter

                   Changes in version 1.31.1

• – Remove all references and functionality related to the deprecated DESeq package. – Vignette has been updated to Rmarkdown from Sweave.

## HubPub

             Changes in version 0.99.0 (2021-04-23)

• Submitted to Bioconductor

## ideal

                   Changes in version 1.16.0


New features

• It is now possible to export the processed data (count data, results, functional enrichment table) into a combined list object, which can be seamlessly fed into GeneTonic (http://bioconductor.org/packages/release/bioc/html/GeneTonic.html).
• Adjusted the behavior for the modeling with LRT, with extra notifications to inform the user on how to make the most out of the functionality.

Other notes

• The manuscript of ideal is now published in BMC Bioinformatics! The citation file has been updated accordingly.
• A full round of styler has been applied to the codebase.

## immunoClust

                   Changes in version 1.23.6

• CHANGES
• code cleaning

               Changes in version 1.23.3

• CHANGES
• fixes a testthat misspelling

               Changes in version 1.23.2

• CHANGES
• tolerance sufficience in cell-subclustering not required for first model refinement test

               Changes in version 1.23.1

• NEW FEATURES
• added methods clusterDist, clusterProb, clusterCoeff for immunoMeta-object
• starting with unit tests

## immunotation

             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)


• included a mro.obo.gz file which can be read without unzipping

           Changes in version 0.99.1 (2021-01-01)

• Submitted to Bioconductor

## infercnv

             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)

## Informeasure

             Changes in version 1.1.1 (2020-10-30)

• make changes

## InPAS

                   Changes in version 1.99.4

• update get_PAscore2.

                 Changes in version 1.99.3


                 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.

## InteractiveComplexHeatmap

                   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

## InterCellar

                 Changes in version 0.0.0.9000

• Added a NEWS.md file to track changes to the package.

## IRanges

                   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

• Much improved error handling and messages in IRanges() constructor function

DEPRECATED AND DEFUNCT

• Remove RangesList() constructor (was deprecated in BioC 3.7 and defunct in BioC 3.8).

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).

## IRISFGM

                   Changes in version 0.99.8

• Submitted to Bioconductor

## ISAnalytics

             Changes in version 1.1.11 (2021-05-11)


NEW FUNCTIONALITY

• HSC_population_size_estimate and HSC_population_plot allow estimates on hematopoietic stem cell population size
• Importing of Vispa2 stats per pool now has a dedicated function, import_Vispa2_stats
• outlier_filter and outliers_by_pool_fragments offer a mean to filter poorly represented samples based on custom outliers tests

VISIBLE USER CHANGES

• The argument import_stats of aggregate_metadata is officially deprecated in favor of import_Vispa2_stats
• aggregate_metadata is now a lot more flexible on what operations can be performed on columns via the new argument aggregating_functions
• import_association_file allows directly for the import of Vispa2 stats and converts time points to months and years where not already present
• File system alignment of import_association_file now produces 3 separate columns for paths
• separate_quant_matrices and comparison_matrix now do not require mandatory columns other than the quantifications - this allows for separation or joining also for aggregated matrices

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

• Fixed issue in compute_near_integrations: when provided recalibration map export path as a folder now the function works correctly and produces an automatically generated file name
• Fixed issue in aggregate_metadata: now paths to folder that contains Vispa2 stats is looked up correctly. Also, VISPA2 stats columns are aggregated if found in the input data frame independently from the parameter import_stats.

IMPROVEMENTS

• compute_abundance can now take as input aggregated matrices and has additional parameters to offer more flexibility to the user. Major updates and improvements also on documentation and reproducible examples.
• Major improvements in function import_single_Vispa2Matrix: import is now preferentially carried out using data.table::fread greatly speeding up the process - where not possible readr::read_delim is used instead
• Major improvements in function import_association_file: greatly improved parsing precision (each column has a dedicated type), import report now signals parsing problems and their location and signals also problems in parsing dates. Report also includes potential problems in column names and signals missing data in important columns. Added also the possibility to give various file formats in input including *.xls(x) formats.
• Function top_integrations can now take additional parameters to compute top n genes for each specified group
• Removed faceting parameters in CIS_volcano_plot due to poor precision (easier to add faceting manually) and added parameters to return the data frame that generated the plot as an additional result. Also, it is now possible to specify a vector of gene names to highlight even if they’re not above the annotation threshold.

MINOR

• ISAnalytics website has improved graphic theme and has an additional button on the right that leads to the devel (or release) version of the website
• Updated vignettes

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)


• It is now possible to save html reports to file from import_parallel_Vispa2Matrices_auto and import_parallel_Vispa2Matrices_interactive, remove_collisions and compute_near_integrations

FIXES

• Fixed sample_statistics: now functions that have data frame output do not produce nested tables. Flat tables are ready to be saved to file or can be nested.
• 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)


• Upgraded import_association_file function: now file alignment is not mandatory anymore and it is possible to save the html report to file
• Updated vignettes and documentation

           Changes in version 1.1.4 (2020-11-16)


• Greatly improved reports for collision removal function
• General improvements for all widget reports

           Changes in version 1.1.3 (2020-11-10)


FIXES

• Further fixes for printing reports when widgets not available
• Added progress bar to collision processing in remove_collisions
• Updated vignettes

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

• Functions that make use of widgets do not interrupt execution anymore if errors are thrown while producing or printing the widgets
• Optimized widget printing for importing functions
• If widgets can’t be printed and verbose option is active, reports are now displayed on console instead (needed for usage in environments that do not have access to a browser)
• Other minor fixes (typos)
• Bug fixes: fixed a few bugs in importing and recalibration functions
• Minor fix in import_association_file file function: added multiple strings to be translated as NA

IMPORTANT NOTES

• Vignette building might fail due to the fact that package “knitcitations” is temporarily unavailable through CRAN
• ISAnalytics is finally in release on bioconductor!

## iSEE

                   Changes in version 2.3.14

• Allow modification of font sizes for row and column names in ComplexHeatmapPlot.
• Bugfix for assignment of annotation colors in ComplexHeatmapPlot.

                 Changes in version 2.3.13

• Avoid partial name matching in .getCachedCommonInfo.
• 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

• Document and export the .getDotPlotColorHelp utility.
• Bugfix for the RowDotPlot color tour.

                  Changes in version 2.3.9

• Add a distributed tour attached to each individual UI element.
• 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  • Added functionality to use multiple row/column selections as a factor on the axes, for faceting or for coloring. • Moved selection transparency setter into the “Visual parameters” box. • Deprecated SelectionEffect=”Color” in favor of ColorBy=”Column selection” and ColorBy=”Row selection”. • Deprecated SelectionColor as the coloring for selections is determined using colDataColorMap() instead. • Deprecated SelectionEffect=”Restrict” in favor of ColumnSelectionRestrict and RowSelectionRestrict. • 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  • Bugfix to the heatmap color selection for near-zero length ranges. ## iSEEu  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  • Added the MarkdownBoard panel to show arbitrary Markdown-formatted content. • Eliminate duplicates in available fields, as these break selectizes. ## IsoformSwitchAnalyzeR  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. ## isomiRs  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 ## kebabs  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 ## KnowSeq  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 ## limma  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 • The average log-expression column written by write.fit() now has column heading “AveExpr” to match the output from topTable(). The column was previously called “A”. 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

• Fix typo in voom() warning when negative counts are detected.

## LoomExperiment

                   Changes in version 1.10.0


BUG FIXES

• (v 1.9.1) use BiocIO rather than rtracklayer for import(), export(), and LoomFile() definitions.

## LRcell

                   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

• Debug test-LRcell.R and fix the problem due to the data uploaded
• Add PBMC datasets information in utils.R
• Edit description

                 Changes in version 0.99.3

• change both dependency back to R>=4.1
• R>=3.6 generates a warning

                 Changes in version 0.99.2

• change LRcellTypeMarkers dependency to R>=3.6
• change LRcell dependency to R>=3.6

                 Changes in version 0.99.1

• remove the LRcell.Rproj
• change the .gitignore file

                 Changes in version 0.99.0

• version 0.99.0 released
• Submitted to Bioconductor

## Maaslin2

                    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

## maftools

                    Changes in version 2.8.0


NEW FUNCTIONS

• cancerhotspots Genotype known cancer hotspots from the tumor BAM file
• bamreadcounts extract nucleotide counts for targeted variants from the BAM file.
• maftools now natively loads TCGA cohorts. tcgaAvailable and tcgaLoad will display and load the desired cohorts.
• Added maf2mae for converting MAF to MultiAssayExperiment class objects Issue: 640 293 Discussion: 285

ENHANCEMENTS

• Added protein domains for the gene ALMS1. Issue: 705
• Added titv_col argumtn to oncoplot. Issue: 702
• Added protein domains for the gene FAM205A. Issue: 701
• oncoplot can now summarize variant_classifications similar to cBioPortal style. Issue: 686
• Added pathway support for mafCompare() or clinicalEnrichment(). Issue: 681
• Added default title for side and topbar plots to oncoplot. Issue: 682
• Added annotationOrder argument to coOncoplot. Issue: 676
• Added plot argument to survGroup. Thank you OmarElAshkar PR: 674
• Added path_order argument to oncoplot for custom ordering of pathways on oncoplot.
• Added geneMar argument to coBarplot. Issue: 260

BUG FIXES

• coOncoplot not allowing more than one additional feature. Issue: 675

## marr

                    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.

## MatrixGenerics

                    Changes in version 1.2.1

• Sync API with matrixStats v0.58.0.

## MatrixQCvis

            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)


• 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)

• reduce package dependencies
• remove magick
• use stats::cmdscale instead of ape::pcoa
• remove MsCoreUtils
• remove preprocessCore
• remove Matrix
• 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)

• shinyQC including visualizations/functionality for
• histogram of sample types,
• information on number of missing/measured values
• information on (intersecting, disjoint) sets for missing/measured values
• barplot and violin plot for (count/intensity) values
• visualization to detect drifts/trends in (count/intensity) values
• coefficients of variation for samples,
• mean-sd plots,
• MA plots,
• empirical cumulative distribution function,
• visualizations of distances between samples,
• intensities of features and coefficients of variation of features,
• dimension reduction plots (PCA, PCoA, NMDS, tSNE, UMAP)
• differential expression
• 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)

• load different UI if the SummarizedExperiment is loaded on start of shinyQC or not

## matter

             Changes in version 1.17.1 (2020-11-27)


BUG FIXES

• Fix ‘apply()’ signatures for R 4.1

                    Changes in version 1.1.5


NEW FEATURES

• Added process_junction_table (an R-implementation of https://github.com/ChristopherWilks/megadepth#junctions) which parses the output of read_junction_table into a STAR-compatible format.

## memes

                   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

• added list S3 method for plot_sequence_heatmap so now named lists of sequences can be passed natively to this function.
• updated ChIP-seq vignette to demonstrate this

                 Changes in version 0.99.5

• added plot_sequence_heatmap for making heatmaps of sequence lists
• Added significantly more explanation to the ChIP-seq vignette
• renamed ame_plot_heatmap -> plot_ame_heatmap for consistency

                  Changes in version 0.1.2

• runFimo() skip_matched_sequence default is now FALSE. Set this to TRUE if fimo takes a long time to run, then use add_sequence() to add it back if needed.
• runTomTom() dist default is now ed (changed from pearson).

                  Changes in version 0.1.0

• Removed as_universalmotif_df(), as_universalmotif(), and update_motifs().
• These functions are replaced by universalmotif::to_df(), universalmotif::to_list(), and universalmotif::update_motifs()
• runDreme and runTomTom results are now returned in universalmotif_df format (behaves just like a data.frame)

## SeqGate

             Changes in version 1.1.1 (2021-01-22)

• Updated vignette and documentation to fix typos

## SeqGSEA

             Changes in version 1.31.1 (2020-11-30)

• Depends on DESeq2 (previously DESeq)

## SEtools

             Changes in version 1.5.3 (2021-04-19)

• plotting functions moved to the sechm’ package

## SharedObject

                    Changes in version 1.5.0

• NEW FEATURES:
• Support sharing character vectors
• Support creating empty shared objects
• new paramters shareAttributes and minLength in package options
• new paramter depth in function is.shared
• R level developer API supports vector input
• add a safer memory check method before sharing an object on Linux
• Chinese vignette
• CHANGES:
• The package will not provide C APIs in this version
• The C++ APIs are not compatible with the old version
• is.shared function reports the sharing status of object attributes by appending “Shared” to the end of attribute names.

## shinyepico

             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

• (v 1.49.3) report invalid FastqStreamer / FastqSampler on re-serialized objects. Fixes https://github.com/Bioconductor/ShortRead/issues/5.

## signatureSearch

             Changes in version 1.5.3 (2021-04-12)

• Created cellNtestPlot function to visualize number of compounds tested in cell types along with primary site information
• Added get_treat_info function to get the treatment information in reference database including pert, cell, pert_type columns.
• 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)

• Supported 3 enrichment methods in TSEA on Reactome pathway

## SimFFPE

             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

## simplifyEnrichment

                    Changes in version 1.1.4

• add export_to_shiny_app()

• add simplifyGOFromMultipleLists()

                  Changes in version 1.1.2

• add anno_word_cloud() function

## SingleCellExperiment

                   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.)

## singleCellTK

             Changes in version 2.1.2 (2021-05-13)

• Added diffAbundanceFET and plotClusterAbundance function

• 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

## SingleR

                    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

• First release

## sitePath

                    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’.

## slingshot

                    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.

## Spaniel

                    Changes in version 1.5.0


New Features

• Import options for 10X Visium data
• Updated vignette for 10X Visium data

Bug Fixes

• Corrected dependency issue that was causing build to fail

## SpatialExperiment

            Changes in version 1.1.700 (2021-05-05)

• updating roles in DESCRIPTION file

• 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)


• 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

## spatialHeatmap

             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.

• 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.

## Spectra

                     Changes in version 1.1


Changes in 1.1.20

• Fix concatenating empty spectra (issue #200).

Changes in 1.1.19

• New filterPrecursorCharge() method.

Changes in 1.1.18

• Define plotSpectraMirror as a method.

Changes in 1.1.17

• Fix issue #187.
• Add function concatenateSpectra to allow concatenating Spectra objects and list of Spectra objects.

Changes in 1.1.16

• Support arbitrary spectra variables to be passed to the functions provided/added with addProcessing; issue #182.

Changes in 1.1.15

• Pass spectras’ precursor m/z to the MAPFUN in compareSpectra; issue #171.
• Add joinPeaksGnps to perform a peak matching between spectra similar to the one performed in GNPS (issue #171).

Changes in 1.1.14

• Support plotting of empty spectra (issue 175).

Changes in 1.1.13

• Move ProcessingStep to ProtGenerics.

Changes in 1.1.12

• Fix show method for Spectra to list only the 3 most recent processing steps (issue 173).
• Add processingLog function to display the log messages of all processing steps of a Spectra object.

Changes in 1.1.11

• Add support for … to pickPeaks and smooth (issue 168).

Changes in 1.1.10

• Import filterIntensity from ProtGenerics.

Changes in 1.1.9

• Fix label in plotSpectra.

Changes in 1.1.8

• filterIntensity supports passing of additional parameters to the used filter function (issue 164).

Changes in 1.1.7

• Fix bug in show,ProcessingStep (issue 162).

Changes in 1.1.6

Changes in 1.1.5

• Add [[,Msbackend and [[<-,MsBackend methods (issue 149).
• Add [[,Spectra and [[<-,Spectra methods.

Changes in 1.1.4

• Fix issue with labelCol in plotSpectra (issue #157).

Changes in 1.1.3

• Implement a generic Spectra,ANY constructor replacing Spectra,DataFrame and Spectra,character.

Changes in 1.1.2

• Fix problem in export to mzML files that failed for empty spectra (issue #145)

Changes in 1.1.1

• Round retention time in figure titles.
• Document differences between spectrumId (spectrumID), acquisitionNum and scanIndex.

Changes in 1.1.0

• New Bioc devel version

             Changes in version 1.7.1 (2020-11-22)

• Fix bedpe output for Juicebox

## splatter

             Changes in version 1.16.0 (2020-05-20)

• Substantial updates to the splatPop simulation (from Christina Azodi)

• 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

## SplicingFactory

                   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


                 Changes in version 0.99.13

• SummarizedExperiment input for calculate_difference
• Changed SE_assay parameter name to assayno

                 Changes in version 0.99.12


                 Changes in version 0.99.11

• SummarizedExperiment corrections

                 Changes in version 0.99.10

• SummarizedExperiment output for calculate_diversity

                 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

• More flexible single calculate_entropy function instead of separate naive and Laplace entropy.
• P-value correction method can be set by user.
• Formatting corrections.

                 Changes in version 0.99.5

• Updated DESCRIPTION.
• Formatting corrections.
• Corrected bug in data description.
• Updated methods for obtaining class of input.
• Added verbose argument for functions.

                 Changes in version 0.99.4

• Code and documentation formatting corrections.

                 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

• Submitted to Bioconductor.

## statTarget

                   Changes in version 1.21.1

• To update for coCV output

## structToolbox

                    Changes in version 1.4.0


• add ellipse plotting options to pca_scores_plot

• mv_sample_filter can be used in train/predict mode

• 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

## SummarizedExperiment

             Changes in version 1.22.0


NEW FEATURES

• Add combineRows() and combineCols() methods for SummarizedExperiment objects and derivatives. These are more flexible versions of rbind() and cbind() that don’t require the objects to combine to have the same columns or rows. Contributed by Aaron Lun.

## Summix

             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

## supersigs

                   Changes in version 0.99.0

• Adhere to Bioconductor submission guidelines

## SWATH2stats

                   Changes in version 1.21.2


BUG FIXES

                 Changes in version 1.21.1


UPDATE

• update axis.text and theme_bw

                 Changes in version 1.21.0


NEW FEATURES

• SWATH2stats in BioC 3.13 development release

## SynExtend

                   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

• ExtractBy function has been added. Allows extraction of feature sequences into XStingSets organized by the a PairSummaries object or the single linkage clusters implied by pairings within the PairSummaries objects.
• 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

• PairSummaries now returns a column titled Adjacent that provides the number of directly adjacent neighbor pairs to a predicted pair. Gap filling code adjusted.
• 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

• Adjustments to progress bars in both PairSummaries and NucleotideOverlap.
• 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

• PairSummaries now translates sequences based on transl_table attributes provided by gene calls
• PairSummaries now uses a generic model for predicting PID
• gffToDataFrame now parses out the transl_table attribute

                  Changes in version 1.3.2

• Minor changes to NucleotideOverlap
• Major changes to PairSummaries - can now take in objects of class Genes build by the DECIPHER function FindGenes()

## systemPipeShiny

                   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

• Removed the scroll to top button by shinyDashboardPlus, we have our own “go top” button.

Bug fix

• Fix a bug when that loads the server twice
• Fix some default option values
• Fix a bug on addResourcePath when the working directory and app directory is not the same.
• Fix links not working caused by website change
• Fix code in spsInit overwrite all current SPS options.
• Fix errors on admin page when server stats cannot be found, better text and warning messages
• Fix new version of essquise introduced errors
• Fix a warning in vroom due to the column type problem

                 Changes in version 1.1.35


Major change

• Users can choose whether to enable the login or not in global.RSPS options.

• Website updated. https://systempipe.org/sps

• 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

• Addtarget=”_blank” to all external links in the app, so when they are clicked, it will open in a new tab.

Bug fix

• 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.

• 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

• 3 default modules complete: workflow, RNAseq,quick ggplot. Details of these modules updated in our website:

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

• updated all unit test to testthat v3 format.

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

• Now workflow module R session uses a background child R process, which runs independently to the parent R session which runs shiny.
• So the shiny will be not blocked while code is running in the background (you can still click other buttons when the child session is busy) – synchronous and non-blocking. A child indicator is also placed in the UI, updates every second.
• The UI design of R session is similar to Rstudio. Four panels, source code, console, log (specific to SPR), and plots.
• Standard out/error and plots are captured in the workflow folder. Users can download them in the bundle on step 5 Workflow Run.
• Plots will be displayed on the plots panel. Now supports plots that opens R device (base and ggplot), html widget plots are not supported as this moment.
• A new shiny disconnection popup for SPS. Besides the gray layer on shiny disconnection, a panel will be displayed to users to indicate the problem. Similar to what shows on a shiny server, but more informative and also works locally.
• Results of this session can be downloaded by closing the session and go back to step 5 of workflow module and there is a button to download all in a zipped file.

RNAseq module

• redesigned the UI and server logic. Plots for DEG analysis and Canvas connections.
• {SummarizedExperiment} supports. Now it returns SummarizedExperiment objects to global environment once the normalization or DEG calculation is done.

General UI

• Added a “Go Top” button on the right bottom corner, clicking on this button will automatically scroll to the top of the page. This button only shows up when client has > 50px scroll height.

Workflow module CWL tab

• Now the CWL file and CWL input file can be be edited. The edits will be imported to CWL parser every one second. Now this is a very useful place to test or write new CWL scripts.
• Now this tab has a dynamically rendered dropdown panel which allows users to choose which column for the targets table to map to the variables in CWL input file.

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.

• 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.

## TargetSearch

                   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.

## TCC

                   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.

## TCGAutils

                   Changes in version 1.12.0


New features

• makeSummarizedExperimentFromGISTIC has been moved to RTCGAToolbox.
• splitAssays now deprecated for TCGAsplitAssays to avoid conflict with MultiAssayExperiment::splitAssays

Minor changes and bug fixes

• Properly identifies genome annotation (hg*) in oncoPrintTCGA
• qreduceTCGA now works with updates to seqlevelsStyle where genome annotation include patch versions when available

## ternarynet

                   Changes in version 1.35.1

• added replica exchange MCMC parallel fitting algorithm

## TFutils

                   Changes in version 1.11.1

• retrieve_lambert_main has revised URL for Cell supplemental xlsx file

## TOAST

                    Changes in version 1.5.1

• Correct F-test for multiple level testing.

## topdownr

                    Changes in version 1.13

• New version for Bioc 3.13 (devel)

Changes in version 1.13.1

• as(…, “NCBSet”) now treats neutral losses and modifications as bonds as well.
• readTopDownFiles gains a new argument customModifications to allow user-defined modifications. Suggestion and first implementation by Maša Babović masab@bmb.sdu.dk [2021-03-15].

## ToxicoGx

                    Changes in version 1.1.0

• Continue to abstract functionality into CoreGx
• Extend coverage of unit tests to >90%
• Implement a faster version of drugPertubationSignature
• Include scripts for differential expression analysis and GSEA of toxico-genomic data (limma)

## trackViewer

                   Changes in version 1.27.15

• Update documentation of geneModelFromTxdb

                 Changes in version 1.27.14


• update importScSeqScore.

                 Changes in version 1.27.13

• Fix the size by number when read from file.

                 Changes in version 1.27.12


                 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


                 Changes in version 1.27.4

• change the re-sample method for viewTracks

                 Changes in version 1.27.3

• Update documentation.

                 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)

• Major update on associationTest, where the contrasts no longer rely on the knots but rather rely on a new nPoints argument, that specifies the number of points to use per lineage in the contrast. The associationTest also has a new argument contrastType that allows to use three different contrast types to do the test. See the docs on associationTest for more details.

## TrajectoryGeometry

                   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

• Formatting of SingleCellTrajectoryAnalysis.Rmd improved

## transomics2cytoscape

                    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

## TraRe

                   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


• vignette file updated

                 Changes in version 0.99.2

• rewiring method part parallelized

                 Changes in version 0.99.1


• minor bugs fixed

                 Changes in version 0.99.0

• automatic modules selection in rewiring test.

• rewiring html report file added.

## Travel

             Changes in version 0.99.0 (2020-12-30)

• Submitted to Bioconductor

## treeio

                   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

• read.beast.newick and write.beast.newick for importing and exporting newick text with metadata in BEAST style (2021-03-11, Thu)
• https://github.com/YuLab-SMU/treeio/pull/50

                 Changes in version 1.15.4

• support parsing tree qza file from qiime2 (2020-03-01, Mon)
• https://github.com/YuLab-SMU/treeio/pull/46/files

                 Changes in version 1.15.3

• support parsing phyloxml (2021-02-04, Thu)
• https://github.com/YuLab-SMU/treeio/pull/44

                 Changes in version 1.15.2

• bug fixed of parsing nhx, now compatible with missing nhx tag (2020-11-19, Thu)
• https://github.com/YuLab-SMU/treeio/pull/40

                 Changes in version 1.15.1

• remove magrittr::%<>% as it throw error of ‘Error: could not find function “%>%<-“’ (2020-11-19, Thu)

## treekoR

                   Changes in version 0.99.3

• First version of package submitted to BioConductor

## tricycle

                    Changes in version 0.99

• Initial release.

• Added preprint to CITATION, vignette.

## TSCAN

                   Changes in version 1.30.0

• Migrated createClusterMST() to the TrajectoryUtils package.

• Handle pseudotime matrices in testPseudotime() by testing each path separately. Support inclusion of custom row.data= in each output DataFrame.

## ttgsea

             Changes in version 0.99.0 (2020-09-30)

• submission to Bioconductor

## tximeta

                   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 around AnnotationHub pulldown when using RefSeq as the source.
• 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

• Added hashes for GENCODE 36 and Ensembl 102.

## tximport

                   Changes in version 1.19.4

• ‘ignoreAfterBar’ and txOut=TRUE will now strip the characters after ‘|’ on the rownames of the output matrices.

## UMI4Cats

                    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)

## universalmotif

                   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.

## VarCon

             Changes in version 0.99.0 (2018-05-15)

• Submitted to Bioconductor

## VaSP

             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.

## velociraptor

                    Changes in version 1.1.6

• Move sanity check vignette to inst/.

                  Changes in version 1.1.5


                  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.

## vissE

                   Changes in version 0.99.0

• submitted to bioconductor

## wppi

             Changes in version 0.99.8 (2021-05-07)

• The workflow calculates Protein-Protein Interaction weights and scores genes
• Database knowledge is automatically fetched from OmniPath, Gene Ontology and Human Phenotype Ontology
• Submitted to Bioconductor

## xcms

                   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.

• 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.

## YAPSA

                   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.

## zellkonverter

                    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()

• Export zellkonverterAnnDataEnv for use by other packages

# NEWS from new and existing Data Experiment Packages

## BioImageDbs

                   Changes in version 0.99.3


NEW FEATURES

• Package released

## chipenrich.data

                   Changes in version 2.16.0

• Transition to Kai Wang as maintainer.

## curatedMetagenomicData

                    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):

• AsnicarF_2021 (1098 samples)
• BrooksB_2017 (408 samples)
• ChuDM_2017 (86 samples)
• DeFilippisF_2019 (97 samples)
• GhensiP_2019 (113 samples)
• GuptaA_2019 (60 samples)
• HallAB_2017 (259 samples)
• HMP_2019_ibdmdb (1628 samples)
• HMP_2019_t2d (296 samples)
• IjazUZ_2017 (94 samples)
• KaurK_2020 (31 samples)
• KeohaneDM_2020 (117 samples)
• LassalleF_2017 (23 samples)
• LifeLinesDeep_2016 (1135 samples)
• LokmerA_2019 (57 samples)
• MehtaRS_2018 (928 samples)
• NagySzakalD_2017 (100 samples)
• PasolliE_2019 (112 samples)
• RosaBA_2018 (24 samples)
• RubelMA_2020 (175 samples)
• SankaranarayananK_2015 (37 samples)
• ShaoY_2019 (1644 samples)
• ThomasAM_2019_c (80 samples)
• VilaAV_2018 (355 samples)
• WampachL_2018 (63 samples)
• WirbelJ_2018 (125 samples)
• YachidaS_2019 (616 samples)
• YassourM_2016 (36 samples)
• YassourM_2018 (271 samples)
• ZhuF_2020 (171 samples)
• All raw data has been reprocessed with MetaPhlAn3 & HUMAnN3
• The curatedMetagenomicData() method has been refactored for efficiency

• It now returns SummarizedExperiment/TreeSummarizedExperiment objects
• Sample metadata always stays up to date and is updated weekly
• It is now the primary (and only) means to access data
• The mergeData() method has been refactored for accuracy and efficiency
• The returnSamples() method has been added for returns across studies
• The sampleMetadata object replaces the combined_metadata object
• The combined_metadata object will be removed in the next release
• A 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

• These were very hard to maintain and document; the package is now simpler
• The 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

• mRNAArray assays now return matrix type data instead of DataFrame (#31, @lgeistlinger, @vjcitn).

## depmap

                  Changes in version 1.5.1

• 20Q4 data added for crispr, copyNumber, TPM, mutationCalls and metadata datasets. Newer versions for the other datasets were not released.

                   Changes in version 0.99.0


## dorothea

             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.

## emtdata

                   Changes in version 0.99.0

• submitted to bioconductor

## GSE13015

                   Changes in version 0.99.11

• submission to Bioconductor

                    Changes in version 1.8.0


Other notes

• The functions to load the data gain a as.sparse parameter, to control whether the underlying HDF5 dataset should be treated as sparse or not.

## imcdatasets

             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

• Formatted the package for Bioconductor submission

           Changes in version 0.1.0 (2020-11-02)

• Initial commit

• Creation of the imcdatasets package

## LRcellTypeMarkers

                   Changes in version 0.99.3


• 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

## microbiomeDataSets

             Changes in version 0.99.0 (2021-01-21)

• Submitted to Bioconductor

## MouseThymusAgeing

             Changes in version 0.99.5 (2021-04-21)

• Submitted to Bioconductor

## msigdb

                   Changes in version 0.99.0

• submitted to bioconductor

## ptairData

             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 section installation and sessionInfo to the vignette

           Changes in version 0.99.0 (2021-03-05)

• Submitted to Bioconductor

## RforProteomics

                   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


## SBGNview.data

                    Changes in version 1.5.1

• Major updates of SBGNview.data, by Kovidh Vegesna and Weijun Luo.

## scpdata

                    Changes in version 0.99.3

• Updated documentation <2020-05-03>
• 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

• Bioconductor submission <2020-01-06>

## scRNAseq

                    Changes in version 2.6.0

• Added the Bacher T cell dataset.

• Added the Darmanis brain dataset.

• Added the Ernst spermatogenesis dataset.

• Added the Fletcher olfactory 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).

## SimBenchData

             Changes in version 0.99.1 (2021-03-05)

• Submitted to Bioconductor

## SingleCellMultiModal

                    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

## spatialLIBD

                   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.

## STexampleData

             Changes in version 0.99.0 (2021-03-28)

• Initial submission to Bioconductor

## TENxVisiumData

                   Changes in version 0.99.0

• initial package submission of 13 Visium spatial gene expression datasets by 10X Genomics

## TMExplorer

                    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


# NEWS from new and existing Workflows

## ExpHunterSuite

                   Changes in version 0.99.11
`
• Improved documentation and code style <2021-02-21, Sun>

# Deprecated and Defunct Packages

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