Oct 31, 2018
Bioconductors:
We are pleased to announce Bioconductor 3.8, consisting of 1649 software packages, 360 experiment data packages, 941 annotation packages, and 23 workflows.
There are 95 new software packages, 21 new data experiment packages, 2 new workflows, and many updates and improvements to existing packages; Bioconductor 3.8 is compatible with R 3.5.0, and is supported on Linux, 32- and 64-bit Windows, and Mac OS X. This release will include an updated Bioconductor Amazon Machine Image and Docker containers.
Visit https://bioconductor.org for details and downloads.
To update to or install Bioconductor 3.8:
Install R >=3.5.0. Bioconductor 3.8 has been designed expressly for this version of R.
Follow the instructions at http://bioconductor.org/install/.
There are 95 new software packages in this release of Bioconductor.
abseqR AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. abseqR empowers the users of abseqPy (https://github.com/malhamdoosh/abseqPy) with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output.
ACE Uses segmented copy number data to estimate tumor cell percentage and produce copy number plots displaying absolute copy numbers.
AffiXcan Impute a GReX (Genetically Regulated Expression) for a set of genes in a sample of individuals, using a method based on the Total Binding Affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known.
appreci8R The appreci8R is an R version of our appreci8-algorithm - A Pipeline for PREcise variant Calling Integrating 8 tools. Variant calling results of our standard appreci8-tools (GATK, Platypus, VarScan, FreeBayes, LoFreq, SNVer, samtools and VarDict), as well as up to 5 additional tools is combined, evaluated and filtered.
artMS artMS provides a set of tools for the analysis of proteomics label-free datasets. It takes as input the MaxQuant search result output (evidence.txt file) and performs quality control, relative quantification using MSstats, downstream analysis and integration. artMS also provides a set of functions to re-format and make it compatible with other analytical tools, including, SAINTq, SAINTexpress, Phosfate, and PHOTON.
AssessORF In order to assess the quality of a set of predicted genes for a genome, evidence must first be mapped to that genome. Next, each gene must be categorized based on how strong the evidence is for or against that gene. The AssessORF package provides the functions and class structures necessary for accomplishing those tasks, using proteomic hits and evolutionarily conserved start codons as the forms of evidence.
bayNorm bayNorm is used for normalizing single-cell RNA-seq data.
BDMMAcorrect Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch effects do not consider the interactions between variables—microbial taxa in microbial studies—and the overdispersion of the microbiome data. Therefore, they are not applicable to microbiome data. We develop a new method, Bayesian Dirichlet-multinomial regression meta-analysis (BDMMA), to simultaneously model the batch effects and detect the microbial taxa associated with phenotypes. BDMMA automatically models the dependence among microbial taxa and is robust to the high dimensionality of the microbiome and their association sparsity.
BiocNeighbors Implements exact and approximate methods for nearest neighbor detection, in a framework that allows them to be easily switched within Bioconductor packages or workflows. The exact algorithm is implemented using pre-clustering with the k-means algorithm, as described by Wang (2012). This is faster than conventional kd-trees for neighbor searching in higher (> 20) dimensional data. The approximate method uses the Annoy algorithm. Functions are also provided to search for all neighbors within a given distance. Parallelization is achieved for all methods using the BiocParallel framework.
BiocPkgTools Bioconductor has a rich ecosystem of metadata around packages, usage, and build status. This package is a simple collection of functions to access that metadata from R. The goal is to expose metadata for data mining and value-added functionality such as package searching, text mining, and analytics on packages.
brainImageR BrainImageR is a package that provides the user with information of where in the human brain their gene set corresponds to. This is provided both as a continuous variable and as a easily-interpretable image. BrainImageR has additional functionality of identifying approximately when in developmental time that a gene expression dataset corresponds to. Both the spatial gene set enrichment and the developmental time point prediction are assessed in comparison to the Allen Brain Atlas reference data.
breakpointR This package implements functions for finding breakpoints, plotting and export of Strand-seq data.
BUScorrect High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed “batch effects,” and the latter is often modelled by “subtypes.” The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity.
CAMTHC An R package for tissue heterogeneity characterization by convex analysis of mixtures (CAM). It provides basic functions to perform unsupervised deconvolution on mixture expression profiles by CAM and some auxiliary functions to help understand the subpopulation-specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures.
celaref After the clustering step of a single-cell RNAseq experiment, this package aims to suggest labels/cell types for the clusters, on the basis of similarity to a reference dataset. It requires a table of read counts per cell per gene, and a list of the cells belonging to each of the clusters, (for both test and reference data).
CellTrails CellTrails is an unsupervised algorithm for the de novo chronological ordering, visualization and analysis of single-cell expression data. CellTrails makes use of a geometrically motivated concept of lower-dimensional manifold learning, which exhibits a multitude of virtues that counteract intrinsic noise of single cell data caused by drop-outs, technical variance, and redundancy of predictive variables. CellTrails enables the reconstruction of branching trajectories and provides an intuitive graphical representation of expression patterns along all branches simultaneously. It allows the user to define and infer the expression dynamics of individual and multiple pathways towards distinct phenotypes.
cicero Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data.
COCOA COCOA is a method for understanding variation among samples and can be used with data that includes genomic coordinates such as DNA methylation. On a high level, COCOA uses a database of “region sets” and principal component analysis (PCA) of your data to identify sources of variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance, transcription factor binding regions, histone modification regions, or open chromatin regions. COCOA works in both supervised (known groups of samples) and unsupervised (no groups) situations and can be used as a complement to “differential” methods that find discrete differences between groups. COCOA can identify biologically meaningful sources of variation between samples and increase understanding of variation in your data.
compartmap Compartmap performs shrunken A/B compartment inference from ATAC-seq and methylation arrays.
condcomp For a given clustered data, which can also be split into two conditions, this package provides a way to perform a condition comparison on said clustered data. The comparison is performed on each cluster. Several statistics are used and, when analysed in conjunction, they might give some insight regarding the heterogeneity of some of the clusters.
consensus An implementation of the American Society for Testing and Materials (ASTM) Standard E691 for interlaboratory testing procedures, designed for cross-platform genomic measurements. Given three (3) or more genomic platforms or laboratory protocols, this package provides interlaboratory testing procedures giving per-locus comparisons for sensitivity and precision between platforms.
consensusDE This package allows users to perform DE analysis using multiple algorithms. It seeks consensus from multiple methods. Currently it supports “Voom”, “EdgeR” and “DESeq”, but can be easily extended. It uses RUV-seq (optional) to remove batch effects.
coRdon Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results.
countsimQC countsimQC provides functionality to create a comprehensive report comparing a broad range of characteristics across a collection of count matrices. One important use case is the comparison of one or more synthetic count matrices to a real count matrix, possibly the one underlying the simulations. However, any collection of count matrices can be compared.
cTRAP Compare differential gene expression results with those from known cellular perturbations (such as gene knock-down, overexpression or small molecules) derived from the Connectivity Map. Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations.
DEqMS DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data.
EnhancedVolcano Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many transcript names in the plot window as possible, thus avoiding ‘clogging’ up the plot with labels that could not otherwise have been read.
ERSSA The ERSSA package takes user supplied RNA-seq differential expression dataset and calculates the number of differentially expressed genes at varying biological replicate levels. This allows the user to determine, without relying on any a priori assumptions, whether sufficient differential detection has been acheived with their RNA-seq dataset.
ExCluster ExCluster flattens Ensembl and GENCODE GTF files into GFF files, which are used to count reads per non-overlapping exon bin from BAM files. This read counting is done using the function featureCounts from the package Rsubread. Library sizes are normalized across all biological replicates, and ExCluster then compares two different conditions to detect signifcantly differentially spliced genes. This process requires at least two independent biological repliates per condition, and ExCluster accepts only exactly two conditions at a time. ExCluster ultimately produces false discovery rates (FDRs) per gene, which are used to detect significance. Exon log2 fold change (log2FC) means and variances may be plotted for each significantly differentially spliced gene, which helps scientists develop hypothesis and target differential splicing events for RT-qPCR validation in the wet lab.
FastqCleaner An interactive web application for quality control, filtering and trimming of FASTQ files. This user-friendly tool combines a pipeline for data processing based on Biostrings and ShortRead infrastructure, with a cutting-edge visual environment. Single-Read and Paired-End files can be locally processed. Diagnostic interactive plots (CG content, per-base sequence quality, etc.) are provided for both the input and output files.
FCBF This package provides a simple R implementation for the Fast Correlation Based Filter described in Yu, L. and Liu, H.; Feature Selection for High-Dimensional Data: A Fast Correlation Based Filter Solution,Proc. 20th Intl. Conf. Mach. Learn. (ICML-2003), Washington DC, 2003 The current package is an intent to make easier for bioinformaticians to use FCBF for feature selection, especially regarding transcriptomic data.This implies discretizing expression (function discretize_exprs) before calculating the features that explain the class, but are not predictable by other features. The functions are implemented based on the algorithm of Yu and Liu, 2003 and Rajarshi Guha’s implementation from 13/05/2005 available (as of 26/08/2018) at http://www.rguha.net/code/R/fcbf.R .
FoldGO FoldGO is a package designed to annotate gene sets derived from expression experiments and identify fold-change-specific GO terms.
GeneAccord A statistical framework to examine the combinations of clones that co-exist in tumors. More precisely, the algorithm finds pairs of genes that are mutated in the same tumor but in different clones, i.e. their subclonal mutation profiles are mutually exclusive. We refer to this as clonally exclusive. It means that the mutations occurred in different branches of the tumor phylogeny, indicating parallel evolution of the clones. Our statistical framework assesses whether a pattern of clonal exclusivity occurs more often than expected by chance alone across a cohort of patients. The required input data are the mutated gene-to-clone assignments from a cohort of cancer patients, which were obtained by running phylogenetic tree inference methods. Reconstructing the evolutionary history of a tumor and detecting the clones is challenging. For nondeterministic algorithms, repeated tree inference runs may lead to slightly different mutation-to-clone assignments. Therefore, our algorithm was designed to allow the input of multiple gene-to-clone assignments per patient. They may have been generated by repeatedly performing the tree inference, or by sampling from the posterior distribution of trees. The tree inference methods designate the mutations to individual clones. The mutations can then be mapped to genes or pathways. Hence our statistical framework can be applied on the gene level, or on the pathway level to detect clonally exclusive pairs of pathways. If a pair is significantly clonally exclusive, it points towards the fact that this specific clone configuration confers a selective advantage, possibly through synergies between the clones with these mutations.
GIGSEA We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal.
glmSparseNet glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely “gaussian”, “poisson”, “binomial”, “multinomial”, “cox”, and “mgaussian”.
gpart we provide a new SNP sequence partitioning method which partitions the whole SNP sequence based on not only LD block structures but also gene location information. The LD block construction for GPART is performed using Big-LD algorithm, with additional improvement from previous version reported in Kim et al.(2017). We also add a visualization tool to show the LD heatmap with the information of LD block boundaries and gene locations in the package.
gwasurvivr gwasurvivr is a package to perform survival analysis using Cox proportional hazard models on imputed genetic data.
HiCBricks A flexible framework for storing and accessing high-resolution Hi-C data through HDF files. HiCBricks allows import of Hi-C data through various formats such as the 2D matrix format or a generalized n-column table formats. In terms of access, HiCBricks offers functions to retrieve values from genomic loci separated by a certain distance, or the ability to fetch matrix subsets using word alike terms. HiCBricks will at a later point offer the ability to fetch multiple matrix subsets using fewer calls. It offers the capacity to store GenomicRanges that may be associated to a particular Hi-C experiment, to do basic ranges overlap (any, within) with the Hi-C experiment associated Ranges object and also to store any metadata that users may think to be relevant for their Hi-C experiment. Finally, you can do TAD calls with LSD and create pretty heatmaps.
hierinf Tools to perform hierarchical inference for one or multiple studies / data sets based on high-dimensional multivariate (generalised) linear models. A possible application is to perform hierarchical inference for GWA studies to find significant groups or single SNPs (if the signal is strong) in a data-driven and automated procedure. The method is based on an efficient hierarchical multiple testing correction and controls the FWER. The functions can easily be run in parallel.
HIREewas In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. We propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. The “HIREewas” R package is to implement HIRE model in R.
HPAanalyze Provide functions for retrieving, exploratory analyzing and visualizing the Human Protein Atlas data.
iasva Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors.
icetea icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5’-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis.
INDEED An Implementation of Integrated Differential Expression and Differential Network Analysis of Omic Data. The differential network is obtained based on partial correlation or correlation.
ipdDb All alleles from the IPD IMGT/HLA https://www.ebi.ac.uk/ipd/imgt/hla/ and IPD KIR https://www.ebi.ac.uk/ipd/kir/ database for Homo sapiens. Reference: Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P, De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA KIR Nomenclature in non-human species Immunogenetics (2018), in preparation.
IsoCorrectoR IsoCorrectoR is a tool for correcting natural isotope abundance contributions in tracing experiments.
KinSwingR KinSwingR integrates phosphosite data derived from mass-spectrometry data and kinase-substrate predictions to predict kinase activity. Several functions allow the user to build PWM models of kinase-subtrates, statistically infer PWM:substrate matches, and integrate these data to infer kinase activity.
levi The tool integrates data from biological networks with transcriptomes, displaying a heatmap with surface curves to evidence the altered regions.
LoomExperiment The LoomExperiment class provide a means to easily convert Bioconductor’s “Experiment” classes to loom files and vice versa.
LRBaseDbi Interface to construct LRBase package (LRBase.XXX.eg.db).
maser This package provides functionalities for analysis, annotation and visualizaton of alternative splicing events.
methylGSA The main functions for methylGSA are methylglm and methylRRA. methylGSA implements logistic regression adjusting number of probes as a covariate. methylRRA adjusts multiple p-values of each gene by Robust Rank Aggregation. For more detailed help information, please see the vignette.
MetID This package uses an innovative network-based approach that will enhance our ability to determine the identities of significant ions detected by LC-MS.
MetNet MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two matrices are combined to form a adjacency matrix inferred from both quantitative and structure information.
miRSM The package aims to identify miRNA sponge modules by integrating expression data and miRNA-target binding information. It provides several functions to study miRNA sponge modules, including popular methods for inferring gene modules (candidate miRNA sponge modules), and a function to identify miRNA sponge modules, as well as a function to conduct functional analysis of miRNA sponge modules.
mixOmics Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.
mlm4omics To conduct Bayesian inference regression for responses with multilevel explanatory variables and missing values; It uses function from ‘Stan’, a software to implement posterior sampling using Hamiltonian MC and its variation Non-U-Turn algorithms. It implements the posterior sampling of regression coefficients from the multilevel regression models. The package has two main functions to handle not-missing-at-random missing responses and left-censored with not-missing-at random responses. The purpose is to provide a similar format as the other R regression functions but using ‘Stan’ models.
MPRAnalyze MPRAnalyze provides statistical framework for the analysis of data generated by Massively Parallel Reporter Assays (MPRAs), used to directly measure enhancer activity. MPRAnalyze can be used for quantification of enhancer activity, classification of active enhancers and comparative analyses of enhancer activity between conditions. MPRAnalyze construct a nested pair of generalized linear models (GLMs) to relate the DNA and RNA observations, easily adjustable to various experimental designs and conditions, and provides a set of rigorous statistical testig schemes.
MSstatsTMT Tools for protein significance analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling.
MTseeker Variant analysis tools for mitochondrial genetics.
multiHiCcompare multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner.
NBSplice The package proposes a differential splicing evaluation method based on isoform quantification. It applies generalized linear models with negative binomial distribution to infer changes in isoform relative expression.
NeighborNet Identify the putative mechanism explaining the active interactions between genes in the investigated phenotype.
NormalyzerDE NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis.
nuCpos nuCpos, a derivative of NuPoP, is an R package for prediction of nucleosome positions. In nuCpos, a duration hidden Markov model is trained with a chemical map of nucleosomes either from budding yeast, fission yeast, or mouse embryonic stem cells. nuCpos outputs the Viterbi (most probable) path of nucleosome-linker states, predicted nucleosome occupancy scores and histone binding affinity (HBA) scores as NuPoP does. nuCpos can also calculate local and whole nucleosomal HBA scores for a given 147-bp sequence. Furthermore, effect of genetic alterations on nucleosome occupancy can be predicted with this package. The parental package NuPoP, which is based on an MNase-seq-based map of budding yeast nucleosomes, was developed by Ji-Ping Wang and Liqun Xi, licensed under GPL-2.
OMICsPCA OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it’s core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals.
onlineFDR This package allows users to control the false discovery rate for online hypothesis testing, where hypotheses arrive sequentially in a stream, as presented by Javanmard and Montanari (2015, 2018). In this framework, a null hypothesis is rejected based only on the previous decisions, as the future p-values and the number of hypotheses to be tested are unknown.
OUTRIDER Identification of aberrent gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Further OUTRIDER provides useful plotting function to analyze and visualize the results.
PepsNMR This package provides R functions for common pre-procssing steps that are applied on 1H-NMR data. It also provides a function to read the FID signals directly in the Bruker format.
plotGrouper A shiny app-based GUI wrapper for ggplot with built-in statistical analysis. Import data from file and use dropdown menus and checkboxes to specify the plotting variables, graph type, and look of your plots. Once created, plots can be saved independently or stored in a report that can be saved as a pdf. If new data are added to the file, the report can be refreshed to include new data. Statistical tests can be selected and added to the graphs. Analysis of flow cytometry data is especially integrated with plotGrouper. Count data can be transformed to return the absolute number of cells in a sample (this feature requires inclusion of the number of beads per sample and information about any dilution performed).
primirTSS A fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs.
ProteoMM ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics” (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in “Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition.” (Karpievitch et al. Bioinformatics 2009).
qPLEXanalyzer Tools for quantitative proteomics data analysis generated from qPLEX-RIME method.
QSutils Set of utility functions for viral quasispecies analysis with NGS data. Most functions are equally useful for metagenomic studies. There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data.
REBET There is an increasing focus to investigate the association between rare variants and diseases. The REBET package implements the subREgion-based BurdEn Test which is a powerful burden test that simultaneously identifies susceptibility loci and sub-regions.
Rmmquant RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way.
RNASeqR This R package is designed for case-control RNA-Seq analysis (two-group). There are six steps: “RNASeqRParam S4 Object Creation”, “Environment Setup”, “Quality Assessment”, “Reads Alignment & Quantification”, “Gene-level Differential Analyses” and “Functional Analyses”. Each step corresponds to a function in this package. After running functions in order, a basic RNASeq analysis would be done easily.
SCBN This package provides a scale based normalization (SCBN) method to identify genes with differential expression between different species. It takes into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to detect differentially expressed orthologous genes. The method on this package are described in the article ‘A statistical normalization method and differential expression analysis for RNA-seq data between different species’ by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication).
scruff A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics.
sesame Tools For analyzing Illumina Infinium DNA methylation arrays.
sigFeature This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy.
SIMD This package provides a inferential analysis method for detecting differentially expressed CpG sites in MeDIP-seq data. It uses statistical framework and EM algorithm, to identify differentially expressed CpG sites. The methods on this package are described in the article ‘Methylation-level Inferences and Detection of Differential Methylation with Medip-seq Data’ by Yan Zhou, Jiadi Zhu, Mingtao Zhao, Baoxue Zhang, Chunfu Jiang and Xiyan Yang (2018, pending publication).
slingshot Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction.
slinky Wrappers to query the L1000 metadata available via the clue.io REST API as well as helpers for dealing with LINCS gctx files, extracting data sets of interest, converting to SummarizedExperiment objects, and some facilities for performing streamlined differential expression analysis of these data sets.
sparsenetgls The package provides methods of combining the graph structure learning and generalized least squares regression to improve the regression estimation. The main function sparsenetgls() provides solutions for multivariate regression with Gaussian distributed dependant variables and explanatory variables utlizing multiple well-known graph structure learning approaches to estimating the precision matrix, and uses a penalized variance covariance matrix with a distance tuning parameter of the graph structure in deriving the sandwich estimators in generalized least squares (gls) regression. This package also provides functions for assessing a Gaussian graphical model which uses the penalized approach. It uses Receiver Operative Characteristics curve as a visualization tool in the assessment.
strandCheckR This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering.
TimeSeriesExperiment Visualization and analysis toolbox for short time course data which includes dimensionality reduction, clustering, two-sample differential expression testing and gene ranking techniques. The package also provides methods for retrieving enriched pathways.
transite transite is a computational method that allows comprehensive analysis of the regulatory role of RNA-binding proteins in various cellular processes by leveraging preexisting gene expression data and current knowledge of binding preferences of RNA-binding proteins.
tRNA The tRNA package allows tRNA sequences and structures to be accessed and used for subsetting. In addition, it provides visualization tools to compare feature parameters of multiple tRNA sets and correlate them to additional data. The tRNA package uses GRanges objects as inputs requiring only few additional column data sets.
tRNAdbImport tRNAdbImport imports the entries of the tRNAdb and mtRNAdb (http://trna.bioinf.uni-leipzig.de) as GRanges object.
tximeta Transcript quantification import from Salmon with automatic population of metadata and transcript ranges. Filtered, combined, or de novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for reproducible analyses.
Ularcirc Ularcirc reads in STAR aligned splice junction files and provides visualisation and analysis tools for splicing analysis. Users can assess backsplice junctions and forward canonical junctions.
universalmotif Allows for importing most common motif types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others.
Wrench Wrench is a package for normalization sparse genomic count data, like that arising from 16s metagenomic surveys.
XINA An intuitive R package simplifies network analyses output from multiplexed high-dimensional proteomics/trascriptomics kinetics data.
There are 21 new data experiment packages in this release of Bioconductor.
allenpvc Celular taxonomy of the primary visual cortex in adult mice based on single cell RNA-sequencing from a study performed by the Allen Institute for Brain Science. In said study 49 transcriptomic cell types are identified.
AssessORFData This package provides access to mapping and results objects generated by the AssessORF package, as well as the genome sequences for the strains corresponding to those objects.
brainImageRdata brainImageRdata contains image masks for the developing human and the adult human brain. These masks can be used in conjunction with the gene expression data to generate spatial gene set enrichment plots. It also contains the expression data for the 15 pcw human brain, the adult human brain, and the developing human brain.
breakpointRdata Strand-seq data to demonstrate functionalities of breakpointR package.
celarefData This experiment data contains some processed data used in the celaref package vignette. These are publically available datasets, that have been processed by celaref package, and can be manipulated further with it.
CopyNeutralIMA Provides a set of genomic copy neutral samples hybridized using Illumina Methylation arrays (450k and EPIC).
DuoClustering2018 Preprocessed experimental and simulated scRNA-seq data sets used for evaluation of clustering methods for scRNA-seq data in Duò et al (2018). Also contains results from applying several clustering methods to each of the data sets, and functions for plotting method performance.
FlowSorted.Blood.EPIC Raw data objects to be used for blood cell proportion estimation in minfi and similar packages. The FlowSorted.Blood.EPIC object is based in samples assayed by Brock Christensen and colleagues; for details see Salas et al. 2018. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110554.
GIGSEAdata The gene set collection used for the GIGSEA package.
mcsurvdata This package stores two merged expressionSet objects that contain the gene expression profile and clinical information of -a- six breast cancer cohorts and -b- four colorectal cancer cohorts. Breast cancer data are employed in the vignette of the hrunbiased package for survival analysis of gene signatures.
MSMB Data sets for the book ‘Modern Statistics for Modern Biology’, S.P. Holmes and W. Huber
MTseekerData Provides examples for the MTseeker package vignette.
OMICsPCAdata Supporting data for package OMICsPCA
PepsNMRData This package contains all the datasets used in the PepsNMR package.
qPLEXdata qPLEX-RIME and Full proteome TMT mass spectrometry datasets.
RegParallel In many analyses, a large amount of variables have to be tested independently against the trait/endpoint of interest, and also adjusted for covariates and confounding factors at the same time. The major bottleneck in these is the amount of time that it takes to complete these analyses. With RegParallel, a large number of tests can be performed simultaneously. On a 12-core system, 144 variables can be tested simultaneously, with 1000s of variables processed in a matter of seconds via ‘nested’ parallel processing. Works for logistic regression, linear regression, conditional logistic regression, Cox proportional hazards and survival models, Bayesian logistic regression, and negative binomial regression.
RNASeqRData RNASeqRData is a helper experiment package for vignette demonstration purpose in RNASeqR software package.
sesameData Provides supporting annotation and test data for SeSAMe package.
TabulaMurisData Access to processed 10x (droplet) and SmartSeq2 (on FACS-sorted cells) single-cell RNA-seq data from the Tabula Muris consortium (http://tabula-muris.ds.czbiohub.org/).
tcgaWGBSData.hg19 Data package for WGBS Data in TCGA. Data is stored as SummarizedExperiment Format. See vignette on how to extract the data and perform differential methylation analysis.
TENxPBMCData Single-cell RNA-seq data for on PBMC cells, generated by 10X Genomics.
There are 2 new workflow packages in this release of Bioconductor.
maEndToEnd In this article, we walk through an end-to-end Affymetrix microarray differential expression workflow using Bioconductor packages. This workflow is directly applicable to current “Gene” type arrays, e.g. the HuGene or MoGene arrays, but can easily be adapted to similar platforms. The data analyzed here is a typical clinical microarray data set that compares inflamed and non-inflamed colon tissue in two disease subtypes. For each disease, the differential gene expression between inflamed- and non-inflamed colon tissue was analyzed. We will start from the raw data CEL files, show how to import them into a Bioconductor ExpressionSet, perform quality control and normalization and finally differential gene expression (DE) analysis, followed by some enrichment analysis.
rnaseqDTU RNA-seq workflow for differential transcript usage (DTU) following Salmon quantification. This workflow uses Bioconductor packages tximport, DRIMSeq, and DEXSeq to perform a DTU analysis on simulated data. It also shows how to use stageR to perform two-stage testing of DTU, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU.
Changes in version 1.11.7:
USER-LEVEL CHANGES
simplify functions ‘get_expression’ and ‘plot_expression’ (remove option to automatically use data from last aba_enrich-call)
‘plot_expression’ now takes matrix from ‘get_expression’ as input, instead of calling ‘get_expression’ internally
add color key to ‘plot_expression’ heatmap when gene-associated variables are shown in a colored side bar
Changes in version 1.11.6:
USER-LEVEL CHANGES
Changes in version 1.11.3:
Changes in version 1.11.2:
USER-LEVEL CHANGES
Changes in version 1.11.1:
NEW FEATURES
Changes in version 0.99.0:
Changes in version 1.35.2 (2018-06-28):
Changes in version 0.99.8 (2018-09-06):
Changes in version 0.99.6 (2018-07-26):
Changes in version 0.99.4 (2018-06-26):
IMPROVEMENTS
changed linkmutationdata to linkvariants
linkvariants can estimate copies of heterozygous germline variants
linkvariants calculates the upper and lower bound of a given confidence level when read depth is given
changed postanalysisloop to accommodate linkvariants
added argument in runACE to specify genome build
several minor documentation amendments
improved coding robustness using seq and seq_along functions
Changes in version 0.99.0 (2018-05-24):
SUBMISSION
Changes in version 2.21.1 (2018-07-16):
Changes in version 1.53.2 (2018-10-22):
DOCUMENTATION
Link to Affx Fusion SDK archive on GitHub.
Spell corrections.
Changes in version 1.53.1 (2018-08-28):
Changes in version 1.53.0 (2018-04-30):
Changes in version 1.5.2:
USER VISIBLE CHANGES
Changes in version 1.11.0:
MODIFICATIONS
Removed scripts for Pazar DB as website no longer active
Update from BiocInstaller to BiocManager
NEW FEATURES
BUG FIX
Fix TwoBit resource receipe. Converts DNA that is not A,C,T,G,N to N do to design of rtracklayer::export for TwoBit
Fix bug with assignment of tags in annotationhub
makeEpigenomeRoadMap recipe updated to account for XML bug that cannot handle http urls. updated to https
Changes in version 1.3.1:
fixed a bug where in anota2seqPlotPvalues and anota2seqPlotFC the contrast names were not displayed correctly when selecting only 1 contrast in case there were multiple
Using the anota2seqRun function with custom filtering parameters for maxP was still based on a maxPAdj of 0.15. This has been fixed, i.e. when maxP filtering is applied no maxPAdj filter will be used.
Changes in version 1.3.2:
Changes in version 3.11.2 (2018-09-04):
CODE REFACTORING
Changes in version 3.11.1 (2017-08-28):
Changes in version 3.11.0 (2017-04-30):
Changes in version 0.99.25:
Changes in version 0.99.02:
Changes in version 0.99.01:
Submit the package to the Bioconductor project For Developers
All the functions name must have the prefix ‘artms’
Normalized all documentation using roxygen2 Deprecated
Nothing yet Defunct
Nothing yet
Changes in version 1.1.1:
Improvements
need to specify number of cores to use a parallel environment
reference library (improvement of reference spectra by cleaning them)
Changes in version 1.17.2:
Bugfix ComBat.step2 by forcing ComBat input to be a matrix
Added new argument to assign.wrapper to specify direction of signature genes.
parameters file in assign.wrapper is now in yaml format.
Changes in version 1.33.2 (2018-06-29):
Fix NEWS
change maintainer email
Changes in version 1.33.1 (2018-05-15):
Changes in version 1.3.24 (2018-10-21):
Changes in version 1.3.23 (2018-10-18):
Changes in version 1.3.22 (2018-10-19):
Update of DESCRIPTION to require C++11
Once again, replacement of R::lgammafn
by std::lgamma
in c++ code
Changes in version 1.3.21 (2018-10-19):
R::lgammafn
due to errors in Bioconductor build
reportChanges in version 1.3.20 (2018-10-18):
Changes in version 1.3.19 (2018-10-18):
R::lgammafn
by std::lgamma
in c++ code (great
speed-up! with thanks to Shian Su (@Shians))Changes in version 1.3.18 (2018-10-15):
BASiCS_D_TestDE
has been deprecated (replaced by BASiCS_TestDE
)
SingleCellExperiment::
added to all calls of isSpike
Extra input check in BASiCS_MCMC
to avoid issues due to multiple
types of spike-ins being present in the data.
Changes in version 1.3.17 (2018-10-11):
Changes in version 1.3.16 (2018-10-08):
Changes in version 1.3.15 (2018-10-07):
Changes in version 1.3.13 (2018-09-30):
Changes in version 1.3.11 (2018-09-30):
Changes in version 1.3.10 (2018-09-28):
Prevent BASiCS_Chain from crashing when StoreAdapt=TRUE for the regression case
Prevent plot function errors
Updated documentation using roxygen2
Changes in version 1.3.9 (2018-09-27):
Changed raw data accessor to ‘counts’ instead of ‘assay’
makeExampleBASiCS_Data now generates 30 cells instead of 20
Updated unit tests to match these 30 cells
Changes in version 1.3.8 (2018-09-05):
Changes in version 1.3.7 (2018-09-05):
Changes in version 1.3.6 (2018-09-04):
Changes in version 1.3.5 (2018-08-31):
Changes in version 1.3.3 (2018-05-21):
Changes in version 1.3.2 (2018-05-21):
Changes in version 1.3.1 (2018-05-21):
Unit tests created for selected C++ functions (Hidden_rDirichlet
and Hidden_muUpdate
)
Updated unit tests in spike-in parameter estimation to have tolerance (TEMPORARY FIX)
Minor updates in makeExampleBASiCS_Data
and newBASiCS_Data
function to allow different spike-in types (as in
SingleCellExperiment
)
Changes in version 0.99.19:
Replacing foreach with BiocParallel
Fix some errors in BB_fun
Changes in version 0.99.9:
Changes in version 0.99.8:
Changes in version 0.99.4:
Changes in version 0.99.1:
Changes in version 0.99.0:
Pass all checks of both R CMD build and R CMD BiocCheck.
Future work: * Vignette needs to be further improved. * Need to Improve man pages. * Submit to Bioconductor.
Changes in version 1.3.3:
Changes in version 1.3.2:
Changes in version 1.3.1:
Changes in version 1.4.0:
Removed native support for RleMatrix and packed symmetric matrices.
Added multi-row/column getters.
Added mechanism for native support of arbitrary developer-defined matrices.
Switched to row/colGrid() for defining chunks in unsupported matrices.
Changes in version 1.13.11 (2018-10-25):
Changes in version 1.13.10 (2018-10-24):
test for the gdepoch function
simplification of code
performance improvements
Changes in version 1.13.9 (2018-10-23):
test for the loss function
some simplifications
Changes in version 1.13.8 (2018-10-22):
first implementation of tests with testthat
simplifications of the code
Changes in version 1.13.7 (2018-10-10):
Changes in version 1.13.6 (2018-10-05):
Changes in version 1.13.5 (2018-10-04):
checks for the validity of inputs added
some performance improvements
Changes in version 1.13.4 (2018-10-02):
Changes in version 1.13.3 (2018-09-28):
usage of data.table for various functions for performance improvement
simplification of the source code
Changes in version 1.13.2 (2018-09-27):
Changes in version 1.13.1 (2018-09-25):
BEclear uses now BiocParallel instead of snowfall for parallelisation
roxygen2 is now used for the generating the documentation
major code refactoring
some minor bug fixes
performance improvements
Changes in version 2.6.2:
Fix issue in the formatData() function. It is now possible to format using fpkm expression values when using Bgee 14.0.
Implementation of regression tests
Update vignette and README
Changes in version 2.6.1:
Fix issue on Bgee 14.0 tar.gz annotation file management.
Update README and DESCRIPTION files.
Changes in version 1.1.5 (2018-07-02):
Changes in version 1.9.07:
replace DOSE::dotplot by clusterProfiler::dotplot
replace r_data by r_info in reports
not need to define genelist in r_data
update ReactomeFI.RDS file (version 2017)
update DisGeNet0918.RDS file (version September 2018), move it from wiki.ubuntu.com to github/kmezhoud
Changes in version 1.9.06:
rm warning message for min and max functions
run getFreqMutData() when getListProfData() instead getCoffeeWheel_Mut(). Avoid error when loading x profiles data to workspace.
include Tools panel into Workspace panel.
update Overview image
Changes in version 1.9.05:
r_data vs r_info … https://radiant-rstats.github.io/docs/news.html
use r_info for dataset list and r_data for genes list
set progress bar
Changes in version 1.9.04:
Changes in version 1.9.03:
modify stop function
update paste gene list function
Changes in version 1.9.02:
upload and download using Rstudio file browser remove plot_downloader function and replace it by download_link (defined in radiant.R file)
add radiant_old.R file for needed functions but not longer used by radiant.data
Changes in version 1.9.01:
replace getdata() by get_data()
replace factorizer() by lapply(.,factor)
Changes in version 1.17:
NEW FEATURES
(1.17.21) Added quit-with-status option to both BiocCheck and BiocCheckGitClone for compatibility with travis
(1.17.18) Update devel to use BiocManager instructions instead of BiocInstaller
(1.17.17) Add a new function that can be run interactive or command line BiocCheckGitClone which is only run on a source directory not a tarball. This will check for bad system files
(1.17.17) BiocCheck addition: Checks vignette directory for intermediate and end files that should not be included.
(1.17.16) Checks for Bioconductor package size requirement if checking tarball
BUG FIXES
Changes in version 1.5:
NEW FEATURES
BUG FIX
USER-VISIBLE CHANGES
Changes in version 1.31.2:
NEW FEATURES
BiocManager
a CRAN package.BUG FIXES
remotes::install
which is
not an exported function of the package. This link was fixed.Changes in version 1.0.0:
Changes in version 1.5.4:
Improved spelling
Reduced the complexity fo combineScoresPar and combineScores.
Improve efficiency of code in vignettes.
Change the news section
Adding a section about GeneOverlap package.
Changed the License
Changes in version 1.16:
NEW FEATURES
(v 1.15.9) BatchtoolsParam() gains resources=list() for template file substitution.
(v 1.15.12) bpexportglobals() for all BPPARAM exports global options (i.e., base::options()) to workers. Default TRUE.
BUG FIXES
(v 1.15.6) bpiterate,serial-method does not return a list() when REDUCE present (https://github.com/Bioconductor/BiocParallel/issues/77)
(v 1.15.7) bpaggregate,formula-method failed to find BPREDO (https://support.bioconductor.org/p/110784)
(v 1.15.13) bplappy,BatchtoolsParam() coerces List to list (https://github.com/Bioconductor/BiocParallel/issues/82)
(v 1.15.14) implicit loading of BiocParallel when loading a third-
party package failed because reference class initialize()
methods
are not installed correctly. This bug fix results in signficant
revision in the implementation, so that valid objects must be
constructed through the public constructors, e.g.,
BatchtoolsParam()
Changes in version 0.99.0:
Changes in version 1.17:
BSseq() will no longer reorder inputs. Previously, the returned BSseq object was ordered by ordering the loci, although this behaviour was not documented. BSseq() may still filter out loci if rmZeroCov = FALSE or collapse loci if strandCollapse = FALSE or duplicate loci are detected, but the relative order of loci in the output will match that of the input.
Fix bug with maxGap argument of BSmooth(). The bug meant that the ‘maximum gap between two methylation loci’ was incorrectly set to 2 * maxGap + 1 instead of maxGap. This likely did not affect results for users who left the default value of maxGap = 10^8 but may have affected results for small values of maxGap.
Changes in version 0.99.13 (2018-10-09):
Changes in version 0.99.6 (2018-07-27):
Changes in version 1.5:
Added new functions for spatial analysis of clusters: findLinks finds nearby pairs of clusters (for example TSSs and enhancers) and calculates the correlation of expression between them. findStretches find stretches along the genome where clusters are within a certain distance of eachother (for example groups of enhancer forming a super enhancer) and calculates the average pairwise correlation between members.
Changed the way clustering works: CAGEfightR uses coverage() to calculate genome-wide signals and now rounds the resulting signal to a certain number of digits (this can be modified via the CAGEfightR.round option), to prevent small positive or negative values due to floating point errors. This makes clustering more stable meaning the tuneTagClustering function is now deprecated. This should also increase the speed of most functions.
CAGEfightR now uses GPos instead of GRanges for storing CTSSs, this should result in improved memory performance.
Several changes to clusterBidirectionality: Balance is now calculated using the midpoint as well (preventing some rare cases where the midpoint could mask a single highly expressed CTSS), the pooled CTSS signal is now prefiltered for bidirectionality to increase speed and custom balance function can be provided (Bhattacharyya coefficient and Andersson’s D are included).
Added new check-functions to make it easier to check if objects are formatted correctly
Changes in version 1.24.0:
NEW FEATURES
BUG FIXES
Prevent mergeSamples() from producing colData that cause other functions to crash later when coercing to data.frame.
Repaired paraclu support for CAGEset objects.
normalizeTagCount() works again on CAGEset objects.
consensusClustersGR() reports expression score of the selected sample (instead of silently ignoring the “sample” argument and reporting expression sum on all the samples).
Changes in version 1.99.2 (2018-10-28):
SIGNIFICANT USER-VISIBLE CHANGES
BUG FIXES
Changes in version 1.99.1 (2018-10-26):
NEW FEATURES
Changes in version 1.99.0 (2018-10-25):
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.13.3 (2018-10-24):
NEW FEATURES
Added ‘process’ method for queueing delayed processing functions to an imaging dataset and applying them
Added new processing methods for Cardinal v2 including new versions of ‘normalize’, ‘smoothSignal’, ‘reduceBaseline’, ‘peakPick’, ‘peakAlign’, and ‘peakFilter’
Added new ‘peakBin’ function for binning peaks
Updated ‘show’ method for new Cardinal v2 classes
New support for exporting ‘processed’ imzML files via the ‘writeImzML’ function
Changes in version 1.13.2:
NEW FEATURES
Changes in version 1.13.1:
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.12.1:
BUG FIXES
Changes in version 1.4.0 (2018-10-30):
New Features
obtainOneStudy() and obtainMultipleStudies() functions can obtain data for groups of genes each possess more than 250 genes (Virtually unlimited gene number).
A new argument for xlsxOutput() function to exchange the columns and rows.
Changes in version 1.1.2:
Changes
Added C++ code update step in vb_factorize(…, useC=TRUE)
Added Singular value decomposition initializer for vb_factorize(…, initializer=’svd2’) random initial condition: initializer=’random’
Changed default: filter_genes(…,rescue.genes=FALSE)
Changed filter_genes(), vmr.min action from vmr >= vmr.min to vmr > vmr.min (removes genes with vmr=0)
Added parallel run for vb_factorize(…, ncores=10)
Added feature_map(…)
Changes in version 0.99.1:
BUG FIXES
Code style.
Do not attempt to multithread on windows (suggest mulithread on linux).
Changes in version 0.99.0:
NEW FEATURES
Changes in version 0.99.15:
Changes in version 0.99.14:
Changes in version 0.99.13:
Bugfixes: - Visualization. Package is compatible with most recent ‘ggplot2’ release (3.x.x).
Updated CITATION
Changes in version 0.99.0:
Public pre-release for Bioconductor submission
CellTrails is fully compatible with a ‘SingleCellExperiment’ object
Bugfixes
Changes in version 1.7.1:
CHANGES
Changes in version 3.15.2:
Changes in version 3.15.1:
Changes in version 1.7.2:
BUGFIXES
Compatibility fixes for the new release of ggplot2 (3.0.0).
seqlevels() that are smaller than binsize are dropped properly in fixedWidthBins() and variableWidthBins().
Changes in version 1.9.2:
Changes in version 1.9.1:
Changes in version 2.2.0:
getClasses is no longer a slot of PredictParams. Every predictor function needs to return either a factor vector of classes, a numeric vector of class scores for the second class, or a data frame with a column for the predicted classes and another for the second-class scores.
Cross-validations which use folds ensure that samples belonging to each class are in approximately the same proportions as they are for the entire data set.
Classification can reuse fitted model from previous classification by using previousTrained function.
Feature selection using gene sets and networks. Classification can use meta-features derived from the individual features used for feature selection.
tTestSelection function for feature selection based on ordinary t-test statistic ranking. Now the default feature selection function, if none is specified.
Tuning parameter optimisation metric is specified by providing a tuneOptimise parameter to TrainParams rather than depending on ResubstituteParams being used during feature selection.
Changes in version 2.1.5 (2018-06-28):
Changes
Add functionality to getBestFeatures
to allow edgeR
for DE, as
well as weights used with edgeR
for zinbwave
compatability. As
part of this change: - Removed isCount
argument and replaced with
more fine-grained DEMethod
argument in getBestFeatures
,
mergeClusters
; or mergeDEMethod
in RSEC
. - Change to class
definition: added slot merge_demethod
to keep track of the DE
method used in merging
Change function names (old function name is now depricated): -
combineMany
-> makeConsensus
- removeUnclustered
->
removeUnassigned
Arguments to functions changed: - combineProportion
->
consensusProportion
in RSEC
- combineMinSize
->
consensusMinSize
in RSEC
- sampleData
-> colData
to match
SummarizedExperiment
syntax (in many plotting functions). -
alignSampleData
-> alignColData
in plotHeatmap
-
ignoreUnassignedVar
-> filterIgnoresUnassigned
in mergeClusters
(and other functions) for clarity. - removeNegative
->
removeUnassigned
in getBestFeatures
for uniformity - Removed
largeDataset
option to subsampleClustering
because no longer
provides advantage. - nBlank
-> nBlankFeatures
in makeBlankData
to allow for samples
Created functions: - primaryClusterLabel
and primaryClusterType
-
getReducedData
- assignUnassigned
: assigns unassigned samples to
nearest cluster - renameClusters
and recolorClusters
: assign new
names/colors to clusters within a particular clustering -
clusterMatrixColors
: wrapper to convertClusterLegend
to return
matrix like clusterMatrix
only with colors in place of the internal
cluster ids (like existing clusterMatrixNamed
) -
plotClustersTable
for plotting a heatmap showing the results of
tableClusters
- subsetByCluster
for subsetting CE object to only
those samples in a particular cluster(s) of a clustering. -
plotFeatureScatter
for a scatter plot of 2+ features (genes)
colored by cluster - addToColData
and colDataClusters
adding
clustering information to colData of object. addToColData
returns
object with colData
augmented, while colDataClusters
just returns
the DataFrame
with clusterings added. - updateObject
to update
historical object created from previous versions to the current class
definitions.
Added arguments: - whichAssay
to all functions to allow the user to
select the assay on which the operations will be performed. -
stopOnErrors
to RSEC
- nColLegend
to plotReducedDims
-
subsample
and sequential
to RSEC
to allow for opting out of
those options (but default is TRUE
unlike clusterMany
) -
nBlankSamples
and groupsOfSamples
to makeBlankData
to allow for
separating samples (columns) - add
and location
to
plotClusterLegend
makeBlankData
will now allow for making blank columns to separate
groups of samples.
plotDendrogram
now allows for plotting of colData
(previously
sampleData
) like plotHeatmap
or plotClusters
clusterMany
now allows user-defined ClusterFunction
objects to
argument clusterFunction
.
Removed restriction in plotClustersWorkflow
that only
clusterType="clusterMany"
allowed.
Allow getClusterManyParams
to search old clusterMany
runs as
well.
Added table
method to plotHeatmap
(for plotting heatmap of
results of table
function)
Added error catch if try to give argument whichCluster
to
mergeCluster
.
Added error catch if give param whichClusters
to functions that
only take whichCluster
(singular) as an argument
plotFeatureBoxplot
now returns (invisibly) the colors and
clusterIds along with the boxplot information.
Bugs
Add check for merge_nodeMerge table that mergeClusterId
column
can’t be NA for entries where isMerged=TRUE
Fix internal .makeIntegerClusters so that if given values 1:K
for
input clustering will retain these same values (Issue #227)
mergeClusters
now returned object saves the merge information (and
deletes old info and updates clusterType/clusterLabel of existing
merge clusters), even if mergeMethod="none"
.
Fix removeClusterings
so doesn’t loose merge info unless deleting
relevant clusterings.
Changes in version 2.1.4 (2018-06-27):
Bugs
Changes in version 2.1.3 (2018-05-24):
Bugs
Fix bug in how clusterMany
and defaultNDims
dealt with filtering
choices in reduceMethod
.
Fixed bug in how clusterMany
assigned label names
Fixed bug so that clusterMany
labels are increased (iteration
version added) if clusterMany
is rerun. Also, user-defined labels
for functions like mergeClusters
are now updated with iteration
value if they are duplicated.
Changes in version 2.1.2 (2018-05-17):
Bugs
Fix so that estimates of the proportion non-null in mergeClusters
are always positive.
Fix so that calculation of filter with ignoreUnassignedVar
doesn’t
delete existing base filter of same type.
Fix plotClustersWorkflow
where wrong cluster was grabbed to plot.
Fix bug in plotDendrogram
where colors of clusters plotted with
could be subsumbed by color of previous clustering. (git Issue
#220
)
Changes in version 2.1.1 (2018-05-15):
Bugs:
mergeCutoff
and
mergeLogFCcutoff
were passed instead to dendroNDims
.Changes in version 1.15.1 (2018-05-30):
add dotplot in heatmapPEI and vignettes
multi-group labelling
Changes in version 1.19.4:
markers=
argument is passed in and not null.Changes in version 1.17.1:
Changes in version 1.15.1:
Changes in version 1.19.1:
Heatmap()
: no column name added if the input matrix is a one-column
matrix.
oncoPrint()
: scales the the row annotations are now the same if
rows are split.
Changes in version 0.99.0:
Changes in version 0.99.11:
Changes in version 0.99.0:
countsimQCReport()
. For reproducible results,
please set the random seed explicitly in the R session.Changes in version 0.5.2:
Add options to silence progress indicators
Fixes in dispersion visualization
Changes in version 0.5.0:
Add a number of quantitative evaluation criteria and tests for pairwise comparison of data sets
The argument subsampleSize
to the countsimQCReport()
function now
determines the number of observations for which (time-consuming)
statistics are calculated
A new argument maxNForCorr
is added to the countsimQCReport()
function to indicate the number of observations for which pairwise
correlations are calculated
Improvements in documentation
Changes in version 0.4.6:
Changes in version 0.4.5:
Allow data frames or matrices as input (assuming design = ~ 1)
First implementation of area between ECDFs
Increase transparency of points in scatter plots
Changes in version 0.4.4:
Changes in version 0.4.2:
Changes in version 0.4.1:
Added pairwise sample and variable correlation distributions.
Added box plots and violin plots.
Changes in version 0.4.0:
Changes in version 0.3.2:
Changes in version 0.3.0:
Added K-S statistics.
Added line density plots.
Changes in version 1.9.2:
Keep metadata columns in targets
Adds function for splitting insertion sequences
Changes in version 1.16.0:
Added normFactors() function to avoid confusion when normOffsets() returns factors.
Deprecated type=”scaling” option in normOffsets().
Added calculateCPM() function for convenient calculation of (log-)CPMs.
Split up consolidateSizes() function into consolidateWindows(), consolidateTests() and consolidateOverlaps(). Deprecated consolidateSizes() itself.
Switched output of combineTests() and getBestTest() and related functions to a DataFrame.
Modified mergeWindows() behaviour with specified sign=, for dealing with nested windows of opposing sign.
Altered controlClusterFDR() to take the largest adjusted p-value threshold that yields a cluster-level FDR below target=.
Simplified detailRanges() output so that it no longer returns an arbitrary exon number.
Changes in version 1.20.3:
UPDATED FUNCTIONS
Fix bug in function PrepareAnnotationRefseq.R
Update function aaVariation.R to avoid translate ‘TTG’ and ‘CTG’ into ‘M’
Changes in version 1.6.0:
Restructured the CyData class for simplicity and internal fields.
Deprecated plotCell* functions, renamed them to plotSphere*.
Added the createColorBar() convenience function.
Removed the diffIntDist() function.
Restored option for quantile normalization in normalizeBatch(). Switched to deterministic algorithm for sampling when mode=”warp”.
Changes in version 3.6:
Changes in version 1.9.2:
NEW FEATURES
SIGNIFICANT USER-VISIBLE CHANGES
primer.fwd has been replaced by orient.fwd in the filterAndTrim function. This option consistently orients mixed-orientation single-end or paired-end reads based on matching the provided sequence fragment to the start or end of each read (or paired read). Intended for use with mixed-orientation reads that included sequenced primers. If primers aren’t included in the amplicons, an external re-orientation solution remains preferable.
trimRight has been added to the filterAndTrim function. This removes the specified number of bases from the end (“right” side) of each read.
BUG FIXES
Changes in version 1.9.1:
BUG FIXES
mergePairs now gracefully handles cases when zero reads succesfully merge.
plotQualityProfile now works correclty when given a directory containing fastq files.
Changes in version 1.5.2:
The DaMiR.normalization function embeds also the ‘logcpm’ normalization.
Now, DaMiR.EnsembleLearning calculates also the Positive Predicted Values (PPV) and the Negative Predicted Values (NPV).
Three new functions have been implemented for the binary classification task: DaMiR.EnsembleLearning2cl_Training, DaMiR.EnsembleLearning2cl_Test and DaMiR.EnsembleLearning2cl_Predict. The first one allows the user to implement the training task and to select the model with the highest accuracy or the average accuracy; the second function allows the user to test the selected classification model on a test set defined by the user; the last function allows the user to predict the class of new samples.
Removed black dots in the violin plots.
Changes in version 1.4.1:
Changes in version 1.11.1:
Changes in version 1.9.21:
Changes in version 1.8.6:
The columns that have strings removed while loading
Labels can be changed in the main plots
Plot Information box added to give more information about the plots.
Changes in version 1.8.4:
Calculated padj and foldchange columns added to all detected genes result
Normalization issue is fixed in comparison table
Changes in version 1.8.3:
Changes in version 1.8.2:
Package installation changed to warning
Dependancies removed from DESCRIPTION to suppress loading messages
Biarxiv citation added
Changes in version 1.8.1:
Interactive heatmap height and width fix
DEBrowser turned to a modular structure. The modules can be used in other shiny applications.
More interactivity added to Heatmaps and main plots.
Lasso selection is added to main plots
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
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
Changes in version 1.99.1 (2013-06-25):
Updates
Using knitr for prettier vignettes
Including shearwater vignette
Bugfixes
fixed issues with deletions in bf2Vcf()
makePrior() adds background on all sites
Changes in version 1.99.0 (2013-04-30):
Updates
New shearwater algorithm
Including VCF output through summary(deepSNV, value=”VCF”)
Changes in version 1.17.8:
Changes in version 1.17.7:
Changes in version 1.17.6:
Changes in version 1.17.5:
Changes in version 1.17.4:
Feature: Reduce parameter is used to remove outlier points after clustering genes.
Feature: Add maximum log2FoldChange to the significance output when multiple comparisons are used as inputs.
Feature: Remove non-mapped genes in degPlot.
Feature: Add specific function to plot degPatterns clusters.
Fix: Support DESeqResults for list of DEGSets.
Feature: Add variable selection for covariates that correlate with PCs in degCovariate function.
Feature: Add lasso as an option to variable selection in covariate analysis.
Feature: Fill with colors only significant variables by lm or lasso, and draw borber for correlated variables by cor.test.
Changes in version 1.17.3:
Fix: degCovariates works with metadata only with numerical variables
Fix: Remove theme set up for degPCA plot.
Feature: Make function to generate colors for metadata variables for annotation column in heatmap figure.
Feature: Improve degCovariates to add effect size of the covariates. Thanks to @vbarrera
Changes in version 1.17.1:
Fix: remove discrete scale color in degPCA.
Feature: Return same output for degPatterns with single genes. Thanks Amir Jassim.
Feature: Allow custom y-axis lab in degPlot. Thanks @vbarrera.
Changes in version 1.34.1:
Changes in version 0.8.0:
NEW FEATURES
Add get/setAutoBlockSize(), getAutoBlockLength(), get/setAutoBlockShape() and get/setAutoGridMaker().
Add rowGrid() and colGrid(), in addition to blockGrid().
Add get/setAutoBPPARAM() to control the automatic ‘BPPARAM’ used by blockApply().
Reduce memory usage when realizing a sparse DelayedArray to disk
+ On-disk realization of a DelayedArray object that is reported to be sparse
(by is_sparse()) to a "sparsity-optimized" backend (i.e. to a backend with
a memory efficient write_sparse_block() like the TENxMatrix backend imple-
mented in the HDF5Array package) now preserves sparse representation of
the data all the way. More precisely, each block of data is now kept in
a sparse form during the 3 steps that it goes thru: read from seed,
realize in memory, and write to disk.
showtree() now displays whether a tree node or leaf is considered sparse or not.
Enhance “aperm” method and dim() setter for DelayedArray objects. In addition to allowing dropping “ineffective dimensions” (i.e. dimensions equal to 1) from a DelayedArray object, aperm() and the dim() setter now allow adding “ineffective dimensions” to it.
Enhance subassignment to a DelayedArray object.
+ So far subassignment to a DelayedArray object only supported the **linear
form** (i.e. x[i] <- value) with strong restrictions (the subscript 'i'
must be a logical DelayedArray of the same dimensions as 'x', and 'value'
must be an ordinary vector of length 1).
+ In addition to this linear form, subassignment to a DelayedArray object
now supports the **multi-dimensional form** (e.g. x[3:1, , 6] <- 0). In
this form, one subscript per dimension is supplied, and each subscript
can be missing or be anything that multi-dimensional subassignment to
an ordinary array supports. The replacement value (a.k.a. the right
value) can be an array-like object (e.g. ordinary array, dgCMatrix object,
DelayedArray object, etc...) or an ordinary vector of length 1. Like the
linear form, the multi-dimensional form is also implemented as a delayed
operation.
Re-implement internal helper simple_abind() in C and support long arrays. simple_abind() is the workhorse behind realization of arbind() and acbind() operations on DelayedArray objects.
Add “table” and (restricted) “unique” methods for DelayedArray objects, both block-processed.
range() (block-processed) now supports the ‘finite’ argument on a DelayedArray object.
%*% (block-processed) now works between a DelayedMatrix object and an ordinary vector.
Improve support for DelayedArray of type “list”.
Add TENxMatrix to list of supported realization backends.
Add backend-agnostic RealizationSink() constructor.
Add linearInd() utility for turning array indices into linear indices. Note that linearInd() performs the reverse transformation of base::arrayInd().
Add low-level utilities mapToGrid() and mapToRef() for mapping reference array positions to grid positions and vice-versa.
Add downsample() for reducing the “resolution” of an ArrayGrid object.
Add maxlength() generic and methods for ArrayGrid objects.
SIGNIFICANT USER-VISIBLE CHANGES
Multi-dimensional subsetting is no more delayed when drop=TRUE and the result has only one dimension. In this case the result now is returned as an ordinary vector (atomic or list). This is the only case of multi-dimensional single bracket subsetting that is not delayed.
Rename defaultGrid() -> blockGrid(). The ‘max.block.length’ argument is replaced with the ‘block.length’ argument. 2 new arguments are added: ‘chunk.grid’ and ‘block.shape’.
Major improvements to the block processing mechanism. All block-processed operations (except realization by block) now support blocks of arbitrary geometry instead of column-oriented blocks only. ‘blockGrid(x)’, which is called by the block-processed operations to get the grid of blocks to use on ‘x’, has the following new features: + It’s “chunk aware”. This means that, when the chunk grid is known (i.e. when ‘chunkGrid(x)’ is not NULL), ‘blockGrid(x)’ defines blocks that are “compatible” with the chunks i.e. that any chunk is fully contained in a block. In other words, blocks are chosen so that chunks don’t cross their boundaries. + When the chunk grid is unknown (i.e. when ‘chunkGrid(x)’ is NULL), blocks are “isotropic”, that is, they’re as close as possible to an hypercube instead of being “column-oriented” (column-oriented blocks, also known as “linear blocks”, are elongated along the 1st dimension, then along the 2nd dimension, etc…) + The returned grid has the lowest “resolution” compatible with ‘getAutoBlockSize()’, that is, the blocks are made as big as possible as long as their size in memory doesn’t exceed ‘getAutoBlockSize()’. Note that this is not a new feature. What is new though is that an exception now is made when the chunk grid is known and some chunks are >= ‘getAutoBlockSize()’, in which case ‘blockGrid(x)’ returns a grid that is the same as the chunk grid. + These new features are supposed to make the returned grid “optimal” for block processing. (Some benchmarks still need to be done to confirm/quantify this.)
The automatic block size now is set to 100 Mb (instead of 4.5 Mb previously) at package startup. Use setAutoBlockSize() to change the automatic block size.
No more ‘BPREDO’ argument to blockApply().
Replace block_APPLY_and_COMBINE() with blockReduce().
BUG FIXES
Changes in version 1.15.4:
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.15.2:
BUG FIXES
Changes in version 1.15.1:
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.15.1:
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.15.1:
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.22.0:
No replicate designs no longer supported (previous version began deprecation with a warning).
unmix() now optionally will return the correlation (in the variance stabilized space) of the fitted data to the original data, and the matrix of fitted data (format=”list”). Argument ‘loss’ was changed to ‘power’. Will give warning if the columns of ‘pure’ have high correlation (in the variance stabilized space).
Changes in version 1.21.21:
Changes in version 1.21.15:
Changes in version 1.21.14:
Changes in version 1.21.13:
Changes in version 1.21.9:
DESeq() now only says one time ‘using supplied model matrix’, previously this was repeated three times from sub-functions. Sub-functions therefore no longer print this message.
Fixed bug when lfcShrink run directly after LRT with supplied model matrices.
Added heuristic to prevent Cook’s outlier based filtering when the max Cook’s sample has lower counts than 3 other samples. Restricted to two group comparison datasets.
Changes in version 1.0.5:
Changes in version 1.0.1:
Changes in version 1.7.4:
Changes in version 1.7.3:
Changes in version 1.7.2:
Changes in version 1.14.0:
Changes in version 1.0.0:
Five diffusion kernels available, they can be computed from an ‘igraph’ object.
Diffusion implementations divided between ‘diffuse_raw’ for deterministic scores and ‘diffuse_mc’ for permutation analysis, which is parallelised. In total, seven diffusion scores are accessible through the ‘diffuse’ function.
Performance evaluation wrapped in the ‘perf’ function.
Helper functions in helpers.R (to plot diffusion scores, to check if a kernel matrix is actually a kernel, to extract largest CC from a graph)
Changes in version 1.7.3:
Changes in version 1.3.1:
CHANGES SINCE LAST TIME
A new citation is added.
Several bugs are corrected.
Changes in version 1.1.20 (2018-10-11):
Changes in version 1.1.2 (2018-05-09):
chrsPerChunk
argument specifies the number of
chromosomes to compute at a time (default is 1).Changes in version 1.1.2:
BUG FIX
Changes in version 1.1.1:
UDPATE
Changes in version 1.1.0:
NEW FEATURES
Changes in version 1.1.1:
FEATURES
New function called draw_folding(). This function allows the drawing of the STRAND, HELIX and TURN types which denote regions of the proteins that assemble as beta-strands, alpha helicies or make a turn in the 3D structure of the protein.
New function called parse_gff(). This function imports files or urls that link to a GFF3 format and parses the data to allow it to be plotted.
Changes in version 1.2.0:
Added removeSwappedDrops() for removing swapping in other types of droplet-based data.
Added alpha= argument to testEmptyDrops() to support overdispersion during sampling. Returned arguments and estimates in metadata of testEmptyDrops(), emptyDrops().
Added encodeSequences() for convenient 2-bit encoding of sequences.
Added get10xMolInfoStats() function to compute per-cell statistics from a molecule info file.
Deprecated read10xMatrix(), as it does not add much practical value over Matrix::readMM().
Support the 10X sparse HDF5 format in read10xCounts().
Support the 10X sparse HDF5 format in write10xCounts().
Changes in version 1.11.1:
Changes in version 3.24.0:
New functions catchKallisto() and catchSalmon() to read outputs from kallisto and Salmon and to compute overdispersion factors for each transcript from bootstrap samples.
New function readBismark2DGE() to read coverage files created by Bismark for BS-seq methylation data.
New method “TMMwzp” for calcNormFactors() to better handle samples with a large proportion of zero counts.
The default value for prior.count increased from 0.25 to 2 in cpm() and rpkm(). The new value is more generally useful and agrees with the default values in aveLogCPM() and with the DGEList method for plotMDS().
zscoreNBinom() now supports non-integer q values.
The scaleOffset() S3 methods for DGEList and default objects are now registered in the NAMESPACE. Previously the functions were exported but not registered as S3 methods.
The rowsum() method for DGEList objects (rowsum.DGEList) now automatically removes gene annotation columns that are not group-level.
More specific error messages from DGEList() when invalid (NA, negative or infinite) count values are detected.
Bug fix to glmfit.default() when lib.size is specified.
Bug fix to column name returned by decideTestsDGE().
Changes in version 1.9.1:
fixed: a minor bug to allow users generate EGSEA reports for only a single base method
fixed: several minor bugs
Change: from biocLite to BiocManager
Changes in version 1.0.0:
user can now supply her/his/its own colour vector to label the points
default is to now draw grid lines and only have left and bottom borders
when selectLab is not NULL, even variables that do not pass the thresholds are now labelled along with those that do, even when DrawConnectors is either TRUE or FALSE.
correctly catches non-numeric x and / or y variables, and throws error.
the function now tolerates P values of 0 (zero) and replaces these with the lowest possible double value, given a user’s specific computer architecture and R version.
Changes in version 1.11.1:
as.normalizedMatrix()
function to convert a matrix to
normalizedMatrix
classChanges in version 2.12.0:
Major refactoring of ID mapping for the rownames of a SummarizedExperiment: (functions idmap / probe2gene): - to.ID can also be a rowData column to support user-defined mappings - support of data-driven strategies for many:1 and 1:many mappings - synchronized behavior of microarray probe ID mapping (probe2gene) and general gene ID mapping (idmap)
Alternative representation of gene sets based on GSEABase::GeneSet and GSEABase::GeneSetCollection to facilitate gene ID mapping for gene sets (function getGenesets)
Output destination of HTML reports (functions eaBrowse / ebrowser): extended control via arguments out.dir and report.name that overwrite corresponding config defaults (configEBrowser)
Separation of nominal and adjusted p-values in DE and EA result tables (functions deAna / sbea / nbea)
Changes in version 2.5.9:
Changes in version 2.5.8:
Changes in version 2.5.6:
Add additional (integer) ID columns to the tables for the MySQL backend to improve performance.
Use integer primary key columns for join queries in MySQL/MariaDB EnsDb databases.
Changes in version 2.5.5:
Changes in version 2.5.2:
Changes in version 2.5.1:
Changes in version 1.24.0:
NEW FEATURES
Changes in version 0.99.8:
Changes in version 0.99.7:
Changes in version 0.99.5:
Increase font sizes in plots
Add line of mean DEG discovery in interect plot
Improve clarity in plots
Vignette receives major edits to improve clarity
Changes in version 0.99.4:
Changes in version 0.99.3:
Add additional citations
Reduce example runtime with fewer combinations
Changes in version 0.99.2:
Changes in version 0.99.0:
Codes ready for bioconductor submission
Added full example dataset
Cleanup the outputs to be more descriptive and append ERSSA_ to all files
Write vignette
Changes in version 1.7.3:
vignette: use print rather ‘knit_print.ggvis’
ggvis: include transparency, fix issue position legend
rbokeh: use vector instead of column names for ly_hexbin
Changes in version 1.7.2:
Changes in version 1.7.1:
Changes in version 2.0:
Minor bugs fixed (ClassifyEvents)
Relative error is now obtained when obtaining PSI
New statistical analysis based on PSI and the associated relative error
New pipeline for RNA-Seq based on quantification using a reference transcriptome
Multi-path events detection o Events with more than two alternative paths are detected
Changes in version 0.99.13:
Changes in version 0.99.12:
Loosened FDR calculations slightly (ExCluster was a bit too stringent)
Added plot.Type option to plotExonlog2FC function, which accepts “bitmap” and “PNG”
Bug-tested plot.Type so machines with at least Ghostscript or X11 forwarding will have minimal issues
Changed how files/folders are written & how write-permissions are detected, to avoid bugs
Updated the vignette to use BiocManager instead of biocLite
Changes in version 0.99.11:
Removed some duplicated code (stripping ID numbers, computing p-values)
removed several instances of cat() and print() and replaced them with message()
changed apply() code to use matrixStats instead, which is up to 500 times faster (Thanks Lori!)
this previous change sped the algorithm up from 1 hour+ to only ~ 20 minute runtime
changed the test dataset so one of the genes has a p-value < 0.05 and plots results
Changes in version 0.99.10:
The GFF_convert function now outputs GFF3 formatted annotations
Amended other functions (processCounts, ExCluster) to work on GFF3 formatted annotations
Changed the output of GFF_convert to be a GRanges object of said GFF3 annotations
Created a separate, internal, load_ExCluster_functions.R script to load helper functions
Removed many depenencies on variables outside the environment for said functions
Created a library of error messages in the ExCluster_errors.R script (internal)
Separated the large ExCluster function into ExCluster.R, ExClust_compute_stats.R, and ExClust_main_function.R
Changes in version 0.99.9:
Added the function rtracklayerGTFtoGFF, which flattens GTF files imported by rtracklayer to GFF format
Added the function GRangesFromGFF, which converts GFF formatted data to GRanges format
Added the function GRangesFromExClustResults, which converts ExCluster function results to GRanges format
Removed repeated code from the GFF_convert function
Most functions now have checks to ensure GTF, GFF, and ExClustResults data is formatted correctly’
Made an improvement to the processCounts function, which handles some edge cases better (zero reads in some conditions)
Changes in version 0.99.8:
Changes in version 0.99.7:
Fixed a number of bugs resulting from ExCluster() function code changes
Updated the Vignette to correctly reflect changes suggested from the Bioconductor review
Added several package imports to better keep track of global variables/functions
Reduced the lengths of code lines in a number of instances
Cleaned up error messages for ExCluster() function
Changes in version 0.99.6:
Completely re-wrote the GFF_convert() function to have helper functions and take advantage of GenomicRanges
Changed processCounts, which was incorrectly counting reads as stranded (is now set to unstranded)
Altered ExCluster null hypothesis simulations to run faster (about 4 times faster now)
Made numerous small changes to address the issues with first Bioconductor review
Changes in version 0.99.5:
Changes in version 0.99.4:
Changes in version 0.99.3:
Changes in version 0.99.2:
Changes in version 0.99.1:
Changes in version 0.99.0:
ExCluster package now passes R CMD build and check, and BiocCheck!
takes about 2-3 hours to run on large datasets
added a “Toy Dataset” for function manual runnable examples within the package
more code in ExCluster has been turned into functions to reduce repeated code
still some repeated code (such as parsing EnsIDs and exon bins)
Changes in version 1.7.0:
NEW FEATURES
MODIFICATIONS
Changes in version 1.1.6:
Changes in version 1.1.5:
Added full vignette on the zebrafish dataset
Small modifications to the Mus musculus vignette
Changes in version 1.1.4:
Added a full vignette showing a case study on Mus musculus
Functions getCom
and getGraph
are exported now
Added DT
and other packages to suggests
Small fixes for BiocCheck
Changes in version 1.1.3:
Changes in version 1.1.2:
buildGraphFromKEGGREST
test in 32-bit Windows due to its
memory usageChanges in version 3.15:
Changes in version 1.7.1:
Setting colwidth to zero make column not to be drawn
Changable line width in plotEnrichment
Changes in version 1.10.1:
Changes in version 1.19.1:
Changes in version 1.19.0:
Changes in version 0.99.10 (2018-09-27):
Changes in version 0.99.9:
Changes in version 0.99.8:
Man pages list shortened to 10 pages
Documentation update
Changes in version 0.99.7 (2018-07-19):
Argument specifying column with Gene IDs added to GAFReader class
Example gaf file truncated (don’t use it for real analysis!)
Changes in version 0.99.6 (2018-07-19):
Malformed description filed problem fixed
Imports for stats and methods packages added
Changes in version 0.99.5 (2018-07-16):
Changes in version 0.99.4 (2018-07-16):
Vignettes update: less chunks with eval=FALSE
/data added to .Rbuildignore
Changes in version 0.99.3 (2018-07-13):
Vignettes update
Bugs with documentation fixed
R CMD check with no warnings
Changes in version 0.99.2 (2018-07-12):
Changes in version 0.99.1 (2018-07-12):
Changes in version 0.99.0 (2018-07-12):
Changes in version 1.17.1-1.17.6:
NEW FEATURES
ls.gdsn()
: the
listing recurses into child nodesUTILITIES
replace BiocInstaller biocLite mentions with BiocManager
digest.gdsn()
fails if the digest package is not installed
SIMD optimization in 2-bit array decoding with a logical vector of selection (3x speedup when there are lots of zeros)
BUG FIXES
put.attr.gdsn()
fails to update the existing attributeChanges in version 0.99.13 (2018-08-02):
Improved the code to include more from tidyverse; simplified code
Using now roxygen2 to create Namespace and importing each function indiviually
Created a NEWS.Rd file parsable by utils::news in folder inst
Changes in version 1.64.0:
NEW FEATURES
na.rm =
to row/colttests, requested by
https://github.com/Bioconductor/genefilter/issues/1Changes in version 1.23.2:
Changes in version 2.12.0:
pcair and pcrelate have been completely rewritten for better consistency with other methods. Some argument names have changed; see the documentation. The output of pcrelate is now a list of data.frames instead of a list of matrices.
pcrelateReadKinship and pcrelateReadInbreed are deprecated, as these tables are now returned by pcrelate.
pcrelateMakeGRM is deprecated; use pcrelateToMatrix with new pcrelate output format.
king2mat is deprecated; use kingToMatrix instead.
fitNullMM, fitNullReg, assocTestMM, and admixMapMM are deprecated. assocTestSeq and assocTestSeqWindow are defunct. Use fitNullModel, assocTestSingle, assocTestAggregate, and admixMap instead.
Changes in version 2.11.15:
Changes in version 2.11.14:
Changes in version 2.11.11:
Changes in version 2.11.8:
Changes in version 2.11.4:
Changes in version 1.18.0
NEW FEATURES
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.5.8:
Changes in version 1.34.0:
NEW FEATURES
2 changes to makeTxDbFromGFF() / makeTxDbFromGRanges(): + Now they support GFF3 files where the CDS parent is an exon instead of a transcript. Note that such GFF3 files are rare and not following the well established convention documented in the GFF3 specs: https://github.com/The-Sequence-Ontology/Specifications/blob/master/gff3.md + Now they accept missing/invalid CDS phases (with a warning).
makeTxDb() now accepts missing CDS phases.
Changes in version 1.18.0:
NEW FEATURES
Changes in version 1.34.0:
NEW FEATURES
DEPRECATED AND DEFUNCT
Deprecate several RangedData methods: seqinfo, seqinfo<-, seqnames, and findOverlaps#RangedData#GenomicRanges
+ RangedData objects will be deprecated in BioC 3.9 (their use has been
discouraged since BioC 2.12, that is, since 2014). Package developers
that are still using RangedData objects need to migrate their code to
use GRanges or GRangesList objects instead.
BUG FIXES
Make [[, as.list(), lapply(), and unlist() fail more graciously on a GenomicRanges object.
Make “show” methods for GenomicRanges and GPos objects robust to special metadata column names like “stringsAsFactors”.
Export the “update” method for GRanges objects. This addresses https://github.com/Bioconductor/GenomicRanges/issues/7
Changes in version 1.6.0:
USER VISIBLE CHANGES
Functions and classes deprecated in the previous release (scores, MafDb class) have been now removed from the package.
Added support to latest release 2.1 of gnomAD MAF data, stored in packages MafDb.gnomAD.r2.1.hs37d5 and MafDb.gnomADex.r2.1.hs37d5.
Changes in version 1.9 (2018-10-20):
Changes
Bayesian hierarchical model implemented => multiple comparison solved
Transition from effect size metrics to GLMs
Posterior predictive checks automatic
Retrospective power analysis available
Stan model debugging possible
Multicore speedup
Phylogenetic bias quantification
Data reduction with diagnostics procedure based on random forest
To do
Include a CONFIG file to change internal parameters
Add practical examples where genphen has been used.
Implement modules for data augmentation
Update todo after data augmentation
Changes in version 3.43.1:
man page links modernized
vignette to Rmd
Warning added to vignette indicating that gQTLBase/gQTLstats are more modern
Changes in version 4.0.0:
new function ‘gGlobalAncova’ for generalized linear models: groups of tested variables can be quantitative, categorical, ordinal and even of mixed types
new function ‘Plot.features’ for showing contributions of individual variables to global test statistic; equivalent to ‘Plot.genes’, but for ‘gGlobalAncova’
new function ‘gGlobalAncova.hierarchical’ for hierarchical testing.
new S4-class ‘GAhier’ for storing results of gGlobalAncova.hierarchical
new methods ‘show’, ‘results’, ‘sigEndnodes’, and ‘Plot.hierarchy’ to access and visualize results from ‘GAhier’ objects
added CITATION and NEWS files
Vignettes are now created with knitr
Changes in version 1.1.5:
USER-LEVEL CHANGES
add name and domain to categories in get_anno_categories()
update GO-graph (version 11-Oct-2018)
Changes in version 1.1.2:
NEW FEATURES
USER-LEVEL CHANGES
Changes in version 1.0.0:
NEW FEATURES
Changes in version 1.27.6 (2018-10-25):
Updated all pathway data.
Added PathBank database.
Changes in version 1.27.2 (2018-05-22):
Changes in version 1.13.2:
Changes in version 1.27.1:
Changes in version 1.10.0:
NEW FEATURES
SIGNIFICANT USER-VISIBLE CHANGES
By default automatic HDF5 datasets (e.g. the dataset that gets written to disk when calling ‘as(x, “HDF5Array”)’) now are created with chunks of 1 million array elements (revious default was 1/75 of ‘getAutoBlockLength(x)’). This can be controlled with new low-level utilities get/setHDF5DumpChunkLength().
By default automatic HDF5 datasets now are created with chunks of shape “scale” instead of “first-dim-grows-first”. This can be controlled with new low-level utilities get/setHDF5DumpChunkShape().
getHDF5DumpChunkDim() looses the ‘type’ and ‘ratio’ arguments (only ‘dim’ is left).
Changes in version 1.17.1:
hlaDistance()
Changes in version 1.11.1:
Changes in version 1.1.2 (2018-09-07):
Adding gene name to tooltip
Fixing minor bugs in chart.R, save.R, stats.R
Changes in version 1.0.1 (2018-06-14):
Fixing minor bug in heatmap_plot about variances in case variable_cluster = TRUE.
Fixing minor bug in visualize_report.
Adding test_package.R file.
Changes in version 0.99.19:
Changes in version 0.99.18:
Changes in version 0.99.17:
Changes in version 0.99.16:
Changes in version 0.99.14:
Changes in version 0.99.13:
Respond to Bioconductor review on Aug 6, 2018
Minor changes: - Fix example for hpaXml() (issue #5) - Update documentation for hpa_downloaded_histology_v18 dataset (issue #6) - Re-name R/data.R to R/hpa_downloaded_histology_v18.R (issue #7) - Cross-reference documentations (issues #8, #9) - Combining hpaListParam() and hpaSubset() on one man page (issue #10)
Changes in version 0.99.12:
Changes in version 0.99.11:
Respond to Bioconductor review on Jun 11, 2018
Major changes: - Add the hpaVis() function as an umbrella for the whole function family - Add the hpaXml() function as an umbrella for all XML extraction
Minor changes: - Remove the ignore/ directory - Import individual functions for xml2 - Remove /dontrun{} on man pages - Modifications to hpaVis functions for better consistency - Updated the help files with details about output of each function
Changes in version 0.99.10:
Changes in version 1.23.3:
Changes in version 1.23.2:
Changes in version 1.23.1:
New Bioconductor devel
Update to HPA version 18
Changes in version 0.99.3:
MODIFICATIONS
updated R functions to use stopifnot() for error control
changed cat() to message() for communicating output
change use of R CRAN parallel functions to BiocParallel
replace date field with hard-corded date in vignette
update variable names to lower snake case in vignette
added new biocViews (RNASeq, Software, StatisticalMethod, FeatureExtraction)
Changes in version 0.23.2 (2018-07-18):
NEW FEATURES
Changes in version 0.23.1 (2018-07-18):
SOFTWARE QUALITY
Changes in version 0.23.0 (2018-05-01):
NOTES
Changes in version 1.13.3:
NEW FEATRUES
CHANGES
Changes in version 1.10.0:
Bug fix to seqinfo<- to support other arguments in the generic.
Modified behaviour of inflate() with unspecified fill= for GInteractions objects.
Changes in version 1.5.2:
NEW FEATURES
BUG FIXES
interest.sequential() and interest() corrections to their object output option.
annotateU12() modified to work correctly with the new changes in Biostrings package.
buildSsTypePwms() corrected and modified to better suit data for all species from SpliceRack and U12DB.
Changes in version 1.7.5:
added usage of clustering method FORK on unix-systems (thanks to Pablo Moreno)
fixed bug in parallelization to prevent conflicts with package ‘snow’
Changes in version 1.7.4:
preceded parallel-functions with ‘parallel::’ to use right package
fixed bug in function writeRScript using ‘loess’ retention time cor.
decreased runtime for R CMD check IPO
Changes in version 1.7.3:
added runnable examples
decreased size of pictures in vignettes/rsmDirectory
decreased runtime for unit-tests
replaces expand.grid with expand.grid.subset (in utils.R)
Changes in version 1.7.2:
bugfix: try to prevent error in calcPPS possibly caused by NAs
replaced cat() and print() calls with message()
Changes in version 1.7.1:
checking correlation of peak-shape with sinus curve (-pi/2 to pi*1.5), normal distribution or checkBorderIntensity
findIsotopes.IPO renamed parameter checkBorderIntensity to checkPeakShape
performance improvement calcPPS for checkPeakShape=FALSE
calculating xcmsSet-object and respective PPS for each DoE. (PPS is not estimated from rsm anymore)
additionally forwarding nSlaves for xcmsSet-function (also see getDefaultXcmsSetStartingParams())
Changes in version 1.7.0:
added support for XCMS-method retcor.loess
updated help files
changed return value of getRGTVValues
adapted unit tests
parameter scanrange for XCMS-methods findPeaks can be set but not optimized
Changes in version 1.6.2:
Changes in version 1.6.1:
Changes in version 2.16.0
SIGNIFICANT USER-VISIBLE CHANGES
Optimize unlist() on Views objects.
Optimize range(), any() and all() on CompressedRleList objects.
Optimize start(), end(), width() setters on CompressedRangesList objects.
DEPRECATED AND DEFUNCT
Deprecate several RangedData methods: + score, score<-, lapply, within, countOverlaps; + coercions from list, data.frame, DataTable, Rle, RleList, RleViewsList, IntegerRanges, or IntegerRangesList to RangedData. + RangedData objects will be deprecated in BioC 3.9 (their use has been discouraged since BioC 2.12, that is, since 2014). Package developers that are still using RangedData objects need to migrate their code to use GRanges or GRangesList objects instead.
BUG FIXES
Fix DF[IRanges(…), ] on a DataFrame with data.frame columns.
Make [[, as.list(), lapply(), and unlist() fail more graciously on a IRanges object.
NCList objects now properly support c().
Changes in version 1.1.13:
Add missing observer for assay type in row data plot panels.
Add missing observer for colorpicker when colouring by feature name in row-based plots, or by sample name in column-based plots.
Ignore NA values when computing the range of coloring scales.
Add a size expansion factor (5x) to the selected point when colouring by feature name in row-based plots, or by sample name in column-based plots.
Fix redundant coloring of selected point when colouring by feature name in row-based plots, or by sample name in column-based plots.
Update basic vignette.
Changes in version 1.1.12:
Changes in version 1.1.11:
Changes in version 1.1.10:
Fix colour scale to be invariant when selecting on a different color.
Protect heat map plot panels against restriction on zero samples.
Changes in version 1.1.9:
Changes in version 1.1.8:
Extend unit test coverage.
Move generics to separate file.
Minor fix to annotateEnsembl.
Update list of functionalities in README.
Changes in version 1.1.7:
Changes in version 1.1.6:
Changes in version 1.1.5:
New panel colors.
Control arguments to custom panels through action buttons.
Distinguish visible from active arguments for custom panels.
Changes in version 1.1.4:
Split ?defaults help page by panel type.
Generalized support for custome data plots and statistics tables.
Changes in version 1.1.3:
Add new Sample assay plot panel type.
Extend documentation.
Split vignette into three: basic, advanced, ExperimentColorMap.
Fix initialization of reduced dimensions with a single plot axis choice.
Substitute discouraged use of sapply.
Moved roxygen importFrom instructions closer to the relevant code.
Increase unit test coverage.
Consistent use of “colormap” through the package.
Update installation instructions.
Add CITATION file.
Add Figure 1 of article in README.
Changes in version 1.1.2:
Enable faceting by row and column, with appropriate updates to brush and lasso.
Enable shaping on data points.
Minor fix of jitter for violin and square plots.
INTERNAL: Enable storage of additional plot.data beyond X and Y in all.coordinates. See constant .allCoordinatesNames. Necessary for correct behaviour of brushes on faceted plots.
Changes in version 1.0.1:
Changes in version 0.1.27:
Changes in version 1.3.10:
A problem was fixed with the isoformSwitchTestDEXSeq() which could cause continuous co-variables to interpreted as discrete co-variables.
importRdata() was updated to be a bit more versetile with regards to accepting isoform_ids as row.names.
Both isoformSwitchTestDRIMSeq() and isoformSwitchTestDRIMSeq() was updated so the the resulting “isoformSwitchAnalysis” entry in the switchAnalyzeRlist also contains results with p-values set to NA. (NA filter removed). Furthemore the interpretation of design matrixes with regards to continous or discrete variables was improved.
The vignette was updated all around including the FAQ sections:
“What Quantification Tool(s) Should I Use?”
“What constitue an independent biological replicate?”
An error message was corrected to give the rigth error
various small updates
Changes in version 1.3.9:
Update to namespace to fix 1.3.8 update of importCufflinksFiles
Update to vignette to fix header
Changes in version 1.3.8:
Changes in version 1.3.7:
Changes in version 1.3.5:
One-line summary: Improved robustness, usability and speed
Main changes:
isoformSwitchTestDEXSeq() is introduced as the new default test as it is a more robust and much more reliable test for differential isoform usage.
The original isoformSwitchTest() is decommissioned due to it being inferior to both isoformSwitchTestDEXSeq() and isoformSwitchTestDRIMSeq() in most aspects.
importIsoformExpression() now also support import of StringTie quantifications.
updates that allows for better handling of Ensemble data.
updates throughout the R package making IsoformSwitchAnalyzeR (much) faster and more reliable.
Specifically the changes in inlcuded functions are:
isoformSwitchTestDEXSeq() is introduced as the default switch isoform switch test function
I handles the False Discovery Rate much better
It allows for batch corrected effect size estimation
It is a good deal faster (for smaller sample sizes)
isoformSwitchTestDRIMSeq() was updated to handle continous co-variates.
isoformSwitchTest() has been removed from the package since it is obsolte.
The importRdata() now:
Allows for import via either replicate abundance or replciate count data (or both - which is highly reccomended).
These changes were reflected in createSwitchAnalyzeRlist()
Test for full rank of experimental design
The importCufflinksCummeRbund() and importCufflinksFiles() now also extract and replicate isoform abundance estimates.
The functions importRdata(), importCufflinksCummeRbund() and importCufflinksFiles()
Calculates isoform fractions based on the replicate isoform fraction matrix (instead based on average isoform and gene expression) providing more accurate estimataes.
Was uptimized so they are more streamlined and faster.
importGTF (also used by importRdata() ) was updated to handle the problems with version numbering in amongst other Ensembl data.
To support the batch correction feature in isoformSwitchTestDEXSeq() the subsetSwitchAnalyzeRlist() function was modified so when subsetting in the the exon entry of the switchAnalyzeRList, as well as any replicate matrix entry (counts, abundances or isoform fractions), all isoforms from genes where at least one isoform passed the filters are kept.
The isoformToIsoformFraction() - a general purpose function for calculateing Isoform Fraction (IFs) from isoform expression - are introduced
The isoformToGeneExp() function was updated to be true general purpose (less stringent about data formating) and thanks to a tidyverse solution to the central problem is now between 2x-10x faster than previously (and becomses faster as the large the datasets are)
createSwitchAnalyzeRlist() was updated to
handle replicate data
fix condition name problems
test for full rank of design
importIsoformExpression() was updated to:
Support StringTie data.
Perform the inter-library normalization after a lenient expression cutoff have beeen applied (to remove most very lowly expressed isoforms).
Now uses the “scaledTPM” instead of “lengthScaledTPM” tximport option when imporitng with countsFromAbundance=TRUE The ignoreAfterBar argument from tximport() is now also supported.
We introduce the removeAnnoationData() function which eables removal of biological sequence and/or the replicate quantification data from a switchAnalyzeRlist threby significantly removing the size.
The default on the IFcutoff in switchPlot() and switchPlotTopSwitches() was updated to 0.05 which should result in cleaner plots (meaningisoforms only contributing minimally to the parent gene expression are now omitted from plot).
Specifically the package maintenance changes are:
All around speed improvements mainly due to updates regarding two bottelnecks:
stringr::str_c replaces paste0 since it is up to 10x faster on data.frames
dplyr::inner_join() or dplyr::left_join() have replaced most base::merge() opperations since since they are up to 10x faster.
All documentation and examples are now based on Salmon data. Cufflinks is shown as a special case.
For this switch new example data was included in the package.
Directy suppor of Cufflinks/Cuffdiff files via the cummeRbund R package (via the importCufflinksCummeRbund function) have been removed. Use importCufflinksFiles() instead.
analyzeSignalP, analyzePFAM, analyzeCPAT now better handles empty files.
All documentation regarding PFAM was updated to use EBI’s homepage (and their restrictions).
Updated package title to reflect the introduction of the alternative splicing module
A requirement for tximport >= 1.8.0 was introduced (due to problems with importing from RSEM in previous versions)
Highligting that import of GTF files can be done from both unziped and gziped gtf files.
Updated NEWS file to follow bioconductor style guideline
Genral update to support condition (and covariate) names compatible with model building in R.
All general support functions (potentially) used more than once place were moved to tool.R and names were streamlined.
Various updates in vignette to reflect all changes desribed above as well as update of installation instructions.
Various updates to input testing to catch commonly occuring problems.
Correction of loads of spelling mistakes kindely pointed out by @afonsoguerra - thanks!
Changes in version 1.9.5:
FIX
Changes in version 1.9.4:
Changes in version 1.9.3:
Changes in version 1.9.2:
Changes in version 1.9.1:
MAJOR
Improve clustering in isoNetwork plot.
Use varianceStabilizingTransformation to normalize counts.
Use a less common column as ID for samples in isoPLot fns.
Add updateIsomirDataSeq to be compatible with previous versions.
Adapt all functions to new object. Fix documentation.
Reduce object size and structure
Changes in version 1.0.0:
Changes in version 3.38.0:
New function plotExonJunc() to plot results from diffSplice().
New function logsumexp().
New argument hl.col for volcanoplot(), allowing users to specify the color for the gene names when highlight > 0.
barcodeplot() no longer assumes that ‘statistic’ has unique names. Previously it returned an error if names(statistic) contained any duplicated values.
The colors “blue”, “red” and “yellow” used by coolmap() changed to “blue2”, “red2” and “yellow2” when used in a color panel with white.
goana.Rd now explains more explicitly that p-values are unadjusted for multiple testing.
arrayWeights.Rd now mentions minimum dimensions for expression object.
More advice on how to choose ‘lfc’ added to the treat() help page.
Minor bug fix to the mixed p-value from roast() and mroast() when set.statistic=”floormean”.
Bug fix for cumOverlap(), which was under-counting overlaps in some cases.
Changes in version 0.99.17:
Changes in version 0.99.16:
Changes in version 0.99.15:
Changes in version 0.99.14:
Changes in version 0.99.13:
Changes in version 0.99.12:
Changes in version 0.99.11:
Changes in version 0.99.10:
Changes in version 0.99.9:
Changes in version 0.99.8:
Changes in version 0.99.7:
Changes in version 0.99.6:
Changes in version 0.99.5:
Added VignetteBuilder and VignetteEngine
The vignette set as BiocStyle
Changes in version 0.99.4:
Changes in version 0.99.3:
Changes in version 0.99.2:
Changes in version 0.99.1:
Changes in version 0.99.0:
Changes in version 1.8.0:
NEW FUNCTIONS
OncogenicPathways - Perform enrichment for known oncogenic pathways from TCGA studies.
PlotOncogenicPathways - Plots OncogenicPathways results
drugInteractions - Drug gene interactions from DGIB database.
SIGNIFICANT USER-LEVEL IMPROVEMENT
trinucleotideMatrix functions now works with BSgenomes instead of time consuming fasta files
rainfallPlot now detects Kataegis based on a greedy algorithm
Changes in version 1.1.9:
Add parameter ‘pathway_limit’ / ‘limit’ / ‘gmtpath’ in FluteRRA, FluteMLE, and all enrichment functions which enable users to customize gene sets for enrichment analysis.
Remove DAVID and GOstats, which are not recommended.
Add enrichment score in enrichment results from all algorithms.
Changes in version 1.1.6:
Add functions to plot figures in NatureProtocol manuscript.
Shorten the check time.
Remove null figures in pathview part.
Changes in version 1.1.1:
Speed up the enrichment analysis.
Release memory in time.
Changes in version 1.7.6:
NEW FEATURES
Added ‘apply’ methods for ‘sparse_mat’ and ‘virtual_mat’
Added ‘vm_used’ function to exported internal utilities
Can infer length of ‘matter_vec’ from paths when missing
Can coerce ‘matter_list’ to ‘matter_matc’ or ‘matter_matr’ if all elements of the list are the same length
SIGNIFICANT USER-VISIBLE CHANGES
BUG FIXES
Fixed bug where subsetting ‘sparse_mat’ objects would pull ‘matter_list’ key-value pairs into memory
Subsetting ‘sparse_mat’ objects with out-of-bounds subscripts when ‘drop=NULL’ is now an error
Changes in version 1.7.5:
NEW FEATURES
Changes in version 1.7.4:
NEW FEATURES
Changes in version 1.7.3:
NEW FEATURES
Changes in version 1.7.2:
NEW FEATURES
Updated installation instructions for BiocManager
Setting ‘sparse_mat’ keys also updated nrows/ncols
BUG FIXES
Changes in version 1.7.1:
BUG FIXES
Changes in version 1.7.1:
Changes in version 1.11.3 (2018-10-22):
Changes in version 1.11.2 (2018-10-15):
remove Makefile from directory vignettes/
check if package passes R CMD build and R CMD check without any error messages and vignette can be run without any errors
Changes in version 1.3.1:
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.5.1:
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.7.10:
IMPROVEMENTS AND BUG FIXES
Changes in version 1.7.9:
IMPROVEMENTS AND BUG FIXES
Changes in version 1.7.8:
IMPROVEMENTS AND BUG FIXES
Changes in version 1.7.7:
IMPROVEMENTS AND BUG FIXES
Changes in version 1.7.6:
NEW FUNCTIONS AND FEATURES
initialize.on.subset
to subset data
for initialization of mixture modeling; update description; add testsChanges in version 1.7.4:
IMPROVEMENTS AND BUG FIXES
Changes in version 1.7.3:
IMPROVEMENTS AND BUG FIXES
Changes in version 1.7.2:
NEW FUNCTIONS AND FEATURES
IMPROVEMENTS AND BUG FIXES
fix bug in methSeg: when joinSegments was activated, diagnostic plot would always be plotted
fixes in selectByOverlap() function: update description, to show that any methylKit object (tabix or not) can be used with it ; fixed broken method after @subjectHits was not available anymore
fix error in .checkTabixFileExists function, that lead to overwriting of files
Changes in version 0.99.24 (2018-10-22):
Changes in version 0.99.23 (2018-10-16):
Changes in version 0.99.20 (2018-08-06):
Changes in version 0.99.19 (2018-07-26):
Changes in version 0.99.18 (2018-07-19):
Changes in version 0.99.17 (2018-07-19):
Changes in version 0.99.15 (2018-07-16):
Changes in version 0.99.14 (2018-07-14):
Changes in version 0.99.13 (2018-07-12):
Changes in version 0.99.12 (2018-07-12):
use BiocStyle package for vignette
remove Makefile
use BiocParallel instead of parallel, for instance use bplapply instead of mclapply
Changes in version 0.99.11 (2018-07-03):
Changes in version 0.99.10 (2018-07-03):
Changes in version 0.99.9 (2018-06-26):
Changes in version 0.99.8 (2018-06-26):
Changes in version 0.99.6 (2018-06-13):
use camelCaps for functions
use no spaces between ‘=’ and named arguments
fix typo in lasso function
Changes in version 0.99.5 (2018-06-12):
Changes in version 0.99.4 (2018-06-12):
Changes in version 0.99.3 (2018-06-11):
Changes in version 0.99.2 (2018-06-11):
Changes in version 0.99.1 (2018-06-11):
remove bugs that there are no WARNINGS and ERRORs when running R CMD check and R CMD BiocCheck
reduce file size of peaklist_example.RData
submit to Bioconductor
Changes in version 0.99.0 (2018-05-14):
implement functionality to calculate statistical models of correlation (Pearson, Spearman), LASSO, Random Forest, Context likelihood or relatedness network algorithm, algorithm for the reconstruction of accurate cellular networks, constraint-based structure learning algorithm
implement the function create_statistical_network to calcululate the consensus matrix from the different statistically-infered networks
implement the function create_structural_network to calculate molecular weight differences and create a network
implement the function combine_structural_statistical to combine the structurally-derived and statistically-derived network
implement model partial and semi-partial pearson/spearman correlation using the ppcor package
Changes in version 1.27:
v1.27.2 Fix bug in preprocessQuantile() that arose when checking input for previous preprocessing method. Thanks to @DelnazR for the report (<URL: https://github.com/hansenlab/minfi/issues/165>).
v1.27.3 Fixed bug related to switch A and B for SNPs of type I when using convertArray / combineArrays. Reported by Jenny van Dongen.
v1.27.3 Fixed error in dmpFinder.
Added preliminary support for HorvathMammalMethylChip40.
Changes in version 0.99.28:
Changes in version 0.99.27:
Changes in version 0.99.26:
Changes in version 0.99.25:
Changes in version 0.99.24:
Changes in version 0.99.23:
Changes in version 0.99.15-0.99.22:
Changes in version 0.99.8-0.99.14:
Changes in version 0.99.4-0.99.7:
Changes in version 0.99.3:
Changes in version 0.99.2:
Changes in version 0.99.1:
Changes in version 0.99.0:
Changes in version 1.7.5:
Changes in version 1.7.4:
Changes in version 1.7.3:
Changes in version 1.7.2:
Changes in version 1.7.1:
Changes in version 1.7.0:
Changes in version 1.15.2:
Updated getMappedEntrezIDs, gometh and gsameth to to speed up execution by taking the array annotation in as an optional argument.
missMethyl now uses the latest IlluminaHumanMethylationEPICanno.ilm10b2.hg19 annotation by default for EPIC arrays.
Changes in version 1.15.1:
Added getAdjusted function for extracting RUVm adjusted data for visualisation purposes
Updated vignette to demonstrate use of getAdjusted function
Vignette now includes an example of how to handle cases with RUVm where number of samples is greater than number of Illumina negative controls
Changes in version 1.5.4:
Changes in version 1.5.3:
Changes in version 1.5.2:
scoreSequence, scoreHistogram, scoreProfile motifHits, motifHitProfile can be used with N-containing sequences. Positions at which the motif overlaps with N’s will be NaN.
Background can be computed from a DNAString in addition to a DNAStringSet object.
Changes in version 1.5.1:
Changes in version 1.24:
NEW FEATURES
query method now flexible, with “andStrings”, “orStrings”, “notStrings” parameters The previous usage style is still supported. See man page.
associateTranscriptionFactors (with motifs) substantially faster
Changes in version 1.25.2:
Changes in version 1.25.1:
Changes in version 0.99.0 (2018-08-07):
Changes in version 2.7.12:
Fix warnings on windows (see #371) <2018-10-26 Fri>
Add parameter ppm to consensusSpectrum and meanMzInts (see #373 for details) <2018-10-26 Fri>
Changes in version 2.7.11:
Change default for timeDomain
in combineSpectra
and
combineSpectraMovingWindow
to FALSE
<2018-10-18 Thu>
Add new spectra combination function consensusSpectrum
<2018-10-24
Wed>
Amend plot,Spectrum 1 and 2 (see #369)
Changes in version 2.7.10:
Changes in version 2.7.9:
Import rather than depend on BiocParallel <2018-10-15 Mon>
Fix failing test on Windows (requiring normalizePath) <2018-10-15 Mon>
Changes in version 2.7.8:
MGF exporter gets a new addFields
argument (see PR #362)
<2018-10-12 Fri>
New Spectra
(SimpleList
of Sepctrum
objects) (see PR #361)
<2018-10-13 Sat>
Changes in version 2.7.7:
Changes in version 2.7.6:
Changes in version 2.7.5:
Changes in version 2.7.4:
Changes in version 2.7.3:
Fix bug in robust summary (see PR #349) <2018-07-28 Sat>
Fix failing unit test <2018-07-28 Sat>
Changes in version 2.7.2:
Handle files without any spectra - see #342 <2018-05-15 Tue>
New mergeFeatureVars
and expandFeatureVars
functions <2018-05-30
Wed>
Update plot,Spectrum methods to match the tolerance and relative arguments (see #350) <2018-06-29 Fri>
Changes in version 2.7.1:
Changes in version 2.7.0:
Changes in version 1.15.1:
Changes in version 1.6.1:
Changes in version 0.99.0 (2018-09-21):
Changes in version 1.11.2:
Changes in version 1.11.1:
Changes in version 2.15.5:
Changes in version 2.15.4:
Use new dependency ncdf4 for netCDF reading, removes a lot of build hassles with old libnetcdf-dev linking.
specParams returns a numeric scan.number.s.
Changes in version 2.15.3:
Changes in version 2.15.2:
Add header column ionMobilityDriftTime to report the corresponding CV parameter (issue https://github.com/sneumann/mzR/issues/44).
Ensure ion injection time is always reported in milliseconds.
Replace BiocInstaller::biocLite with BiocManager::install (by Bioc core)
Changes in version 2.15.1:
Fix typo (see https://github.com/sneumann/mzR/pull/162)
New .hasSpectra and .hasChromatograms private function (see https://github.com/lgatto/MSnbase/issues/343)
Fix bug in score when more cvParams than expected are read - see https://github.com/sneumann/mzR/issues/136 <2018-05-26 Sat>
Changes in version 2.15.0:
Changes in version 0.99.4:
Changes in version 0.99.24:
Changes in version 0.99.22:
Changes in version 0.99.21:
Changes in version 0.99.20:
Changes in version 0.99.19:
Changes in version 0.99.17:
Changes in version 0.99.16:
Crash when feeding SummarizedExperiment as input is fixed
If full rows contains only NA values they are filtered and a warning is generated
Changes in version 0.99.15:
Corrected expected output from vapply in correlation matrix (where previous varying lengths caused crash)
Clearer error message when providing invalid sample/group conditions to statistics module
Issue where several identical RT-values caused crash in RT-slicing fixed
Changes in version 1.1.2:
bug fix for CZM in RAB plots. if not zeros, plots would fail.
updated install instructions to use BiocManager
Changes in version 1.1.1:
added slider inputs to change size and opacity of sample names for biplot and coloured biplot
changed UI for filtering page
added downloadable scripts for PCA biplots, dendrogram/barplots, and effect plots (simple effect plot script).
Changes in version 2.11.1:
Changes in version 0.99.7:
MODIFICATIONS
randomisation of batches now implemented via the randBatch helper function
updated references
updated vignette
Changes in version 0.99.5:
MODIFICATIONS
replace date field with hard-coded date in vignette
updating data.frame rows by group now uses ‘split-apply-combine’
vectorise part of the LOND function
replace 1:N with seq_len(N)
applied consistent formatting & indentation
added new tests
Changes in version 2.0.1:
Changes in version 2.0.0:
Changes in version 1.1.12:
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.8.1:
Update data uploading
Modify visualization
Add biomarker tab
Changes in version 1.11.2 (2018-06-29):
Updated NEWS file of pathVar
Updated maintainer email address
Changes in version 1.11.1 (2018-05-15):
Changes in version 2.8.0:
NEW FEATURES
Changes in version 1.11.4:
Changes in version 1.11.2:
Changes in version 1.1.6:
Changes in version 1.1.5:
PCA plot uses annotation color scheme
Rename annotation column implemented
log2(1 + x) adjust tool added
Calculated annotation revamped
Annotate dataset revamped and moved to file
Session syncing improved.
Sweep adjustment implemented
Changes in version 1.1.3:
Option to get vertical GSEA plot
Showing heatmap along the GSEA plot
Changed filter scheme
Better session synching between fronend and backend
Changes in version 1.1.2:
Moved to protobuf 3.4 in docker (supports messages up to 2GB)
Set exact match to be default
Set row profile as the default chart
Changes in version 1.1.1:
Fixes in PCA plot
GSEA plotCHANGES IN VERSION 0.99.34
Detecting conditions and replicates in GEO data
Changes in version 1.22.0:
DOCUMENTATION
Changes in version 1.20.1:
DOCUMENTATION
Changes in version 1.7.2 (2018-05-22):
General
Changes in version 0.99.37:
New features
Changes in version 0.99.24:
New features
Changes in version 0.99.15:
New features
Changes in version 0.99.14:
Minor improvements and bug fixes
vapply()
instead of sapply()
for safer code.Changes in version 1.99.3:
NB function now exported
note that version 1.99.3 on GitHub was version 1.1.0 on Bioconductor.
Changes in version 1.99.2:
Changes in version 1.1.1:
Changes in version 1.1.0:
Changes in version 0.99.7:
Changes in version 1.21.9:
Fix type in vignette header <2018-09-18 Tue>
Fix bug in plot method for ThetaRegRes object <2018-09-24 Mon>
Changes in version 1.21.8:
fcol
argument to plotDist
to plot and colour all profiles
<2018-08-09 Thu>Changes in version 1.21.7:
Use BiocManager in vignette
Fix bug in plot2D: pass … to hexbin <2018-08-02 Thu>
Changes in version 1.21.6:
Changes in version 1.21.5:
Changes in version 1.21.4:
Fix bugs in tagmMcmcPredict, where fcol was ignored <2018-06-05 Tue>
Order vignettes by prefixing the files with numbers <2018-06-05 Tue>
Changes in version 1.21.3:
Changes in version 1.21.2:
Changes in version 1.21.1:
Fix bug in higlightOnPlot with missing fcol (see #105) <2018-05-03 Thu>
New TAGM-MAP generative model, contributed by Oliver Crook <2018-05-18 Fri>
New plotEllipse
function to visualise and assess TAGM models
<2018-05-18 Fri>
Changes in version 1.12.0:
NEW FEATURES
normalDB does not need input normal coverage files anymore after creation (so the resulting normalDB.rds file can be moved)
base quality filtering can be turned off by setting min.base.quality to 0 or NULL
possible to change the POP_AF info field name
possible to change POP_AF cutoff to set a high germline prior
possible to change min.cosmic.cnt and max.homozygous.loss in PureCN.R
set number of cores in PureCN.R (thanks Brad)
SIGNIFICANT USER-VISIBLE CHANGES
renamed reptimingbinsize to reptimingwidth in IntervalFile.R, added this feature to preprocessIntervals
clarified “targets” vs. “intervals”; whenever something affects both on-target and off-target, it is now called “intervals”. When only targets, e.g. in annotateTargets, “targets” was kept.
made gc.gene.file defunct
new default for min.cosmic.cnt = 6 (instead of 4)
BUGFIXES
catch various input problems and provide better error messages instead of crashing
stranded input BED files do not cause problems anymore
fixed a bug when only a single local optimum was tested (happens only when users copy the examples that restrict the search speach to avoid long runtimes)
added missing QC flag to predictSomatic VCF annotation
Changes in version 0.99.0:
Changes in version 1.7.4:
Changes in version 1.7.3:
Changes in version 1.7.2:
Changes in version 1.7.1:
Changes in version 2.2.0:
New functions to remove duplicate edges - deleteDuplicateEdges - deleteSelfLoops
New node selection function - selectNodesConnectedBySelectedEdges
New visual style management functions - importVisualStyles - deleteVisualStyle - deleteStyleMapping
New edge bundling function - bundleEdges
New custom graphics options for nodes - setNodeCustomBarChart - setNodeCustomBoxChart - setNodeCustomHeatMapChart - setNodeCustomLineChart - setNodeCustomPieChart - setNodeCustomRingChart - setNodeCustomLinearGradient - setNodeCustomRadialGradient - setNodeCustomPosition - removeNodeCustomGraphics
New filter functions - applyFilter - createColumnFilter - createCompositeFilter - createDegreeFilter - getFilterList - exportFilters - importFilters
Improved speed on bulk node and edge property bypasses
Bug Fixes - selectEdgesConnectingSelectedNodes – set default by.col = ‘name’ - setEdgeLineWidthMapping – fixes input type - getGroupInfo – works without collapsing first - getTableColumns – work with List type columns
For Developers - Updated many functions to properly pass the base.url parameter to functions like getNetworkSuid. Please be aware and vigilent about this with future development. - Adopted use of seq_len(). Please be aware and vigilent. - Replaced all but one case of sapply() with vapply().
Deprecated - Nothing
Defunct - Previously deprecated functions in v2.0 from older 1.x version of the package
Changes in version 1.25.1:
Changes in version 0.99.0 (2018-07-12):
Changes in version 1.7.5:
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.7.4:
BUG FIXES
Changes in version 1.7.3:
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.7.2:
BUG FIXES
Changes in version 1.7.1:
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.13.2:
BUG FIXES
Changes in version 1.13.1:
NEW FEATURES
Changes in version 1.15.4:
BUG FIXES
Changes in version 1.15.3:
BUG FIXES
Fixed an issue with DESeq2Exploration.Rmd that affected both DESeq2Report and edgeReport. This should also fix the recount bioc-release (3.7) and bioc-devel (3.8) branches.
Fixed a NAMESPACE issue with rmarkdown::html_document and BiocStyle::html_document
Changes in version 1.15.2:
SIGNIFICANT USER-VISIBLE CHANGES
Changes in version 1.15.1:
BUG FIXES
Changes in version 1.13.1:
plotRegionGeneAssociationGraphs()
: par(mfrow)
is automatically
set according to the length of type
.Changes in version 0.99.25:
Changes in version 0.99.24:
Changes in version 0.99.23:
Changes in version 1.99.0:
RnBeadsDJ: updated documentation and tooltips
deactivated intersample plots (exploratory module) as default option for BS-seq data
Roxygen documentation updates
Extended methods descriptions in reports (imputation, filtering, differential, …)
Miscellaneous bugfixes and performance improvements in RnBeadsDJ, imputation, …
Changes in version 1.13.3:
added CNV estimation using the GLAD package to QC module
some bugfixes
changed ‘gender’ to ‘sex’, affected functions and options are ‘rnb.execute.gender.prediction’ and ‘import.gender.prediction’
Changes in version 1.13.2:
Changes in version 1.13.1:
Changes in version 2.9.4:
Changes in version 2.9.3:
Changes in version 2.9.2:
Changes in version 2.9.1:
Changes in version 2.9.0:
Changes in version 1.13.8:
INTERNAL MODIFICATION
Changes in version 1.13.6:
INTERNAL MODIFICATION
Changes in version 1.13.4:
INTERNAL MODIFICATION
Changes in version 1.13.2:
BUG CORRECTION
INTERNAL MODIFICATION
Changes in version 1.17.2:
Changes in version 1.17.1:
Changes in version 1.17.0:
Changes in version 1.33:
NEW FEATURES
BUG FIXES
(v 1.33.1) Do not try to grow NULL (not-yet-encountered) tags (https://support.bioconductor.org/p/110609/ ; Robert Bradley)
(v 1.33.5) Check for corrupt index (https://github.com/Bioconductor/Rsamtools/issues/3 ; kjohnsen)
Changes in version 1.32.0:
New function flattenGTF() that merges overlapping features into a single interval.
New parameter for align() and subjunc(): sortReadsByCoordinates.
New parameters for featureCounts(): readShiftType, readShiftSize and additionalAttributes.
Specify strand protocol for each library individually in featureCounts().
Much improved speed of align() and subjunc().
align() and subjunc() return mapping statistics.
Default setting of buildindex() is changed to building a one-block full index.
Changes in version 2.6.0:
Changes in version 1.6.0:
Changes in version 0.20.0
NEW FEATURES
rbind() now supports DataFrame objects with the same column names but in different order, even when some of the column names are duplicated. How rbind() re-aligns the columns of the various objects to bind with those of the first object is consistent with what base:::rbind.data.frame() does.
Add isSequence() low-level helper.
Add ‘nodup’ argument to selectHits().
SIGNIFICANT USER-VISIBLE CHANGES
The rownames of a DataFrame are no more required to be unique.
Change ‘use.names’ default from FALSE to TRUE in mcols() getter.
Coercion to DataFrame now always propagates the names.
Rename low-level generic concatenateObjects() -> bindROWS().
replaceROWS() now dispatches on ‘x’ and ‘i’ instead of ‘x’ only.
Speedup row subsetting of DataFrame with many columns.
DEPRECATED AND DEFUNCT
BUG FIXES
Fix window() on a DataFrame with data.frame columns.
2 fixes to “rbind” method for DataFrame objects: + It now properly handles DataFrame objects with duplicated colnames. Note that the new behavior is consistent with base::rbind.data.frame(). + It now properly handles DataFrame objects with columns that are 1D arrays.
Fix showAsCell() on nested data-frame-like objects.
2 fixes to “as.data.frame” method for DataFrame objects: + It now works if the DataFrame object contains nested data-frame-like objects or other complicated S4 objects (as long as these complicated objects in turn support as.data.frame()). + It now handles ‘stringsAsFactors’ argument properly. Originally reported here: https://github.com/Bioconductor/GenomicRanges/issues/18
Changes in version 1.10.0:
Fixes to all violin plots to ensure scatter matches up with violin outlines.
Rectangle categorical/categorical plots collapse to mirrored bar plots when either factor contains only one level.
Removed scater_gui(), downsampleCounts(), read10xResults(), normalizeExprs().
Simplified plotRLE() to avoid the need for internal faceting.
Added option for row subsetting in librarySizeFactors().
Ensured calcAverage() with subset_row= behaves as if the matrix was subsetted prior to the function call. Added support for parallelization.
Ensured calculateCPM() with subset_row= behaves as if the matrix was subsetted prior to the function call.
Added support for parallelization in nexprs().
Added readSparseCounts() for creating a sparse matrix from a dense array on file.
Added normalizeCounts() for easy division of matrix columns by the size factors. Modified to throw error upon encountering negative, NA or zero size factors.
Added preserve_zeroes= option to normalizeSCE() for preserving sparsity with non-unity pseudo-counts.
Added runUMAP() and plotUMAP() to use the UMAP dimensionality reduction method.
Added plotExplanatoryPCs() and getExplanatoryPCs() to correlate PCs with known factors. Deprecated findImportantPCs().
Added getVarianceExplained() to get the variance in gene expression explained by known factors.
Removed runKallisto() and runSalmon().
Switched readTxResults() to use tximport. Switched readSalmonResults() and readKallistoResults() to use readTxResults().
Removed obsolete fields in calculateQCMetrics(). Moved processing into C++ for a single-pass algorithm. Supported parallelization across cells for QC computations.
Added sumCountsAcrossFeatures() to sum counts across multiple redundant features. Deprecated summariseExprsAcrossFeatures().
All plotting functions can now access internal fields by using a character vector with NA as the first element.
Returned threshold values in the attributes of the output from isOutlier().
Deprecated the ticks in plotReducedDim().
Changes in version 0.99.1:
Modify description part in DESCRIPTION file.
Change seq_len(length(x)) to seq_along(x) in sageTestNew.R function.
Changes in version 1.1.7:
PKG FEATURES
methylationDist function produces more interpretable mean methylation values
In addition to the report file QC_Summary text file is generated. Also mbias table and downsample table are stored as text file
Added “all” option in functions that utilizes subsampling such that given “all” option certain function would be applied to all the CpGs without any subsample or offset
Changes in version 1.3.8:
Changes in version 1.3.6:
Changes in version 1.3.5:
Changes in version 1.3.4:
distance_to_end
back.Changes in version 1.3.2:
Added gzipped output for sc_trim_barcode()
, if output filename ends
with .gz
then gzipped output will be produced.
Added get_read_str()
function for getting common read structures.
Changes in version 1.3.1:
sc_exon_mappping
function so it can accept multiple bam
files now, together with a list of cell id or cell barcode.Changes in version 1.10.0:
Removed selectorPlot(), exploreData() functions in favour of iSEE.
Fixed underflow problem in mnnCorrect() when dealing with the Gaussian kernel. Dropped the default sigma= in mnnCorrect() for better default performance.
Supported parallelized block-wise processing in quickCluster(). Deprecated max.size= in favour of max.cluster.size= in computeSumFactors(). Deprecated get.ranks= in favour of scaledColRanks().
Added max.cluster.size= argument to computeSumFactors(). Supported parallelized cluster-wise processing. Increased all pool sizes to avoid rare failures if number of cells is a multiple of 5. Minor improvement to how mean filtering is done for rescaling across clusters in computeSumFactors(). Throw errors upon min.mean=NULL, which used to be valid. Switched positive=TRUE behaviour to use cleanSizeFactors().
Added simpleSumFactors() as a simplified alternative to quickCluster() and computeSumFactors().
Added the scaledColRanks() function for computing scaled and centred column ranks.
Supported parallelized gene-wise processing in trendVar() and decomposeVar(). Support direct use of a factor in design= for efficiency.
Added doubletCluster() to detect clusters that consist of doublets of other clusters.
Added doubletCells() to detect cells that are doublets of other cells via simulations.
Deprecated rand.seed= in buildSNNGraph() in favour of explicit set.seed() call. Added type= argument for weighting edges based on the number of shared neighbors.
Deprecated rand.seed= in buildKNNGraph().
Added multiBlockNorm() function for spike-abundance-preserving normalization prior to multi-block variance modelling.
Added multiBatchNorm() function for consistent downscaling across batches prior to batch correction.
Added cleanSizeFactors() to coerce non-positive size factors to positive values based on number of detected genes.
Added the fastMNN() function to provide a faster, more stable alternative for MNN correction.
Added BPPARAM= option for parallelized execution in makeTechTrend(). Added approx.npts= option for interpolation-based approximation for many cells.
Added pairwiseTTests() for direct calculation of pairwise t-statistics between groups.
Added pairwiseWilcox() for direct calculation of pairwise Wilcoxon rank sum tests between groups.
Added combineMarkers() to consolidate arbitrary pairwise comparisons into a marker list.
Bugfixes to uses of block=, lfc= and design= arguments in findMarkers(). Refactored to use pairwiseTTests() and combineMarkers() internally. Added BPPARAM= option for parallelized execution.
Refactored overlapExprs() to sort by p-value based on pairwiseWilcox() and combineMarkers(). Removed design= argument as it is not compatible with p-value calculations.
Bugfixes to the use of Stouffer’s Z method in combineVar().
Added combinePValues() as a centralized internal function to combine p-values.
Changes in version 1.1.2:
Changes in version 1.1.1:
allow adjustment of covariates;
update ‘SDA’ with additional arguments passed to ‘qvalue’.
Changes in version 1.21.1-1.21.7:
UTILITIES
avoid duplicated meta-information lines in seqVCF2GDS()
and
seqVCF_Header()
require >= R_v3.5.0, since reading from connections in text mode is buffered
seqDigest()
requires the digest package
optimization in reading genotypes from a subset of samples (according to gdsfmt_1.17.5)
NEW FEATURES
seqSNP2GDS()
imports dosage GDS files
seqVCF_Header()
allows a BCF file as an input
a new function seqRecompress()
a new function seqCheck()
for checking the data integrity of a
SeqArray GDS file
seqGDS2SNP()
exports dosage GDS files
BUG FIXES
seqVCF2GDS()
and seqVCF_Header()
are able to import site-only VCF
files (i.e., VCF with no sample)
fix seqVCF2GDS()
and seqBCF2GDS()
since reading from connections
in text mode is buffered for R >= v3.5.0
Changes in version 1.20.1:
BUG FIXES
seqExport()
fails to export haploid data (e.g., Y chromosome)
seqVCF2GDS()
fails to convert INFO variables when Number=”R”
Changes in version 1.4.0:
FEATURES
Add convenience functions for creating and reading multiple SNV profiles
Add functionality for reading general COSMIC mutational data, not just cell line mutational data
FIXES
MISCELLANEOUS:
Changes in version 1.0.0:
Changes in version 1.7.1:
Changes in version 1.13.1:
Changes in version 0.99.9:
put return statement in code at the end.
instead of writing at each line, you paste output lines in a single paste call and then use a single write call.
use a package BiocManager instead of biocLite in the vignettes.
remove a few eval=FALSE sections in vignette to ensure vignette to be evaluated.
Changes in version 0.99.8:
Modify vignette output to BiocStyle .
Add some information in README file.
Changes in version 0.99.6:
Adjust the code format to follow the coding style.
Generate NEWS file in .Rd form.
Add some information in README.md, vignettes and DESCRIPTION file to make the package SIMD be more understandable.
Changes in version 0.99.5:
Changes in version 0.99.4:
Changes in version 0.99.3:
Modify some problems in R biocheck.
Recompile .Rd by roxygen2 package in R.
Changes in version 0.99.2:
Modify some problems in R biocheck.
Change the NEWS format.
Changes in version 0.99.1:
Modify the problem in R check.
To get the vignettes by R Markdown instead of by Sweave.
Add NEWS.md in package.
Changes in version 1.7.3 (2018-10-13):
Changes in version 1.15.1:
Updated getTrueModel, zmatrix, simTime to include additional covariates as predictors
Simplified examples
Changes in version 1.4.0:
Allow … arguments to be passed to rowData() and colData().
Added weights() methods for getting/setting observational weights.
Added reducedDimNames<- method to set the names of reduced dimension slots.
Added withDimnames= argument to reducedDim() and reducedDims().
Exported getters and setters for internal metadata fields.
Added developer instructions for making use of internal metadata fields.
Changes in version 1.1.26 (2018-10-23):
New UI design for the Differential Expression tab.
New UI design for the Data Summary & Filtering tab.
Support for additional assay modification including log transforming any assay and renaming assays.
New function visPlot for creating scatterplots, boxplots, heatmaps, and barplots for custom gene sets.
The Downsample tab now works on a generic counts matrix
You can upload a SCtkExperiment object or a SingleCellExperiment object saved in an RDS file on the Upload tab.
Differential Expression results can now be saved in the rowData of the object and loaded for later analysis.
Improved ability to save a biomarker based on user options.
The Differential Expression plot is not automatically created, for more user control with large datasets.
Changes in version 1.1.3:
Improvements to plotting, change text size and hide labels in gsva plots.
MAST violin and linear model plots are now more square when plotting less than 49 facets.
Changed y axis label in plotBatchVariance to “Percent Explained Variation”
Changes in version 1.1.2:
Changes in version 1.1.1:
Changes in version 0.99.0:
NEW FEATURES
Changes in version 1.15.1-1.15.5:
a new option ‘useMatrix’ to allow for the packed symmetric matrix
using the Matrix package in snpgdsIBDMoM()
, snpgdsIBDKING()
and
snpgdsIBS()
fix a bug of missing sample and SNP IDs in the output of
snpgdsIndInb()
new option ‘start.pos’ in snpgdsLDpruning()
new methods in snpgdsIndInb()
: gcta1, gcta2, gcta3; progress
information is shown during running the function
snpgdsCombineGeno()
supports dosages
Changes in version 1.1.4:
Changes in version 1.1.3:
Changes in version 2.0:
NEW FEATURES
New GUI o Mouse Hover for help information o .log file
New Signal correction o QC-RFSC methods for metabolomics and proteomics data
New feature slection o Random Forest and the Permutation based variable importance measures o new MDSplot for Random Forest o P-value based importance plot
New data preprocessing o PQN/SUM/none normalization o center/none Scaling method
Changes in version 0.99.16:
Changes in version 1.12.0:
NEW FEATURES
SIGNIFICANT USER-VISIBLE CHANGES
BUG FIXES
Changes in version 1.11.7:
UPDATE
Changes in version 1.11.6:
BUG FIXES
Changes in version 1.11.5:
UDPATE
Changes in version 1.11.4:
BUG FIXES
Changes in version 1.11.3:
BUG FIXES
Changes in version 1.11.2:
NEW FEATURES
Changes in version 1.11.1:
BUG FIXES
Changes in version 1.11.0:
NEW FEATURES
Changes in version 2.5.2:
BiocManager::install
[2018-07-16]. # Synapter 2.3Changes in version 1.38.0:
NEW FEATURES
New function checkRimLim
to vizualise a retention index markers
before the actual time correction. It can be useful to fix the search
limits.
Peak detection method (NetCDFPeakFinding) has the option to use a gaussian smoothing in addition to usual moving average.
Detects if CDF files are not found during sample description import. In addition, search for column names matching a pattern if the expected names are not found.
Add support for a custom CDF file for faster data retrieval.
SIGNIFICANT USER-VISIBLE CHANGES
massRange
(m/z mass range) which used to be needed in
some functions is deprected. It was used mostly as a hint and usually
detected automatically. If it is passed, there would be no effect.BUG FIXES
Big refactor of C code to eliminate duplicated code and to separate what is R-C code (ie, SEXP structs) out of the C code that actually does something.
General R code refactoring and housekeeping.
Changes in version 1.21.3:
fixed bug (order of outputs from devel version of baySeq cannot be sorted) in ‘.testByBayseq’.
disabled ‘samseq’ option, since samr package has been removed from CRAN.
change design matrix used in DESeq2
Changes in version 1.1.1:
New Features:
The function GeneID2entrez now suports translation from mouse ENSEMBL gene IDs and MGI symbols to mouse Entrez gene IDs
76 new ChIP-Seq experiments added to the database
Changes in version 099.1:
SIGNIFICANT USER-VISIBLE CHANGES
INTERNALS
depends now on ProtGenerics from which it uses ‘mz’
exchanged various print() with message()
Changes in version 1.3.6:
Changes in version 1.3.5:
R CMD check
[2018-10-10].Changes in version 1.3.4:
Changes in version 1.3.3:
MSnbase 2.7.2
with internal
fragments; see #82 [2018-06-03].”Changes in version 1.3.2:
BiocManager::install
[2018-07-16].Changes in version 1.3.1:
Adapt to MSnbase 2.7.2
with internal fragments; see #82
[2018-06-03].
Fix FragmentViews
start/end/width and labels for internal fragments
[2018-06-03].
Fix as(tds, "MSnSet")
unit test [2018-07-06].
Use elementMetadata(..., use.names=FALSE)
in
combine,FragmentViews,FragmentViews-method
to avoid duplicated
rownames in elementMetadata slot [2018-07-06].
Changes in version 1.3.0:
Changes in version 1.17.8:
Changes in version 1.17.7:
Changes in version 1.17.6:
Changes in version 1.17.4:
Changes in version 1.17.3:
Changes in version 1.17.2:
Changes in version 1.17.1:
Changes in version 1.3.6:
Slot genesInTerm added to the Transcriptogram class.
Argument boundaryConditions from differentiallyExpressed(): default changed from FALSE to TRUE.
Argument onlyGenesInDE from clusterVisualization(): default changed from TRUE to FALSE.
Argument onlyGenesInDE from clusterEnrichment(): default changed from TRUE to FALSE.
Argument colors added to the differentiallyExpressed() method.
Argument colors added to the clusterVisualization() method.
Argument colors added to the enrichmentPlot() method.
Argument alpha added to the enrichmentPlot() method.
Changes in version 1.3.4:
Changes in version 1.3.3:
Changes on the clusterEnrichment() method return.
Slots Protein2GO, and Terms added to the Transcriptogram class.
New methods: enrichmentPlot() and Terms().
Changes in version 1.3.1:
Argument boundaryConditions added to the differentiallyExpressed() method.
Slots Protein2Symbol, clusters, and pbc added to the Transcriptogram class.
Argument onlyGenesInDE added to the clusterVisualization() method.
Argument onlyGenesInDE added to the clusterEnrichment() method.
Changes in version 0.99.0:
Changes in version 1.1.9 (2018-10-24):
moved some functionality to tRNA package
added dependency for tRNA package
Changes in version 1.1.1 (2018-08-16):
Changes in version 0.0.16:
Changes in version 1.9.11:
Changes in version 1.9.10:
Changes in version 1.9.9:
Changes in version 1.9.6:
Changes in version 1.9.4:
Changes in version 1.9.1:
Changes in version 0.99.0:
Changes in version 2.1.4 (2018-10-10):
Bioconductor compliance
Changes in version 2.1.3 (2018-10-03):
Bioconductor compliance
Changes in version 1.11.13:
Changes in version 1.11.11:
Changes in version 1.11.10:
Changes in version 1.11.8:
Check and stop() if response variable has variance of 0 - in dream(), fitExtractVarPartModel(), and fitVarPartModel()
add standardized_t_stat() implicitly in eBayes() using MArrayLM2 class - this transforms moderated t-statistics to have same degrees of freedom
Changes in version 1.11.7:
Simplify object return by dream to be more more similar to lmFit - now returns MArrayLM instead of MArrayLMM_lmer
if a fixed effects formula is specified (i.e. not random terms) - dream call lmFit in the backend - getContrast() works seamlessly
dream() now returns gene annotation if passed to function
Changes in version 1.11.6:
add error checing for L in dream
fix typoes in dream vignette
fix typoes in theory_practice_random_effects.Rnw
Changes in version 1.11.5:
Changes in version 1.11.2:
Changes in version 1.11.1:
Changes in version 1.28.0:
NEW FEATURES
Update package to support VCF format version 4.3 - SAMPLE field lines can now have key ‘SAMPLE’ or ‘META’. To avoid a name clash, the existing ‘META’ DataFrame has been split by row into separate DataFrames. The ‘meta(VCFHeader)’ getter now returns one DataFrame per unique key in the header. - PEDIGREE header line now begins with ‘ID’
Add vcfFields method for character, VCFHeader, VcfFile and VCF to return all available vcf fields in CharacterList().
Add support for single breakend notation (thanks d-cameron)
BUG FIXES
Changes in version 3.3.6:
Changes in version 3.3.5:
Changes in version 3.3.4:
Add featureChromatograms to extract ion chromatograms for each feature.
Add hasFilledChromPeaks function.
Add argument skipFilled to the featureSummary function.
Changes in version 3.3.3:
Add chromPeakSpectra and featureSpectra functions to extract MS2 spectra for chromatographic peaks and features, respectively (issue #321).
Fix profMat to handle also data files with empty spectra (issue #312).
Add argument ylim to plotAdjustedRtime (issue #314).
Add imputeRowMin and imputeRowMinRand, two simple missing value imputation helper functions.
Fix additional problem mentioned in issue #301 with obiwarp retention
time correction if some spectra have m/z values of NA
.
Fix issue #300 avoiding chromatographic peaks with rtmin > rtmax.
Fixes for issues #291, #296.
Add parameter ‘missing’ to diffreport allowing to replace NA with arbitrary numbers.
Add exportMetaboAnalyst function to export the feature matrix in MetaboAnalyst format.
Add parameter missing to featureValues allowing to specify how to handle/ report missing values.
The chromPeaks matrix has now rownames to uniquely identify chromatographic peaks in an experiment. Chromatographic peak IDs start with “CP” followed by a number.
Changes in version 3.3.2:
Add writeMSData method for XCMSnExp allowing to write mzML/mzXML files with adjusted retention times (issue #294).
Fix profEIC call for single-scan-peak (pull request #287 from @trljcl).
Fix centWave avoiding that the same peak is reported multiple times if fitgauss = TRUE is used (issue #284).
featureSummary reports also RSD (relative standard deviations) of features across samples (issue #286).
Add parameters fixedMz and fixedRt to FillChromPeaksParam that allow to increase the features’ m/z and rt widths by a constant factor.
Add option “sum” to featureValues’ method parameter allowing to sum the intensities of peaks that are assigned to the same feature in a file/sample.
Changes in version 3.3.1:
Add overlappingFeatures function to identify overlapping or close features.
Add support for type = “apex_within” for featureDefinitions.
Fix a bug in fillChromPeaks that would return the integrated signal being Inf.
Fix for issue #267: error in fillChromPeaks when the retention time of the peaks are outside of the retention time range of certain files.
New featureSummary function to calculate basic feature summaries (number of samples in which peaks were found etc).
Parameter ‘type’ added to plotChromPeakDensity and ‘whichPeaks’ to highlightChromPeaks. Both parameters are passed to the ‘type’ argument of chromPeaks.
Parameter ‘type’ in chromPeaks gets additional option “apex_within” to return chromatographic peaks that have their apex within the defined rt and/or m/z range.
Add functions rla and rowRla to calculate RLA (relative log abundances).
Add peaksWithMatchedFilter to perform peak detection in chromatographic (MRM/SRM) data (issues #277 and #278).
Add peaksWithCentWave to perform centWave peak detection in chromatographic (MRM/SRM) data (issue #279).
Add findChromPeaks,Chromatogram methods for CentWaveParam and MatchedFilterParam (issue #280).
Changes in version 1.0.0:
NEW FEATURES
BUG FIXES
Changes in version 1.41:
Changes in version 1.3.1 (2018-05-09):
New zinbsurf
function implements approximate method for large
matrices.
New option which_genes
in zinbwave
to specify which genes to use
to compute W
.
Changes in version 0.99.0 (2018-08-05):
Changes in version 1.0:
Changes in version 2.17:
USER VISIBLE CHANGES
Changes in version 0.1.0:
Changes in version 0.99.37 (2018-10-25):
Changes in version 0.99.36 (2018-03-24):
Submitted to Bioconductor
Added NEWS file
Changes in version 0.99.8 (2018-06-30):
Changed: GO.rda to org.Hs.eg.GO.rda
Added: vignettes
Changes in version 0.99.1 (2018-06-04):
Changes in version 1.0.1:
Changes in version 1.19.4:
Changes in version 1.19.3:
Changes in version 1.19.2:
Fix typo in beltran2016 man page <2018-07-24 Tue>
Added LOPIT-DC and hyperLOPIT U2OS data <2018-07-25 Wed>
Changes in version 1.19.1:
Changes in version 1.19.0:
Changes in version 2016-04-21:
Changes in version 0.99.0:
Changes in version 1.19.3:
Use BiocManager
Fix typo in rmd
Changes in version 1.19.0:
Changes in version 1.0.0:
Changes in version 0.99.0:
Changes in version 0.99.3:
PKG FEATURES
Changes in version 1.99.6:
some minor fixes to RLE section
minor text edits as suggested by James McDonald at F1000
modified to code chunk that checks for latex compilation
Changes in version 1.99.2:
Changes in version 0.99.0:
Changes in version 1.3.1:
SIGNIFICANT USER-VISIBLE CHANGES
Seven software packages were removed from this release (after being deprecated in Bioc 3.7): ontoCat, spliceR, OperaMate, DASC, PAnnBuilder, phenoDist, BrowserVizDemo.
Thirteen software are deprecated in this release and will be removed in Bioc 3.9: BiocInstaller, GoogleGenomics, IrisSpatialFeatures, facopy, gaucho, nudge, RamiGO, mQTL.NMR, cytofkit, pbcmc, GeneSelector, ampliQueso, prot2D.
Three experimental data packages were removed in this release (after being deprecated in BioC 3.7): RnaSeqTutorial, cheung2010, MEALData.
Two experimental data packages are deprecated in this release and will be removed in Bioc 3.9: iontreeData, MSBdata.