k3ylabs/cosoco-image-dataset huggingface dataset:
Package: r-bioc-graph (1.72.0-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/graph/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-biocgenerics (>= 0.13.11), libc6 (>= 2.4)
Description:  handle graph data structures for BioConductor This BioConductor module implements some simple graph handling capabilities.  These are for instance used in hypergraph module which in turn is used by several other BioConductor packages.
Package: r-bioc-grohmm (1.28.0-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/groHMM/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-cran-mass, r-bioc-s4vectors (>= 0.17.25), r-bioc-iranges (>= 2.13.12), r-bioc-genomeinfodb, r-bioc-genomicranges (>= 1.31.8), r-bioc-genomicalignments (>= 1.15.6), r-bioc-rtracklayer (>= 1.39.7), libc6 (>= 2.14)
Description:  GRO-seq Analysis Pipeline This BioConductor package provides a pipeline for the analysis of GRO- seq data. Among the more advanced features, r-bioc-grohmm predicts the boundaries of transcriptional activity across the genome de novo using a two-state hidden Markov model (HMM). . The used model essentially divides the genome into transcribed and non- transcribed regions in a strand specific manner. HMMs are used to identify the leading edge of Pol II at genes activated by a stimulus in GRO-seq time course data. This approach allows the genome-wide interrogation of transcription rates in cells.
Package: r-bioc-gseabase (1.56.0+ds-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/GSEABase/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-biocgenerics (>= 0.13.8), r-bioc-biobase (>= 2.17.8), r-bioc-annotate (>= 1.45.3), r-bioc-graph (>= 1.37.2), r-bioc-annotationdbi, r-cran-xml
Description:  Gene set enrichment data structures and methods This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA).
Package: r-bioc-gsva (1.42.0+ds-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/GSVA/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biobase, r-bioc-summarizedexperiment, r-bioc-gseabase, r-cran-matrix, r-bioc-biocparallel, r-bioc-singlecellexperiment, r-bioc-sparsematrixstats, r-bioc-delayedarray, r-bioc-delayedmatrixstats, r-bioc-hdf5array, r-bioc-biocsingular, libc6 (>= 2.4)
Description:  Gene Set Variation Analysis for microarray and RNA-seq data Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene- set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross- tissue pathway analysis, in a pathway-centric manner.
Package: r-bioc-gviz (1.38.3+dfsg-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/Gviz/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-s4vectors (>= 0.9.25), r-bioc-iranges (>= 1.99.18), r-bioc-genomicranges (>= 1.17.20), r-bioc-xvector (>= 0.5.7), r-bioc-rtracklayer (>= 1.25.13), r-cran-lattice, r-cran-rcolorbrewer, r-bioc-biomart (>= 2.11.0), r-bioc-annotationdbi (>= 1.27.5), r-bioc-biobase (>= 2.15.3), r-bioc-genomicfeatures (>= 1.17.22), r-bioc-ensembldb (>= 2.11.3), r-bioc-bsgenome (>= 1.33.1), r-bioc-biostrings (>= 2.33.11), r-bioc-biovizbase (>= 1.13.8), r-bioc-rsamtools (>= 1.17.28), r-cran-latticeextra (>= 0.6-26), r-cran-matrixstats (>= 0.8.14), r-bioc-genomicalignments (>= 1.1.16), r-bioc-genomeinfodb (>= 1.1.3), r-bioc-biocgenerics (>= 0.11.3), r-cran-digest (>= 0.6.8)
Description:  Plotting data and annotation information along genomic coordinates Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data.
Package: r-bioc-hdf5array (1.22.1-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/HDF5Array/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-delayedarray (>= 0.15.16), r-bioc-rhdf5 (>= 2.31.6), r-cran-matrix, r-bioc-rhdf5filters, r-bioc-biocgenerics (>= 0.31.5), r-bioc-s4vectors (>= 0.27.13), r-bioc-iranges, r-bioc-rhdf5lib, libc6 (>= 2.14), libhdf5-103-1, libhdf5-hl-100
Description:  HDF5 backend for DelayedArray objects Implements the HDF5Array and TENxMatrix classes, 2 convenient and memory-efficient array-like containers for on-disk representation of HDF5 datasets. HDF5Array is for datasets that use the conventional (i.e. dense) HDF5 representation. TENxMatrix is for datasets that use the HDF5-based sparse matrix representation from 10x Genomics (e.g. the 1.3 Million Brain Cell Dataset). Both containers being DelayedArray extensions, they support all operations supported by DelayedArray objects. These operations can be either delayed or block-processed.
Package: r-bioc-hilbertvis (1.52.0-1)
Section: universe/math
Homepage: https://bioconductor.org/packages/HilbertVis
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-cran-lattice, libc6 (>= 2.4)
Description:  GNU R package to visualise long vector data This tool allows one to display very long data vectors in a space-efficient manner, by organising it along a 2D Hilbert curve. The user can then visually judge the large scale structure and distribution of features simultaenously with the rough shape and intensity of individual features. . In bioinformatics, a typical use case is ChIP-Chip and ChIP-Seq, or basically all the kinds of genomic data, that are conventionally displayed as quantitative track ("wiggle data") in genome browsers such as those provided by Ensembl or UCSC.
Package: r-bioc-hsmmsinglecell (1.14.0-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/release/data/experiment/html/HSMMSingleCell.html
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14
Description:  Single-cell RNA-Seq for differentiating human skeletal muscle myoblasts Skeletal myoblasts undergo a well-characterized sequence of morphological and transcriptional changes during differentiation. In this experiment, primary human skeletal muscle myoblasts (HSMM) were expanded under high mitogen conditions (GM) and then differentiated by switching to low-mitogen media (DM). RNA-Seq libraries were sequenced from each of several hundred cells taken over a time-course of serum- induced differentiation. Between 49 and 77 cells were captured at each of four time points (0, 24, 48, 72 hours) following serum switch using the Fluidigm C1 microfluidic system. RNA from each cell was isolated and used to construct mRNA-Seq libraries, which were then sequenced to a depth of ~4 million reads per library, resulting in a complete gene expression profile for each cell.
Package: r-bioc-htsfilter (1.34.0+dfsg-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/HTSFilter/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-edger, r-bioc-deseq2, r-bioc-biocparallel, r-bioc-biobase
Description:  GNU R filter replicated high-throughput transcriptome sequencing data This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions.
Package: r-bioc-hypergraph (1.66.0-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/hypergraph/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-graph
Description:  BioConductor hypergraph data structures This package BioConductor implements some simple capabilities for representing and manipulating hypergraphs.
Package: r-bioc-ihw (1.22.0+dfsg-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/IHW/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-cran-slam, r-bioc-lpsymphony, r-cran-fdrtool, r-bioc-biocgenerics
Description:  GNU R independent hypothesis weighting Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis.
Package: r-bioc-impute (1.68.0-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/impute/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, libc6 (>= 2.4)
Description:  Imputation for microarray data R package which provide a function to perform imputation for microarray data (currently KNN only).
Package: r-bioc-interactivedisplaybase (1.32.0+dfsg-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/interactiveDisplayBase/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-biocgenerics, r-cran-shiny, r-cran-dt
Description:  base package for enabling powerful shiny web displays of Bioconductor objects The interactiveDisplayBase package contains the basic methods needed to generate interactive Shiny based display methods for Bioconductor objects.
Package: r-bioc-ioniser (2.18.0+dfsg-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/IONiseR/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-rhdf5, r-cran-dplyr, r-cran-magrittr, r-cran-tidyr, r-bioc-shortread, r-bioc-biostrings, r-cran-ggplot2, r-bioc-biocgenerics, r-bioc-xvector, r-cran-tibble, r-bioc-biocparallel, r-cran-bit64, r-cran-stringr
Description:  Quality Assessment Tools for Oxford Nanopore MinION data IONiseR provides tools for the quality assessment of Oxford Nanopore MinION data. It extracts summary statistics from a set of fast5 files and can be used either before or after base calling.  In addition to standard summaries of the read-types produced, it provides a number of plots for visualising metrics relative to experiment run time or spatially over the surface of a flowcell.
Package: r-bioc-iranges (2.28.0-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/IRanges/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-biocgenerics (>= 0.39.2), r-bioc-s4vectors (>= 0.29.19), libc6 (>= 2.14)
Description:  GNU R low-level containers for storing sets of integer ranges The IRanges class and its extensions are low-level containers for storing sets of integer ranges. A typical use of these containers in biology is for representing a set of chromosome regions. More specific extensions of the IRanges class will typically allow the storage of additional information attached to each chromosome region as well as a hierarchical relationship between these regions.
Package: r-bioc-isoformswitchanalyzer (1.16.0+ds-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/IsoformSwitchAnalyzeR/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-limma, r-bioc-dexseq, r-cran-ggplot2, r-bioc-bsgenome, r-cran-plyr, r-cran-reshape2, r-cran-gridextra, r-bioc-biostrings (>= 2.50.0), r-bioc-iranges, r-bioc-genomicranges, r-bioc-drimseq, r-cran-rcolorbrewer, r-bioc-rtracklayer, r-cran-venndiagram, r-cran-dbi, r-bioc-genomeinfodb, r-bioc-tximport (>= 1.7.1), r-bioc-tximeta (>= 1.7.12), r-bioc-edger, r-cran-futile.logger, r-cran-stringr, r-cran-dplyr, r-cran-magrittr, r-cran-readr, r-cran-tibble, r-bioc-xvector, r-bioc-biocgenerics, r-cran-rcurl, r-bioc-biobase
Description:  Identify, Annotate and Visualize Alternative Splicing and Isoform Switches with Functional Consequences from both short- and long-read RNA-seq data. Analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNASeq by tools such as Kallisto, Salmon, StringTie, Cufflinks/Cuffdiff etc.
Package: r-bioc-keggrest (1.34.0+dfsg-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/KEGGREST/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-cran-httr, r-cran-png, r-bioc-biostrings
Description:  GNU R client-side REST access to KEGG A package that provides a client interface to the KEGG REST server. Based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (Python package) by Aurelien Mazurie.
Package: r-bioc-limma (3.50.0+dfsg-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/limma/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, libc6 (>= 2.4)
Description:  linear models for microarray data Microarrays are microscopic plates with carefully arranged short DNA strands and/or chemically prepared surfaces to which other DNA preferably binds. The amount of DNA binding at different locations of these chips, typically determined by a fluorescent dye, is to be interpreted. The technology is typically used with DNA that is derived from RNA, i.e to determine the activity of a gene and/or its splice variants. But the technology is also used to determine sequence variations in genomic DNA. . This Bioconductor package supports the analysis of gene expression microarray data, especially the use of linear models for analysing designed experiments and the assessment of differential expression. The package includes pre-processing capabilities for two-colour spotted arrays. The differential expression methods apply to all array platforms and treat Affymetrix, single channel and two channel experiments in a unified way.
Package: r-bioc-lpsymphony (1.22.0+dfsg-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/lpsymphony/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, coinor-libsymphony3, libc6 (>= 2.14)
Description:  Symphony integer linear programming solver in GNU R This package was derived from Rsymphony_0.1-17 from CRAN. These packages provide an R interface to SYMPHONY, an open-source linear programming solver written in C++. The main difference between this package and Rsymphony is that it includes the solver source code (SYMPHONY version 5.6), while Rsymphony expects to find header and library files on the users' system. Thus the intention of lpsymphony is to provide an easy to install interface to SYMPHONY.
Package: r-bioc-makecdfenv (1.70.0-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/makecdfenv/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-affyio, r-bioc-biobase, r-bioc-affy, r-bioc-zlibbioc, libc6 (>= 2.4)
Description:  BioConductor CDF Environment Maker This package has two functions. One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping. The other creates a package that automatically loads that environment.
Package: r-bioc-matrixgenerics (1.6.0-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/MatrixGenerics/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-cran-matrixstats (>= 0.60.1)
Description:  S4 Generic Summary Statistic Functions that Operate on Matrix-Like Objects S4 generic functions modeled after the 'matrixStats' API for alternative matrix implementations. Packages with alternative matrix implementation can depend on this package and implement the generic functions that are defined here for a useful set of row and column summary statistics. Other package developers can import this package and handle a different matrix implementations without worrying about incompatibilities.
Package: r-bioc-mergeomics (1.22.0-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/Mergeomics/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14
Description:  Integrative network analysis of omics data The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts: 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA).
Package: r-bioc-metagenomeseq (1.36.0-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/metagenomeSeq/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-biobase, r-bioc-limma, r-cran-glmnet, r-cran-rcolorbrewer, r-cran-matrixstats, r-cran-foreach, r-cran-matrix, r-cran-gplots, r-bioc-wrench
Description:  GNU R statistical analysis for sparse high-throughput sequencing MetagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.
Package: r-bioc-metapod (1.2.0+dfsg-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/metapod/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-cran-rcpp (>= 1.0.7), libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 11)
Description:  GNU R meta-analyses on P-Values of differential analyses Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate.
Package: r-bioc-mofa (1.6.1+dfsg-5)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/MOFA/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-rhdf5, r-cran-dplyr, r-cran-reshape2, r-cran-pheatmap, r-cran-corrplot, r-cran-ggplot2, r-cran-ggbeeswarm, r-cran-scales, r-cran-ggally, r-cran-rcolorbrewer, r-cran-cowplot, r-cran-ggrepel, r-bioc-multiassayexperiment, r-bioc-biobase, r-cran-doparallel, r-cran-foreach, r-cran-reticulate, python3-mofapy
Description:  Multi-Omics Factor Analysis (MOFA) Multi-Omics Factor Analysis: an unsupervised framework for the integration of multi-omics data sets. . Upstream no longer supports this package. This package only still ships to help with rerunning/comparing/transitioning existing projects. For new projects please upgrade to MOFA2 (MOFA+). Actually, also when adding new data to old projects, MOFA2 has further improved the handling of multiple factors, and to compensate for a batch effect that is likely introduced with additional data, may be an immediate use case for that new version.
Package: r-bioc-monocle (2.22.0-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/monocle/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-cran-matrix (>= 1.2-6), r-bioc-biobase, r-cran-ggplot2 (>= 1.0.0), r-cran-vgam (>= 1.0-6), r-cran-ddrtree (>= 0.1.4), r-cran-igraph (>= 1.0.1), r-bioc-biocgenerics, r-bioc-hsmmsinglecell (>= 0.101.5), r-cran-plyr, r-cran-cluster, r-cran-combinat, r-cran-fastica, r-cran-irlba (>= 2.0.0), r-cran-matrixstats, r-cran-densityclust (>= 0.3), r-cran-rtsne, r-cran-mass, r-cran-reshape2, r-bioc-limma, r-cran-tibble, r-cran-dplyr, r-cran-qlcmatrix, r-cran-pheatmap, r-cran-stringr, r-cran-proxy, r-cran-slam, r-cran-viridis, r-bioc-biocviews, r-cran-rann (>= 2.5), r-cran-rcpp (>= 0.12.0), libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 11)
Description:  clustering, differential expression, and trajectory analysis for RNA-Seq Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data.  It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well.
Package: r-bioc-multiassayexperiment (1.20.0+dfsg-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/MultiAssayExperiment/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-summarizedexperiment (>= 1.3.81), r-bioc-genomicranges (>= 1.25.93), r-bioc-biocgenerics, r-bioc-s4vectors (>= 0.23.19), r-bioc-iranges, r-bioc-biobase, r-cran-tidyr
Description:  Software for integrating multi-omics experiments in BioConductor MultiAssayExperiment harmonizes data management of multiple assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames.
Package: r-bioc-multtest (2.50.0-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/multtest/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-biocgenerics, r-bioc-biobase, r-cran-survival, r-cran-mass, libc6 (>= 2.29)
Description:  Bioconductor resampling-based multiple hypothesis testing Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments.
Package: r-bioc-mutationalpatterns (3.4.0+dfsg-1)
Section: universe/gnu-r
Homepage: https://bioconductor.org/packages/MutationalPatterns/
Depends: r-base-core (>= 4.1.2-1ubuntu1), r-api-4.0, r-api-bioc-3.14, r-bioc-genomicranges (>= 1.24.0), r-cran-nmf (>= 0.20.6), r-bioc-s4vectors, r-bioc-biocgenerics (>= 0.18.0), r-bioc-bsgenome (>= 1.40.0), r-bioc-variantannotation (>= 1.18.1), r-cran-dplyr (>= 0.8.3), r-cran-tibble (>= 2.1.3), r-cran-purrr (>= 0.3.2), r-cran-tidyr (>= 1.0.0), r-cran-stringr (>= 1.4.0), r-cran-magrittr (>= 1.5), r-cran-ggplot2 (>= 2.1.0), r-cran-pracma (>= 1.8.8), r-bioc-iranges (>= 2.6.0), r-bioc-genomeinfodb (>= 1.12.0), r-bioc-biostrings (>= 2.40.0), r-cran-ggdendro (>= 0.1-20), r-cran-cowplot (>= 0.9.2), r-cran-ggalluvial (>= 0.12.2), r-cran-rcolorbrewer
Description:  GNU R comprehensive genome-wide analysis of mutational processes This BioConductor package provides an extensive toolset for the characterization and visualization of a wide range of mutational patterns in base substitution catalogs.