Пакунок: r-bioc-gsva (1.52.3+ds-1)

[ Source: r-bioc-gsva  ]

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.

Інші пакунки пов'язані з r-bioc-gsva

• dep: libc6 (>= 2.17)

  Бібліотека GNU C: спільні бібліотеки
  also a virtual package provided by libc6-udeb

• dep: r-api-4.0

  virtual package provided by r-base-core

• dep: r-api-bioc-3.19

  virtual package provided by r-bioc-biocgenerics

• dep: r-bioc-biobase (>= 2.64.0)

  base functions for Bioconductor

• dep: r-bioc-biocparallel (>= 1.38.0)

  BioConductor facilities for parallel evaluation

• dep: r-bioc-biocsingular (>= 1.20.0)

  Singular Value Decomposition for Bioconductor Packages

• dep: r-bioc-delayedarray (>= 0.30.1)

  BioConductor delayed operations on array-like objects

• dep: r-bioc-delayedmatrixstats (>= 1.26.0)

  Functions on Rows and Columns of 'DelayedMatrix' Objects

• dep: r-bioc-gseabase (>= 1.66.0)

  Gene set enrichment data structures and methods

• dep: r-bioc-hdf5array (>= 1.32.0)

  HDF5 backend for DelayedArray objects

• dep: r-bioc-iranges (>= 2.38.1)

  GNU R low-level containers for storing sets of integer ranges

• dep: r-bioc-s4vectors (>= 0.42.1)

  BioConductor S4 implementation of vectors and lists

• dep: r-bioc-singlecellexperiment (>= 1.26.0)

  S4 Classes for Single Cell Data

• dep: r-bioc-sparsematrixstats (>= 1.16.0)

  BioConductor summary statistics for rows and columns of sparse matrices

• dep: r-bioc-spatialexperiment (>= 1.14.0)

  S4 Class for Spatially Resolved -omics Data

• dep: r-bioc-summarizedexperiment (>= 1.34.0)

  BioConductor assay container

• dep: r-cran-matrix (>= 1.5-0)

  GNU R package of classes for dense and sparse matrices

• sug: r-bioc-biocgenerics (>= 0.50.0)

  generic functions for Bioconductor

• sug: r-bioc-biocstyle (>= 2.32.1)

  standard styles for vignettes and other Bioconductor documents

• sug: r-bioc-edger (>= 4.2.0)

  Empirical analysis of digital gene expression data in R

• sug: r-bioc-genefilter (>= 1.86.0)

  methods for filtering genes from microarray experiments

• sug: r-bioc-gsvadata (>= 1.40.0)

  Data employed in the vignette of the GSVA package

• sug: r-bioc-limma (>= 3.60.3)

  linear models for microarray data

• sug: r-bioc-org.hs.eg.db (>= 3.19.1-1)

  genome-wide annotation for Human

• sug: r-cran-data.table

  GNU R extension of Data.frame

• sug: r-cran-future

  R package: A Future API for R

• sug: r-cran-ggplot2

  implementation of the Grammar of Graphics

• sug: r-cran-knitr

  GNU R package for dynamic report generation using Literate Programming

• sug: r-cran-plotly

  create interactive web graphics via 'plotly.js' in GNU R

• sug: r-cran-promises

  GNU R abstractions for promise-based asynchronous programming

• sug: r-cran-rcolorbrewer

  GNU R package providing suitable color palettes

• sug: r-cran-rmarkdown

  convert R markdown documents into a variety of formats

• sug: r-cran-runit

  GNU R package providing unit testing framework

• sug: r-cran-shiny

  GNU R web application framework

• sug: r-cran-shinydashboard

  GNU R create dashboards with 'Shiny'

• sug: r-cran-shinyjs

  Easily Improve the User Experience of Your Shiny Apps in Seconds

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