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.
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functionalgenomicsmicroarrayrnaseqpathwaysgenesetenrichmentgene-set-enrichmentgenomicspathway-enrichment-analysis
16.08 score 248 stars 24 dependents 3.0k scripts 22k downloadsGenomicScores - Infrastructure to work with genomewide position-specific scores
Provide infrastructure to store and access genomewide position-specific scores within R and Bioconductor.
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infrastructuregeneticsannotationsequencingcoverageannotationhubsoftware
8.64 score 9 stars 6 dependents 109 scripts 1.2k downloadsqpgraph - Estimation of Genetic and Molecular Regulatory Networks from High-Throughput Genomics Data
Estimate gene and eQTL networks from high-throughput expression and genotyping assays.
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microarraygeneexpressiontranscriptionpathwaysnetworkinferencegraphandnetworkgeneregulationgeneticsgeneticvariabilitysnpsoftwareopenblas
8.24 score 3 stars 3 dependents 1.2k downloadsatena - Analysis of Transposable Elements
Quantify expression of transposable elements (TEs) from RNA-seq data through different methods, including ERVmap, TEtranscripts and Telescope. A common interface is provided to use each of these methods, which consists of building a parameter object, calling the quantification function with this object and getting a SummarizedExperiment object as output container of the quantified expression profiles. The implementation allows one to quantify TEs and gene transcripts in an integrated manner.
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transcriptiontranscriptomicsrnaseqsequencingpreprocessingsoftwaregeneexpressioncoveragedifferentialexpressionfunctionalgenomics
6.24 score 14 stars 4 scripts 487 downloadsVariantFiltering - Filtering of coding and non-coding genetic variants
Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc.
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geneticshomo_sapiensannotationsnpsequencinghighthroughputsequencing
5.96 score 4 stars 19 scripts 800 downloadsgDNAx - Diagnostics for assessing genomic DNA contamination in RNA-seq data
Provides diagnostics for assessing genomic DNA contamination in RNA-seq data, as well as plots representing these diagnostics. Moreover, the package can be used to get an insight into the strand library protocol used and, in case of strand-specific libraries, the strandedness of the data. Furthermore, it provides functionality to filter out reads of potential gDNA origin.
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transcriptiontranscriptomicsrnaseqsequencingpreprocessingsoftwaregeneexpressioncoveragedifferentialexpressionfunctionalgenomicssplicedalignmentalignment
4.90 score 2 stars 8 scripts 397 downloadsGSVAdata - Data employed in the vignette of the GSVA package
This package stores the data employed in the vignette of the GSVA package. These data belong to the following publications: Cahoy et al. J Neurosci 28:264-278, 2008; Carrel and Willard, Nature, 434:400-404, 2005; Huang et al. PNAS, 104:9758-9763, 2007; Pickrell et al. Nature, 464:768-722, 2010; Skaletsky et al. Nature, 423:825-837; Verhaak et al. Cancer Cell 17:98-110, 2010; Costa et al. FEBS J, 288:2311-2331, 2021.
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experimentdataexpressiondatasequencingdatahomo_sapiens_datacancerdatatissuedatapkg
4.65 score 1 dependents 213 scriptsgDNAinRNAseqData - RNA-seq data with different levels of gDNA contamination
Provides access to BAM files generated from RNA-seq data produced with different levels of gDNA contamination. It currently allows one to download a subset of the data published by Li et al., BMC Genomics, 23:554, 2022. This subset of data is formed by BAM files with about 100,000 alignments with three different levels of gDNA contamination.
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experimenthubexperimentdataexpressiondatasequencingdatarnaseqdatahomo_sapiens_datadatapkg
3.68 score 24 scripts