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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.
Maintained by Robert Castelo. Last updated 11 days ago.
functionalgenomicsmicroarrayrnaseqpathwaysgenesetenrichmentgene-set-enrichmentgenomicspathway-enrichment-analysis
212 stars 14.74 score 1.6k scripts 19 dependentsbioc
piano:Platform for integrative analysis of omics data
Piano performs gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses.
Maintained by Leif Varemo Wigge. Last updated 5 months ago.
microarraypreprocessingqualitycontroldifferentialexpressionvisualizationgeneexpressiongenesetenrichmentpathwaysbioconductorbioconductor-packagebioinformaticsgene-set-enrichmenttranscriptomics
13 stars 8.30 score 183 scripts 7 dependents
eltebioinformatics
mulea:Enrichment Analysis Using Multiple Ontologies and False Discovery Rate
Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.
Maintained by Tamas Stirling. Last updated 4 months ago.
annotationdifferentialexpressiongeneexpressiongenesetenrichmentgographandnetworkmultiplecomparisonpathwaysreactomesoftwaretranscriptionvisualizationenrichmentenrichment-analysisfunctional-enrichment-analysisgene-set-enrichmentontologiestranscriptomicscpp
28 stars 7.36 score 34 scripts
acare
hacksig:A Tidy Framework to Hack Gene Expression Signatures
A collection of cancer transcriptomics gene signatures as well as a simple and tidy interface to compute single sample enrichment scores either with the original procedure or with three alternatives: the "combined z-score" of Lee et al. (2008) <doi:10.1371/journal.pcbi.1000217>, the "single sample GSEA" of Barbie et al. (2009) <doi:10.1038/nature08460> and the "singscore" of Foroutan et al. (2018) <doi:10.1186/s12859-018-2435-4>. The 'get_sig_info()' function can be used to retrieve information about each signature implemented.
Maintained by Andrea Carenzo. Last updated 2 years ago.
gene-expression-signaturesgene-set-enrichment
19 stars 4.41 score 27 scripts