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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 dependents
egeulgen
pathfindR:Enrichment Analysis Utilizing Active Subnetworks
Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. 'pathfindR' is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. 'pathfindR' also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in 'pathfindR' are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. 'pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.
Maintained by Ege Ulgen. Last updated 1 months ago.
active-subnetworksenrichmentpathwaypathway-enrichment-analysissubnetwork
187 stars 10.38 score 138 scriptsbioc
CARNIVAL:A CAusal Reasoning tool for Network Identification (from gene expression data) using Integer VALue programming
An upgraded causal reasoning tool from Melas et al in R with updated assignments of TFs' weights from PROGENy scores. Optimization parameters can be freely adjusted and multiple solutions can be obtained and aggregated.
Maintained by Attila Gabor. Last updated 5 months ago.
transcriptomicsgeneexpressionnetworkcausal-modelsfootprintsinteger-linear-programmingpathway-enrichment-analysis
57 stars 9.03 score 90 scripts 1 dependentsbioc
pathlinkR:Analyze and interpret RNA-Seq results
pathlinkR is an R package designed to facilitate analysis of RNA-Seq results. Specifically, our aim with pathlinkR was to provide a number of tools which take a list of DE genes and perform different analyses on them, aiding with the interpretation of results. Functions are included to perform pathway enrichment, with muliplte databases supported, and tools for visualizing these results. Genes can also be used to create and plot protein-protein interaction networks, all from inside of R.
Maintained by Travis Blimkie. Last updated 3 months ago.
genesetenrichmentnetworkpathwaysreactomernaseqnetworkenrichmentbioinformaticsnetworkspathway-enrichment-analysisvisualization
28 stars 6.59 score 2 scripts