{GSEA} R# Documentation

GSEA

require(GCModeller);

#' The GCModeller GSEA toolkit
imports "GSEA" from "gseakit";

The GCModeller GSEA toolkit

##### Gene set enrichment analysis Gene Set enrichment analysis (GSEA) (also called functional enrichment analysis Or pathway enrichment analysis) Is a method To identify classes Of genes Or proteins that are over-represented In a large Set Of genes Or proteins, And may have an association With disease phenotypes. The method uses statistical approaches To identify significantly enriched Or depleted groups Of genes. Transcriptomics technologies And proteomics results often identify thousands Of genes which are used For the analysis.

.NET clr type export
enrich: EnrichmentResult

The GCModeller enrichment analysis output table


.NET clr function exports
as.data.frame.enrichmentresult EnrichmentResult:

The GCModeller enrichment analysis output table
read.enrichment

read the enrichment result table
enrichment

do GSEA enrichment analysis

Gene set enrichment analysis (GSEA) (also called functional enrichment analysis or pathway enrichment analysis) is a method to identify classes of genes or proteins that are over-represented in a large set of genes or proteins, and may have an association with disease phenotypes. The method uses statistical approaches to identify significantly enriched or depleted groups of genes. Transcriptomics technologies and proteomics results often identify thousands of genes which are used for the analysis.
fisher

fisher enrichment test
enrichment.go

do GO GSEA enrichment analysis
write.enrichment

save the enrichment analysis result
as.KOBAS_terms

Convert GSEA enrichment result from GCModeller output format to KOBAS output format
enrichment.go_dag

Create network graph data for Cytoscape
enrichment.draw.go_dag
cast_enrichs

convert dataset to gcmodeller enrichment result set
to_enrichment_terms
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