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pathway-enrichment

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About pathway-enrichment

P

pathway-enrichment

Performs pathway

pathway-enrichment is an agent skill for pathway and gene-set enrichment analysis, on gene lists or on ranked gene data, and for interpreting the results. It is the usual last step after a differential expression analysis, a screen or clustering. The skill explains the choice that matters most, between two methods. Over-representation analysis takes a thresholded gene list, such as genes with an adjusted p-value below 0.05, and tests whether it overlaps a gene set more than chance, using Fisher's exact or hypergeometric tests. Tools such as Enrichr and g:Profiler do this. Gene set enrichment analysis, GSEA, uses the whole ranked list with no threshold, and tests whether each gene set is concentrated towards the top or bottom of the ranking, which works better when effects are broad and subtle. Preranked GSEA uses a score per gene, such as the DESeq2 statistic. The skill covers the gene-set databases behind these analyses, including GO, KEGG, Reactome, WikiPathways and MSigDB Hallmark sets, and it covers scoring of gene sets per sample or per cell with ssGSEA or GSVA.

View pathway-enrichmentAll AI Skills apps
Price
Free
Made by
k-dense-ai
Category
AI Skills
Website
github.com/k-dense-ai/scientific-agent-skills/pathway-enrichment