Fold-change-specific enrichment analysis (FSEA): Quantification of transcriptional response magnitude for functional gene groups

Daniil S. Wiebe, Nadezhda A. Omelyanchuk, Aleksei M. Mukhin, Ivo Grosse, Sergey A. Lashin, Elena V. Zemlyanskaya, Victoria V. Mironova

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-Change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes from transcriptome data with respect to their fold changes. FSEA identifies Gene Ontology (GO) terms, which are shared by the group of genes with a similar magnitude of response, and assesses these changes. GO terms found by FSEA are fold-change-specifically (e.g., weakly, moderately, or strongly) affected by a stimulus under investigation. We demonstrate that many responses to abiotic factors, mutations, treatments, and diseases occur in a fold-change-specific manner. FSEA analyses suggest that there are two prevailing responses of functionally-related gene groups, either weak or strong. Notably, some of the fold-change-specific GO terms are invisible by classical algorithms for functional gene enrichment, Singular Enrichment Analysis (SEA), and Gene Set Enrichment Analysis (GSEA). These are GO terms not enriched compared to the genome background but strictly regulated by a factor within specific fold-change intervals. FSEA analysis of a cancer-related transcriptome suggested that the gene groups with a tightly coordinated response can be the valuable source to search for possible regulators, markers, and therapeutic targets in oncogenic processes. Availability and Implementation: FSEA is implemented as the FoldGO Bioconductor R package and a web-server.

Original languageEnglish
Article number434
Number of pages14
JournalGenes
Volume11
Issue number4
DOIs
Publication statusPublished - 17 Apr 2020

Keywords

  • Enrichment analysis
  • Gene expression
  • Gene ontology
  • Transcriptomics
  • PATHWAYS
  • ACTIVATION
  • PROTEIN
  • enrichment analysis
  • EXPRESSION PATTERNS
  • gene ontology
  • PROSTATE-CANCER CELLS
  • transcriptomics
  • gene expression

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