Differential Gene Set Enrichment Analysis: A statistical approach to quantify the relative enrichment of two gene sets
| Autor: | Nicholas A. Graham, William E. Lowry, James H. Joly |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Metabolic state Cellular senescence Computational biology Biology Biochemistry Transcriptome Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Humans Lactate secretion Molecular Biology Gene Probability 030304 developmental biology Supplementary data 0303 health sciences Gene Expression Profiling Gene sets Original Papers Computer Science Applications Computational Mathematics Metabolic pathway Computational Theory and Mathematics 030220 oncology & carcinogenesis Cancer cell lines Algorithms Software |
| Zdroj: | Bioinformatics |
| DOI: | 10.1101/860460 |
| Popis: | Motivation Gene Set Enrichment Analysis (GSEA) is an algorithm widely used to identify statistically enriched gene sets in transcriptomic data. However, GSEA cannot examine the enrichment of two gene sets or pathways relative to one another. Here we present Differential Gene Set Enrichment Analysis (DGSEA), an adaptation of GSEA that quantifies the relative enrichment of two gene sets. Results After validating the method using synthetic data, we demonstrate that DGSEA accurately captures the hypoxia-induced coordinated upregulation of glycolysis and downregulation of oxidative phosphorylation. We also show that DGSEA is more predictive than GSEA of the metabolic state of cancer cell lines, including lactate secretion and intracellular concentrations of lactate and AMP. Finally, we demonstrate the application of DGSEA to generate hypotheses about differential metabolic pathway activity in cellular senescence. Together, these data demonstrate that DGSEA is a novel tool to examine the relative enrichment of gene sets in transcriptomic data. Availability and implementation DGSEA software and tutorials are available at https://jamesjoly.github.io/DGSEA/. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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