PT  - JOURNAL ARTICLE
AU  - Rani K. Powers
AU  - Andrew Goodspeed
AU  - Harrison Pielke-Lombardo
AU  - Aik-Choon Tan
AU  - James C. Costello
TI  - GSEA-InContext: Identifying novel and common patterns in expression experiments
AID  - 10.1101/259440
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 259440
4099  - http://biorxiv.org/content/early/2018/02/04/259440.short
4100  - http://biorxiv.org/content/early/2018/02/04/259440.full
AB  - Motivation Gene Set Enrichment Analysis (GSEA) is routinely used to analyze and interpret coordinate changes in transcriptomics experiments. For an experiment where less than seven samples per condition are compared, GSEA employs a competitive null hypothesis to test significance. A gene set enrichment score is tested against a null distribution of enrichment scores generated from permuted gene sets, where genes are randomly selected from the input experiment. Looking across a variety of biological conditions, however, genes are not randomly distributed with many showing consistent patterns of up- or down-regulation. As a result, common patterns of positively and negatively enriched gene sets are observed across experiments. Placing a single experiment into the context of a relevant set of background experiments allows us to identify both the common and experiment-specific patterns of gene set enrichment.Results We compiled a compendium of 442 small molecule transcriptomic experiments and used GSEA to characterize common patterns of positively and negatively enriched gene sets. To identify experiment-specific gene set enrichment, we developed the GSEA-InContext method that accounts for gene expression patterns within a user-defined background set of experiments to identify statistically significantly enriched gene sets. We evaluated GSEA-InContext on experiments using small molecules with known targets and show that it successfully prioritizes gene sets that are specific to each experiment, thus providing valuable insights that complement standard GSEA analysis.Availability and Implementation GSEA-InContext is implemented in Python. Code, the background expression compendium, and results are available at: https://github.com/CostelloLab/GSEA-InContext