PT  - JOURNAL ARTICLE
AU  - Bin Zhuo
AU  - Duo Jiang
TI  - MEACA: efficient gene-set interpretation of expression data using mixed models
AID  - 10.1101/106781
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 106781
4099  - http://biorxiv.org/content/early/2017/03/16/106781.short
4100  - http://biorxiv.org/content/early/2017/03/16/106781.full
AB  - Competitive gene-set analysis, or enrichment analysis, is widely used for functional interpretation of gene expression data. It tests a known category (e.g. pathway) of genes for enriched differential expression signals. Current methods do not properly capture inter-gene correlations and heterogeneity, resulting in mis-calibration and power loss. We propose MEACA, a new gene-set method based on mixed-effects models. MEACA flexibly incorporates unknown heterogeneity and correlations across genes, and does not need time-consuming permutations. Compared to existing methods, MEACA substantially improves type 1 error control and power in widely ranging scenarios. Real data applications demonstrate MEACA’s ability to recover biologically meaningful relationships.