Abstract
Motivation: When identifying differentially expressed (DE) genes from high-throughput gene expression measurements, we would like to take both statistical significance (such as P-value) and biological relevance (such as fold change) into consideration. In gene set enrichment analysis (GSEA), a score that can combine fold-change and P-value together is needed for better gene ranking.
Results: We defined a gene significance score π-value by combining expression fold change and statistical significance (P-value), and explored its statistical properties. When compared to various existing methods, the π-value-based approach is more robust in selecting DE genes, with the largest area under the curve in its receiver operating characteristic curve. We applied π-value to GSEA and found it comparable to P-value and t-statistic-based methods, with added protection against false discovery in certain situations. Finally, in a gene functional study of breast cancer profiles, we showed that using π-value helps elucidating otherwise overlooked important biological functions.
