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 pi-value by combining expression fold change and statistical significance (P-value), and explored its statistical properties. When compared to various existing methods, pi-value based approach is more robust in selecting DE genes, with the largest area under curve in its receiver operating characteristic curve. We applied pi-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 pi-value helps elucidating otherwise overlooked important biological functions.
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