A novel significance score for gene selection and ranking.

ABSTRACT: 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.We defined a gene significance score ?-value by combining expression fold change and statistical significance (P-value), and explored its statistical properties. When compared to various existing methods, ?-value based approach is more robust in selecting DE genes, with the largest area under curve in its receiver operating characteristic curve. We applied ?-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 ?-value helps elucidating otherwise overlooked important biological functions.http://gccri.uthscsa.edu/[e-mail], [e-mail] data are available at Bioinformatics online.

SUBMITTER: Xiao Y 

PROVIDER: S-EPMC3957066 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature