A Novel Significance Score for Gene Selection and Ranking

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2012 Feb 11

PMID 22321699

Citations 148

Authors

Yufei Xiao

Tzu-Hung Hsiao

Uthra Suresh

Hung-I Harry Chen

Xiaowu Wu

Steven E Wolf

Yidong Chen

Affiliations

Soon will be listed here.

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, π-value based approach is more robust in selecting DE genes, with the largest area under 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.

Availability: http://gccri.uthscsa.edu/Pi_Value_Supplementary.asp

Contact: xy@ieee.org, cheny8@uthscsa.edu

Supplementary Information: Supplementary data are available at Bioinformatics online.

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