A novel significance score for gene selection and ranking

Yufei Xiao, Tzu Hung Hsiao, Uthra Suresh, Hung I Harry Chen, Xiaowu Wu, Steven E. Wolf, Yidong Chen

Research output: Contribution to journalArticle

69 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)801-807
Number of pages7
JournalBioinformatics
Volume30
Issue number6
DOIs
StatePublished - Mar 1 2014

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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    Xiao, Y., Hsiao, T. H., Suresh, U., Chen, H. I. H., Wu, X., Wolf, S. E., & Chen, Y. (2014). A novel significance score for gene selection and ranking. Bioinformatics, 30(6), 801-807. https://doi.org/10.1093/bioinformatics/btr671