A novel significance score for gene selection and ranking.
BACKGROUND
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.


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


BACKGROUND
xy@ieee.org, cheny8@uthscsa.edu


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Epidemiology and Biostatistics, Computational Biology and Bioinformatics Division, Greehey Children's Cancer Research Institute, Department of Surgery, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, United States Army Institute of Surgical Research, Fort Sam Houston, TX 78234 and Cancer Therapy and Research Center, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
Yufei
Y
Xiao
Tzu-Hung
TH
Hsiao
Uthra
U
Suresh
Hung-I Harry
HI
Chen
Xiaowu
X
Wu
Steven E
SE
Wolf
Yidong
Y
Chen
D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D030541:Databases, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012372:ROC Curve; D011960:Receptors, Estrogen / Q000378:metabolism
D011960:Receptors, Estrogen