Testing multiple hypotheses through IMP weighted FDR based on a genetic functional network with application to a new zebrafish transcriptome study

BioData Mining

Jiang Gui³  Casey S. Greene²  Con Sullivan⁴  Walter Taylor²  Jason H. Moore¹  Carol Kim⁴ 

[1] Department of Biostatistics and Epidemiology, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

[2] Department of Genetics, Geisel school of medicine, Dartmouth College, Hanover, NH, USA

[3] Dartmouth-Hitchcock Medical Center, 883 Rubin Bldg, HB7927, One Medical Center Dr., Lebanon, NH, USA

[4] Graduate School of Biomedical Science and Engineeering, University of Maine, Orono, ME, USA

关键词: Data integration;    Genomic studies;    Family-wise error rate;    False discovery rate;   

Others  :  1216986
DOI  :  10.1186/s13040-015-0050-8

 received in 2014-12-03, accepted in 2015-06-08,  发布年份 2015

【 摘 要 】

In genome-wide studies, hundreds of thousands of hypothesis tests are performed simultaneously. Bonferroni correction and False Discovery Rate (FDR) can effectively control type I error but often yield a high false negative rate. We aim to develop a more powerful method to detect differentially expressed genes. We present a Weighted False Discovery Rate (WFDR) method that incorporate biological knowledge from genetic networks. We first identify weights using Integrative Multi-species Prediction (IMP) and then apply the weights in WFDR to identify differentially expressed genes through an IMP-WFDR algorithm. We performed a gene expression experiment to identify zebrafish genes that change expression in the presence of arsenic during a systemic Pseudomonas aeruginosa infection. Zebrafish were exposed to arsenic at 10 parts per billion and/or infected with P. aeruginosa. Appropriate controls were included. We then applied IMP-WFDR during the analysis of differentially expressed genes. We compared the mRNA expression for each group and found over 200 differentially expressed genes and several enriched pathways including defense response pathways, arsenic response pathways, and the Notch signaling pathway.

【 授权许可】

   
2015 Gui et al.

【 图 表 】

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