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BioData Min. 2015 Jun 17;8:17. doi: 10.1186/s13040-015-0050-8. eCollection 2015.

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

Author information

1
Department of Biomedical Data Science, Geisel school of medicine, Dartmouth College, Hanover, NH USA.
2
Dartmouth-Hitchcock Medical Center, 883 Rubin Bldg, HB7927, One Medical Center Dr., Lebanon, NH USA.
3
Department of Genetics, Geisel school of medicine, Dartmouth College, Hanover, NH USA.
4
Department of Molecular and Biomedical Sciences, University of Maine, Orono, ME USA.
5
Graduate School of Biomedical Science and Engineeering, University of Maine, Orono, ME USA.
6
Department of Biostatistics and Epidemiology, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
#
Contributed equally

Abstract

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.

KEYWORDS:

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

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