Medicine and Health Sciences Commons^™

Testing Multiple Hypotheses Through Imp Weighted Fdr Based On A Genetic Functional Network With Application To A New Zebrafish Transcriptome Study, Jiang Gui, Casey S. Greene, Con Sullivan, Walter Taylor, Jason H. Moore, Carol Kim

Dartmouth Scholarship

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 …