Faculty Candidate Presentation/Colloquium
January 6 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower

The Functional False Discovery Rate with Applications in Genomics

Xiongzhi (Chee) Chen
Lewis-Sigler Institute for Integrative Genomics
Carl Icahn Laboratory
Princeton University

The false discovery rate (FDR) measures the proportion of false positives among a set of hypothesis tests called significant. This quantity is typically estimated based on p-values or test statistics. In some scenarios, there is additional information available that may be used to more accurately estimate the FDR. We develop the functional FDR (fFDR) framework for formulating and estimating FDRs and q-values when an additional piece of information, which we call an “informative variable”, is available. The FDR is then treated as a function of this informative variable. We consider two applications in genomics. The first is a genetics of gene expression (eQTL) experiment in yeast, where every gene marker and gene expression trait pair are tested for association and the informative variable is the distance between each genetic marker and gene. The second is to detect differentially expressed genes in an RNA-seq experiment carried out in mice, where the informative variable is the per-gene read depth. In both applications, the fFDR method provides more accurate estimates of q-values and is more powerful than the standard FDR method.

Xiongzhi Chen graduated with a Ph.D. in Statistics from Purdue University in 2012, and is currently a postdoctoral research associate at the Center for Statistics and Machine Learning, and Lewis-Sigler Institute for Integrative Genomics at Princeton University

All statistics graduate students are invited to attend.


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