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Activity Number: 299 - SPEED: Recent Advances in Statistical Genomics and Genetics
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #330353
Title: Identifying Direct Targets with Knockdown Experiment: An Adaptive Approach Detecting Strong Signals
Author(s): Leying Guan*
Companies: Stanford University
Keywords: Gene Knockdown; Empirical Bayes; Strong effects; Gaussian mixuture; Adaptive Estimation

Motivated by the knockdown experiment data, we propose a modified two-group model where the null group corresponds to genes which are not direct targets of a transcription factor but can have small non-zero effects when this transcription is knocked down. We model the behavior of genes from the null set by a Gaussian distribution with unknown variance $\tau^2$, and we describe methods to adaptively estimate $\tau^2$ from the data. In this paper, we have studied properties of one proposed estimation method for $\tau^2$ and we have provided simulations on estimation quality of $\tau^2$ and on quality of selected gene set. We have also applied our method to a real data set and we have acquired overall better and more stable results compared with original two group model testing for non-zero effects.

Authors who are presenting talks have a * after their name.

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