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Activity Number: 233 - Innovative Approaches for High-Dimensional Omics and Neuroimaging Data
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: International Indian Statistical Association
Abstract #304917 Presentation
Title: Expression-Level-Dependent Correlation Structure Estimation for Repeated-Measures RNA-Seq Data
Author(s): Dan Nettleton* and Meiling Liu
Companies: Iowa State University and Iowa State University
Keywords: parametric bootstrap; shrinkage; covariance estimation; multiple testing; false discovery rate; differential expression analysis

Scientists study messenger ribonucleic acid (mRNA) levels to track gene activity in biological systems. Tens of thousands of gene mRNA levels can be measured simultaneously in a biological sample using RNA sequencing (RNA-seq) technology. By observing how mRNA levels change across samples of different types or across samples taken from plants or animals receiving different treatments in an experiment, scientists gain clues about how genes function together in biological systems. In some experiments, samples are collected from each experimental unit at multiple time points. The RNA-seq measurements corresponding to samples extracted from a single experimental unit tend to be correlated. We present a strategy for estimating gene-specific correlation structures by borrowing information across genes and capitalizing on relationships between correlations and gene expression levels. We introduce a parametric bootstrap approach for differential expression analysis that accounts for expression correlations.

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

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