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Activity Number: 63 - Omics Data: Study Design, Power and Sample Size
Type: Topic Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #329786 Presentation
Title: Sample Size and Power Analysis for RNA-Seq Differential Expression in Paired Study Designs
Author(s): Masha Kocherginsky* and Kwang-Youn Kim and Daniela E Matei
Companies: Northwestern University and Northwestern University and Northwestern University
Keywords: power analysis; RNAseq; paired data; PDX; simulation; transcriptomics

Power calculation is a critical component of RNA-seq experimental design, but the complex data structure, high dimensionality, and a multitude of necessary assumptions make an analytic solution generally unavailable. Simulation-based power calculations for two-group comparisons have been proposed (e.g. PROPER and RnaSeqSampleSize packages), but no methods have been developed for paired designs. We extend the simulation approaches to estimate power for paired designs. This work is motivated by a clinical trial of platinum resistance development in ovarian cancer using patient derived xenograft models (PDX). PDX models are established by xenografting a patient's tumor into multiple mice, and comparing transcriptome using RNA-Seq between the original patient tumor and the corresponding PDX mice. As in other approaches, we assume negative binomial distribution with parameter estimates based on six publicly available RNA-Seq data sets in Ching et al (2014). To account for the correlation between the primary tumor and the matched PDXs, we simulate data from a bivariate negative binomial distribution, and use a generalized linear mixed model with patient ID as the random effect.

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

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