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Activity Number: 595 - Recent Methods Development on RNA-Seq Data Analysis
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #330932 Presentation
Title: A Data Adjustment-Tolerant Strategy for RNA-Seq Differential Gene Expression Analysis
Author(s): Guoshuai Cai* and Jennifer M. Franks and Michael L. Whitfield
Companies: Arnold School of Public Health, University of South Carolina and Geisel School of Medicine at Dartmouth and Geisel School of Medicine at Dartmouth
Keywords: RNA-seq; differential expression analysis; adjustment-tolerant; precision weight
Abstract:

In this study, we propose a novel method for RNA-seq differential expression analysis which is tolerant to data adjustment and is capable of the integration with numerous upstream and downstream analyses on mRNA abundance in RNA-seq studies. Various methods have been proposed, each with its own limitations. Our novel method incorporates information from both mRNA abundance and raw counts by modeling RPKM (reads per kilobase per million), which represents the relative abundance of mRNA transcripts, and borrowing mean-variance dependency from CPM (counts per million) as a precision weight accounting for the variability in sequencing depth. Studies on simulated data and two real datasets showed that RoMA provides an accurate quantification of mRNA abundance and a value adjustment-tolerant DE analysis with high AUC, low FDR and a desirable type I error rate. This study provides a valid strategy for mRNA abundance modeling and data analysis integration for RNA-seq studies, which will greatly facilitate the identification and interpretation of DE genes. The method is implemented in a user-friendly R package (RoMA).


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

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