In this study, we propose a novel method, RoMA, to accurately detect differential expression and unlock 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. Some naive normal-based tests have low testing power with the invalid normal distribution assumptions for RNA-seq read counts, whereas count-based methods lack a biologically meaningful interpretation and have limited capability for integration with other analysis packages for mRNA abundance. RoMA 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 methods is implemented in a user-friendly R package (RoMA).

Citation Format: Guoshuai Cai, Jennifer M. Franks, Michael L. Whitfield. Precision-weighted RNA-seq analyses of molecular abundance (RoMA) for detecting differential gene expression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2289.