Analysis of clustered RNA-seq data. (2017)

• Record Type: Journal Article
• Authors: Park, Hyunjin; Lee, Seungyeoun; Kim, Ye Jin; Choi, Myung-Sook; Park, Taesung
• Is Part Of: International journal of data mining and bioinformatics. Volume 19:Number 1(2017)
• Page Start: 19
• Page End: 31
• Publication Date: 2017
• Subjects: RNA-seq -- DEG -- differentially expressed gene -- simultaneously -- multivariate -- GEE -- generalised estimating equations -- FDR -- false discovery rate; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

RNA sequencing (RNA-seq) technology has now become a powerful tool for measuring levels of transcripts. Through this high-throughput technology, we can investigate post-transcriptional modifications, non-coding RNAs, mutations, gene fusion, and changes in gene expression levels. Recently, many methods have been developed to find differentially expressed genes (DEGs) between treatment groups. Most of these methods assume that RNA-seq data is generated independently from the different subjects. Nowadays, clustered RNA-seq data are also commonly observed, such as paired RNA-seq data, from the same patient. Unfortunately, existing methods cannot adequately handle clustered RNA-seq data. In this paper, we propose a new testing method, based on the Generalised Estimating Equations (GEE) approach, which is widely used to analyse repeatedly measured data. Our GEE-based approach uses the correlations between RNA-seq data appropriately, which results in increased power in detecting DEGs. Through real data analysis and simulation studies, we compare the performance of the GEE method to those of other existing methods. Specifically, our GEE analysis was compared to various other methodologies, particularly with regard to sensitivity to detect DEGs and false discovery rates.