Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

• Main Authors: Kadota, K. (Author), Osabe, T. (Author), Shimizu, K. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: algorithm; Algorithms; article; cluster analysis; Cluster Analysis; clustering algorithm; Clustering algorithms; controlled study; Differential expression; differential expression analysis; Differentially expressed gene; Expression analysis; Expression patterns; Gene clustering; Gene expression; gene expression profiling; Gene Expression Profiling; gene identification; Multi-group; Normalization algorithms; Posterior probability; probability; protein expression; RNA; RNA sequencing; RNA-seq; RNA-Seq; sequence analysis; Sequence Analysis, RNA; simulation; Time course
• Online Access: View Fulltext in Publisher

 Background: RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis. Results: The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (PDEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm. Conclusions: MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required. © 2021, The Author(s).