Enhanced clustering-based differential expression analysis method for RNA-seq data

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering has been widely used to classify DEGs with similar ex-pression patterns, but rarely used to identify DEGs themselves. We recently reported that the clustering-based method (called MBCdeg1 and 2) for identifying DEGs has great potential. How-ever, these methods left room for improvement. This study reports on the improvement (named MBCdeg3). We compared a total of six competing methods: three conventional R packages (edgeR, DESeq2, and TCC) and three versions of MBCdeg (i.e., MBCdeg1, 2, and 3) corresponding to three different normalization algorithms. As MBCdeg3 performs well in many simulation scenarios of RNA-seq count data, MBCdeg3 replaces MBCdeg1 and 2 in our previous report.center dot MBCdeg3 is a method for both identification and classification of DEGs from RNA-seq count data.center dot MBCdeg3 is available as a function of R, which is common in the field of expression analysis.center dot MBCdeg3 performs well in a variety of simulation scenarios for RNA-seq count data.