MBCdeg4: A modified clustering-based method for identifying differentially expressed genes from RNA-seq data - Chiharu Ichikawa; Koji Kadota (MethodsX, 2025)
RNA-seq is a commonly employed methodology for the measurement of transcriptomes, particularly for the identification of differentially expressed genes (DEGs) between different conditions or groups. In a previous report, we described a clustering-based method for identifying DEGs, designated MBCdeg1 and MBCdeg2. and a modified version, MBCdeg3. This study presents a further improved version, designated MBCdeg4. The four versions of MBCdeg employ an R package, designated MBCluster.Seq, internally. The sole distinction between them is the manner of data normalization. MBCdeg4 employs normalization factors derived from a robust normalization algorithm, designated as DEGES. Seven competing methods were compared: the four versions of MBCdeg and three conventional R packages (edgeR, DESeq2, and TCC). MBCdeg4 demonstrated superior performance in a multitude of simulation scenarios involving RNA-seq count data. Therefore, MBCdeg4 is recommended for use in preference to the earlier versions, MBCdeg1–3. • MBCdeg4 is a method for both identification and classification of DEGs from RNA-seq count data. • MBCdeg4 is available as an R function and performs well in a wide variety of simulation scenarios.