Identification of differentially expressed gene using robust singular value decomposition

Nishith Kumar, Md Tofazzal Hossain, Eric J. Beh, Masahiro Sugimoto, Mohammed Nasser

Research output: Contribution to journalArticlepeer-review


Identification of differentially expressed genes (DEG) in transcriptomic analyses is one of the important tasks to find out significantly activated/deactivated pathways. Outliers and/or the missing values are commonly observed in microarray data; however, most available statistical methods did not deal with these issues and, therefore, their analytical results were frequently skewed and deteriorated. Here, we developed a novel technique robust against outliers and missing values: a dimension reduction procedure based on robust singular value decomposition (RSVD). The RSVD was evaluated by two numerical experiments: artificially prepared and nonsmall cell lung cancer data (gene expression data). Four conventional techniques, such as Student’s t-test, SAM, Bayesian Robust Inference for Differential Gene Expression (BRIDGE) and Linear models for microarray data (Limma), were also performed. We evaluated the area under curve (AUC) form receiver operating characteristic curves of these five methods using two experiments with 50 different conditions. The AUC values of our methods showed significantly (p<0.05; Mann-Whitney test) higher than those of the other methods in both experiments. We believe our proposed technique is helpful for the identification of biologically meaningful genes that change in noisy microarray data.

Original languageEnglish
Pages (from-to)366-374
Number of pages9
JournalCurrent Bioinformatics
Issue number3
Publication statusPublished - 2016 Jul 1


  • Bayesian robust inference for differential gene expression (BRIDGE)
  • Bonferroni correction
  • Differential gene expression
  • Linear models for microarray data (LIMMA)
  • Robust singular value decomposition (RSVD)
  • Significance analysis of microarrays (SAM)
  • T-test

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computational Mathematics


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