Identification of differentially expressed gene using robust singular value decomposition

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

研究成果: Article査読


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.

ジャーナルCurrent Bioinformatics
出版ステータスPublished - 2016 7月 1

ASJC Scopus subject areas

  • 生化学
  • 分子生物学
  • 遺伝学
  • 計算数学


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