Communications for Statistical Applications and Methods

  • 제29권5호
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  • Pages.591-601
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  • 2022
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Comparison of covariance thresholding methods in gene set analysis

  • Park, Sora (Department of Statistics, Pusan National University) ;
  • Kim, Kipoong (Department of Statistics, Pusan National University) ;
  • Sun, Hokeun (Department of Statistics, Pusan National University)
  • 투고 : 2022.03.22
  • 심사 : 2022.05.09
  • 발행 : 2022.09.30
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초록

In gene set analysis with microarray expression data, a group of genes such as a gene regulatory pathway and a signaling pathway is often tested if there exists either differentially expressed (DE) or differentially co-expressed (DC) genes between two biological conditions. Recently, a statistical test based on covariance estimation have been proposed in order to identify DC genes. In particular, covariance regularization by hard thresholding indeed improved the power of the test when the proportion of DC genes within a biological pathway is relatively small. In this article, we compare covariance thresholding methods using four different regularization penalties such as lasso, hard, smoothly clipped absolute deviation (SCAD), and minimax concave plus (MCP) penalties. In our extensive simulation studies, we found that both SCAD and MCP thresholding methods can outperform the hard thresholding method when the proportion of DC genes is extremely small and the number of genes in a biological pathway is much greater than a sample size. We also applied four thresholding methods to 3 different microarray gene expression data sets related with mutant p53 transcriptional activity, and epithelium and stroma breast cancer to compare genetic pathways identified by each method.

키워드

과제정보

This work was supported by a 2-Year Research Grant of Pusan National University

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