Identifying gene pathways associated with cancer characteristics via sparse statistical methods

Shuichi Kawano, Teppei Shimamura, Atsushi Niida, Seiya Imoto, Rui Yamaguchi, Masao Nagasaki, Ryo Yoshida, Cristin Print, Satoru Miyano

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

We propose a statistical method for uncovering gene pathways that characterize cancer heterogeneity. To incorporate knowledge of the pathways into the model, we define a set of activities of pathways from microarray gene expression data based on the Sparse Probabilistic Principal Component Analysis (SPPCA). A pathway activity logistic regression model is then formulated for cancer phenotype. To select pathway activities related to binary cancer phenotypes, we use the elastic net for the parameter estimation and derive a model selection criterion for selecting tuning parameters included in the model estimation. Our proposed method can also reverse-engineer gene networks based on the identified multiple pathways that enables us to discover novel gene-gene associations relating with the cancer phenotypes. We illustrate the whole process of the proposed method through the analysis of breast cancer gene expression data.

Original languageEnglish
Article number6175008
Pages (from-to)966-972
Number of pages7
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume9
Issue number4
DOIs
Publication statusPublished - 2012 May 31

Keywords

  • Cancer heterogeneity
  • gene network
  • microarray
  • pathway activity
  • sparse supervised learning

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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