Identifying Gene Pathways Associated with Cancer Characteristics via Sparse Statistical Methods

Authors:
Shuichi Kawano;Teppei Shimamura;Atsushi Niida;Seiya Imoto;Rui Yamaguchi;Masao Nagasaki;Ryo Yoshida;Cristin Print;Satoru Miyano
Affiliations:
Osaka Prefecture University, Sakai;University of Tokyo, Minato-ku;University of Tokyo, Minato-ku;University of Tokyo, Minato-ku;University of Tokyo, Minato-ku;University of Tokyo, Minato-ku;Institute of Statistical Mathematics, Tachikawa;University of Auckland, Auckland;University of Tokyo, Minato-ku
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2012

Citing 4
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Bioinformatics
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning

Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Semi-Supervised Learning

Semi-Supervised Learning

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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.