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A Composite Model for Subgroup Identification and Prediction via Bicluster Analysis

Author

Listed:
  • Hung-Chia Chen
  • Wen Zou
  • Tzu-Pin Lu
  • James J Chen

Abstract

Background: A major challenges in the analysis of large and complex biomedical data is to develop an approach for 1) identifying distinct subgroups in the sampled populations, 2) characterizing their relationships among subgroups, and 3) developing a prediction model to classify subgroup memberships of new samples by finding a set of predictors. Each subgroup can represent different pathogen serotypes of microorganisms, different tumor subtypes in cancer patients, or different genetic makeups of patients related to treatment response. Methods: This paper proposes a composite model for subgroup identification and prediction using biclusters. A biclustering technique is first used to identify a set of biclusters from the sampled data. For each bicluster, a subgroup-specific binary classifier is built to determine if a particular sample is either inside or outside the bicluster. A composite model, which consists of all binary classifiers, is constructed to classify samples into several disjoint subgroups. The proposed composite model neither depends on any specific biclustering algorithm or patterns of biclusters, nor on any classification algorithms. Results: The composite model was shown to have an overall accuracy of 97.4% for a synthetic dataset consisting of four subgroups. The model was applied to two datasets where the sample’s subgroup memberships were known. The procedure showed 83.7% accuracy in discriminating lung cancer adenocarcinoma and squamous carcinoma subtypes, and was able to identify 5 serotypes and several subtypes with about 94% accuracy in a pathogen dataset. Conclusion: The composite model presents a novel approach to developing a biclustering-based classification model from unlabeled sampled data. The proposed approach combines unsupervised biclustering and supervised classification techniques to classify samples into disjoint subgroups based on their associated attributes, such as genotypic factors, phenotypic outcomes, efficacy/safety measures, or responses to treatments. The procedure is useful for identification of unknown species or new biomarkers for targeted therapy.

Suggested Citation

  • Hung-Chia Chen & Wen Zou & Tzu-Pin Lu & James J Chen, 2014. "A Composite Model for Subgroup Identification and Prediction via Bicluster Analysis," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-14, October.
  • Handle: RePEc:plo:pone00:0111318
    DOI: 10.1371/journal.pone.0111318
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    References listed on IDEAS

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    1. Hung-Chia Chen & Wen Zou & Yin-Jing Tien & James J Chen, 2013. "Identification of Bicluster Regions in a Binary Matrix and Its Applications," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-13, August.
    2. Mihee Lee & Haipeng Shen & Jianhua Z. Huang & J. S. Marron, 2010. "Biclustering via Sparse Singular Value Decomposition," Biometrics, The International Biometric Society, vol. 66(4), pages 1087-1095, December.
    3. Wen Zou & Hung-Chia Chen & Kelley B Hise & Hailin Tang & Steven L Foley & Joe Meehan & Wei-Jiun Lin & Rajesh Nayak & Joshua Xu & Hong Fang & James J Chen, 2013. "Meta-Analysis of Pulsed-Field Gel Electrophoresis Fingerprints Based on a Constructed Salmonella Database," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-9, March.
    4. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
    5. Andrea H. Bild & Guang Yao & Jeffrey T. Chang & Quanli Wang & Anil Potti & Dawn Chasse & Mary-Beth Joshi & David Harpole & Johnathan M. Lancaster & Andrew Berchuck & John A. Olson & Jeffrey R. Marks &, 2006. "Oncogenic pathway signatures in human cancers as a guide to targeted therapies," Nature, Nature, vol. 439(7074), pages 353-357, January.
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