A composite model for subgroup identification and prediction via bicluster analysis
Journal
PLoS ONE
Journal Volume
9
Journal Issue
10
Date Issued
2014
Author(s)
Abstract
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.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.
Other Subjects
biological marker; tumor marker; Article; breast cancer; cancer classification; cancer patient; classification algorithm; cluster analysis; diagnostic accuracy; diagnostic test accuracy study; diagonal linear discriminant analysis; genotype; human; lung adenocarcinoma; lung cancer; lung squamous cell carcinoma; nonhuman; phenotype; prediction; random forest; Salmonella; sensitivity and specificity; serotype; support vector machine; algorithm; classification; cluster analysis; information processing; Algorithms; Biomarkers, Tumor; Cluster Analysis; Datasets as Topic; Humans
Publisher
Public Library of Science
Type
journal article