A Resampling Based Clustering Algorithm for Replicated Gene Expression Data
Refereed conference paper presented and published in conference proceedings

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AbstractIn gene expression data analysis, clustering is a fruitful exploratory technique to reveal the underlying molecular mechanism by identifying groups of co-expressed genes. To reduce the noise, usually multiple experimental replicates are performed. An integrative analysis of the full replicate data, instead of reducing the data to the mean profile, carries the promise of yielding more precise and robust clusters. In this paper, we propose a novel resampling based clustering algorithm for genes with replicated expression measurements. Assuming those replicates are exchangeable, we formulate the problem in the bootstrap framework, and aim to infer the consensus clustering based on the bootstrap samples of replicates. In our approach, we adopt the mixed effect model to accommodate the heterogeneous variances and implement a quasi-MCMC algorithm to conduct statistical inference. Experiments demonstrate that by taking advantage of the full replicate data, our algorithm produces more reliable clusters and has robust performance in diverse scenarios, especially when the data is subject to multiple sources of variance.
All Author(s) ListLi H., Li C., Hu J., Fan X.
Detailed descriptionDOI: 10.1109/TCBB.2015.2403320.
Volume Number12
Issue Number6
PublisherIEEE Computer Society
Place of PublicationUnited States
Pages1295 - 1303
LanguagesEnglish-United Kingdom
KeywordsGene clustering, integrative analysis, mixed effect model, replicated microarray data

Last updated on 2020-22-09 at 02:26