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Robust complementary hierarchical clustering for gene expression data analysis by β-divergence

Paper ID Volume ID Publish Year Pages File Format Full-Text
20776 43190 2013 11 PDF Available
Title
Robust complementary hierarchical clustering for gene expression data analysis by β-divergence
Abstract

A hierarchical clustering (HC) algorithm is one of the most widely used unsupervised statistical techniques for analyzing microarray gene expression data. When applying the HC algorithm to the gene expression data to cluster individuals, most of the HC algorithms generate clusters based on the highly differentially expressed (DE) genes that have very similar expression patterns. These highly DE genes may sometimes be irrelevant in biological processes. The serious problem is that those irrelevant genes with high expressions potentially drown out the low expressed genes that have important biological functions. To overcome the problem, Nowak and Tibshirani proposed the complementary hierarchical clustering (CHC) (Biostatistics, 9, 467–483, 2008). However, it is not robust against outlying expression and often produces misleading results if there exist some contaminations in the gene expression data. Thus, we propose the robust CHC (RCHC) method to robustify the CHC with respect to outliers by maximizing the β-likelihood function for sequential extraction of a gene-set with proper groups of individuals. Note that the proposed method reduces to the CHC with the tuning parameter β → 0. A value of β plays a key role in the performance of the RCHC method, which controls the tradeoff between the robustness and efficiency of the estimators. Using simulation and real gene expression analysis, the RCHC method shows robust properties to gene expression clustering with respect to data contaminations, overcomes the problem of the CHC, and predicts critically important genes from breast cancer data.

Keywords
Gene expression; DNA microarray; Robust complementary hierarchical clustering (RCHC); Maximum β-likelihood; Relative gene importance; Selection procedure of β; Robustness
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Robust complementary hierarchical clustering for gene expression data analysis by β-divergence
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Publisher
Database: Elsevier - ScienceDirect
Journal: Journal of Bioscience and Bioengineering - Volume 116, Issue 3, September 2013, Pages 397–407
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us