The clustering of regression models method with applications in gene expression data.

Full Abstract

Identification of differentially expressed genes and clustering of genes are two important and complementary objectives addressed with gene expression data. For the differential expression question, many "per-gene" analytic methods have been proposed. These methods can generally be characterized as using a regression function to independently model the observations for each gene; various adjustments for multiplicity are then used to interpret the statistical significance of these per-gene regression models over the collection of genes analyzed. Motivated by this common structure of per-gene models, we proposed a new model-based clustering method--the clustering of regression models method, which groups genes that share a similar relationship to the covariate(s). This method provides a unified approach for a family of clustering procedures and can be applied for data collected with various experimental designs. In addition, when combined with per-gene methods for assessing differential expression that employ the same regression modeling structure, an integrated framework for the analysis of microarray data is obtained. The proposed methodology was applied to two microarray data sets, one from a breast cancer study and the other from a yeast cell cycle study.

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Author information

Author/s: Qin, Li-Xuan (LX); Self, Steven G (SG);

Affiliation: Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA. qinl(-atsign-)mskcc.org

Grants: 1 U01 AI46703 (Agency:NIAID NIH HHS) ; 2 R37 AI29168 (Agency:NIAID NIH HHS) ; R01 AG014358 (Agency:NIA NIH HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't

Journal: Biometrics (Biometrics), published in United States. (Language: eng)

Reference: 2006-Jun; vol 62 (issue 2) : pp 526-33

Dates: Created 2006/08/21; Completed 2006/09/28; Revised 2007/11/14;

PMID: 16918917, status: MEDLINE (last retrieval date: 12/26/2008)

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Biometry
Breast Neoplasms - genetics
Cell Cycle - genetics
Cluster Analysis
Female
Gene Expression Profiling - statistics & numerical data

Humans
Models, Genetic
Models, Statistical
Oligonucleotide Array Sequence Analysis - statistics & numerical data
Regression Analysis
Saccharomyces cerevisiae - cytology; genetics

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