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PMID: 19007336 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Review

A review of independent component analysis application to microarray gene expression data.

BioTechniques ·Vol. 45 ·No. 5 ·2008-11-00 ·Pages 501-20

Kong W, Vanderburg CR, Gunshin H, Rogers JT, Huang X

Abstract

Independent component analysis (ICA) methods have received growing attention as effective data-mining tools for microarray gene expression data. As a technique of higher-order statistical analysis, ICA is capable of extracting biologically relevant gene expression features from microarray data. Herein we have reviewed the latest applications and the extended algorithms of ICA in gene clustering, classification, and identification. The theoretical frameworks of ICA have been described to further illustrate its feature extraction function in microarray data analysis.

MeSH Terms

Algorithms Cluster Analysis Gene Expression Humans Least-Squares Analysis Neoplasms/classification,genetics Oligonucleotide Array Sequence Analysis/methods Pattern Recognition, Automated/methods Principal Component Analysis Signal Transduction

Authors & Affiliations

5 authors, click to expand affiliations / ORCID

Kong Wei

Information Engineering College, Shanghai Maritime University, Shanghai, China.

Vanderburg Charles R

Gunshin Hiromi

Rogers Jack T

Huang Xudong

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Article Info

Journal

BioTechniques

Abbr.

Biotechniques

ISSN

0736-6205

DOI

10.2144/000112950

Published

2008-11-00

Pages

501-20

Language

English

Region

England

NLM ID

8306785

PMCID

PMC3005719

Subset

Grants

NIA NIH HHS · R21 AG028850 · United States

NIA NIH HHS · R21 AG028850-02 · United States

NIA NIH HHS · 5R21AG028850 · United States

External Links

PubMed source DOI: 10.2144/000112950 PMC Full Text View journal details

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A review of independent component analysis application to microarray gene expression data.

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