A web-based tool for principal component and significance analysis of microarray data

Alexei A. Sharov, Dawood B. Dudekula, Minoru Ko

Research output: Contribution to journalArticle

187 Citations (Scopus)

Abstract

Summary: We have developed a program for microarray data analysis, which features the false discovery rate for testing statistical significance and the principal component analysis using the singular value decomposition method for detecting the global trends of gene-expression patterns. Additional features include analysis of variance with multiple methods for error variance adjustment, correction of cross-channel correlation for two-color microarrays, identification of genes specific to each cluster of tissue samples, biplot of tissues and corresponding tissue-specific genes, clustering of genes that are correlated with each principal component (PC), three-dimensional graphics based on virtual reality modeling language and sharing of PC between different experiments. The software also supports parameter adjustment, gene search and graphical output of results. The software is implemented as a web tool and thus the speed of analysis does not depend on the power of a client computer.

Original languageEnglish
Pages (from-to)2548-2549
Number of pages2
JournalBioinformatics
Volume21
Issue number10
DOIs
Publication statusPublished - 2005 May 15
Externally publishedYes

Fingerprint

Principal Components
Microarrays
Microarray Data
Principal Component Analysis
Web-based
Genes
Gene
Social Adjustment
Tissue
Adjustment
Software
Biplot
Microarray Data Analysis
Statistical Significance
Analysis of variance
Singular value decomposition
Microarray Analysis
Analysis of variance (ANOVA)
Modeling Language
Virtual Reality

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

A web-based tool for principal component and significance analysis of microarray data. / Sharov, Alexei A.; Dudekula, Dawood B.; Ko, Minoru.

In: Bioinformatics, Vol. 21, No. 10, 15.05.2005, p. 2548-2549.

Research output: Contribution to journalArticle

Sharov, Alexei A. ; Dudekula, Dawood B. ; Ko, Minoru. / A web-based tool for principal component and significance analysis of microarray data. In: Bioinformatics. 2005 ; Vol. 21, No. 10. pp. 2548-2549.
@article{dc7a8b239ed84080bb9b6d73a6c4091c,
title = "A web-based tool for principal component and significance analysis of microarray data",
abstract = "Summary: We have developed a program for microarray data analysis, which features the false discovery rate for testing statistical significance and the principal component analysis using the singular value decomposition method for detecting the global trends of gene-expression patterns. Additional features include analysis of variance with multiple methods for error variance adjustment, correction of cross-channel correlation for two-color microarrays, identification of genes specific to each cluster of tissue samples, biplot of tissues and corresponding tissue-specific genes, clustering of genes that are correlated with each principal component (PC), three-dimensional graphics based on virtual reality modeling language and sharing of PC between different experiments. The software also supports parameter adjustment, gene search and graphical output of results. The software is implemented as a web tool and thus the speed of analysis does not depend on the power of a client computer.",
author = "Sharov, {Alexei A.} and Dudekula, {Dawood B.} and Minoru Ko",
year = "2005",
month = "5",
day = "15",
doi = "10.1093/bioinformatics/bti343",
language = "English",
volume = "21",
pages = "2548--2549",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "10",

}

TY - JOUR

T1 - A web-based tool for principal component and significance analysis of microarray data

AU - Sharov, Alexei A.

AU - Dudekula, Dawood B.

AU - Ko, Minoru

PY - 2005/5/15

Y1 - 2005/5/15

N2 - Summary: We have developed a program for microarray data analysis, which features the false discovery rate for testing statistical significance and the principal component analysis using the singular value decomposition method for detecting the global trends of gene-expression patterns. Additional features include analysis of variance with multiple methods for error variance adjustment, correction of cross-channel correlation for two-color microarrays, identification of genes specific to each cluster of tissue samples, biplot of tissues and corresponding tissue-specific genes, clustering of genes that are correlated with each principal component (PC), three-dimensional graphics based on virtual reality modeling language and sharing of PC between different experiments. The software also supports parameter adjustment, gene search and graphical output of results. The software is implemented as a web tool and thus the speed of analysis does not depend on the power of a client computer.

AB - Summary: We have developed a program for microarray data analysis, which features the false discovery rate for testing statistical significance and the principal component analysis using the singular value decomposition method for detecting the global trends of gene-expression patterns. Additional features include analysis of variance with multiple methods for error variance adjustment, correction of cross-channel correlation for two-color microarrays, identification of genes specific to each cluster of tissue samples, biplot of tissues and corresponding tissue-specific genes, clustering of genes that are correlated with each principal component (PC), three-dimensional graphics based on virtual reality modeling language and sharing of PC between different experiments. The software also supports parameter adjustment, gene search and graphical output of results. The software is implemented as a web tool and thus the speed of analysis does not depend on the power of a client computer.

UR - http://www.scopus.com/inward/record.url?scp=19544391863&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=19544391863&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/bti343

DO - 10.1093/bioinformatics/bti343

M3 - Article

VL - 21

SP - 2548

EP - 2549

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 10

ER -