Exploiting sample variability to enhance multivariate analysis of microarray dataCitation formats

Standard

Exploiting sample variability to enhance multivariate analysis of microarray data. / Möller-Levet, Carla S.; West, Catharine M.; Miller, Crispin J.

In: Bioinformatics, Vol. 23, No. 20, 15.10.2007, p. 2733-2740.

Research output: Contribution to journalArticle

Harvard

APA

Vancouver

Author

Möller-Levet, Carla S. ; West, Catharine M. ; Miller, Crispin J. / Exploiting sample variability to enhance multivariate analysis of microarray data. In: Bioinformatics. 2007 ; Vol. 23, No. 20. pp. 2733-2740.

Bibtex

@article{4bafac3f7e5f479eaf7524322cc8b89f,
title = "Exploiting sample variability to enhance multivariate analysis of microarray data",
abstract = "Motivation: Biological and technical variability is intrinsic in any microarray experiment. While most approaches aim to account for this variability, they do not actively exploit it. Here, we consider a novel approach that uses the variability between arrays to provide an extra source of information that can enhance gene expression analyses. Results: We develop a method that uses sample similarity to incorporate sample variability into the analysis of gene expression profiles. This allows each pairwise correlation calculation to borrow information from all the data in the experiment. Results on synthetic and human cancer microarray datasets show that the inclusion of this information leads to a significant increase in the ability to identify previously characterized relationships and a reduction in false discovery rate, when compared to a standard analysis using Pearson correlation. The information carried by the variability between arrays can be exploited to significantly improve the analysis of gene expression data. {\circledC} The Author 2007. Published by Oxford University Press. All rights reserved.",
keywords = "Algorithms, Computer Simulation, Data Interpretation, Statistical, methods: Gene Expression Profiling, genetics: Genetic Variation, Models, Genetic, Models, Statistical, methods: Oligonucleotide Array Sequence Analysis, Reproducibility of Results, Sample Size, Sensitivity and Specificity",
author = "M{\"o}ller-Levet, {Carla S.} and West, {Catharine M.} and Miller, {Crispin J.}",
year = "2007",
month = "10",
day = "15",
doi = "10.1093/bioinformatics/btm441",
language = "English",
volume = "23",
pages = "2733--2740",
journal = "Bioinformatics (Oxford, England)",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "20",

}

RIS

TY - JOUR

T1 - Exploiting sample variability to enhance multivariate analysis of microarray data

AU - Möller-Levet, Carla S.

AU - West, Catharine M.

AU - Miller, Crispin J.

PY - 2007/10/15

Y1 - 2007/10/15

N2 - Motivation: Biological and technical variability is intrinsic in any microarray experiment. While most approaches aim to account for this variability, they do not actively exploit it. Here, we consider a novel approach that uses the variability between arrays to provide an extra source of information that can enhance gene expression analyses. Results: We develop a method that uses sample similarity to incorporate sample variability into the analysis of gene expression profiles. This allows each pairwise correlation calculation to borrow information from all the data in the experiment. Results on synthetic and human cancer microarray datasets show that the inclusion of this information leads to a significant increase in the ability to identify previously characterized relationships and a reduction in false discovery rate, when compared to a standard analysis using Pearson correlation. The information carried by the variability between arrays can be exploited to significantly improve the analysis of gene expression data. © The Author 2007. Published by Oxford University Press. All rights reserved.

AB - Motivation: Biological and technical variability is intrinsic in any microarray experiment. While most approaches aim to account for this variability, they do not actively exploit it. Here, we consider a novel approach that uses the variability between arrays to provide an extra source of information that can enhance gene expression analyses. Results: We develop a method that uses sample similarity to incorporate sample variability into the analysis of gene expression profiles. This allows each pairwise correlation calculation to borrow information from all the data in the experiment. Results on synthetic and human cancer microarray datasets show that the inclusion of this information leads to a significant increase in the ability to identify previously characterized relationships and a reduction in false discovery rate, when compared to a standard analysis using Pearson correlation. The information carried by the variability between arrays can be exploited to significantly improve the analysis of gene expression data. © The Author 2007. Published by Oxford University Press. All rights reserved.

KW - Algorithms

KW - Computer Simulation

KW - Data Interpretation, Statistical

KW - methods: Gene Expression Profiling

KW - genetics: Genetic Variation

KW - Models, Genetic

KW - Models, Statistical

KW - methods: Oligonucleotide Array Sequence Analysis

KW - Reproducibility of Results

KW - Sample Size

KW - Sensitivity and Specificity

U2 - 10.1093/bioinformatics/btm441

DO - 10.1093/bioinformatics/btm441

M3 - Article

VL - 23

SP - 2733

EP - 2740

JO - Bioinformatics (Oxford, England)

JF - Bioinformatics (Oxford, England)

SN - 1367-4803

IS - 20

ER -