Exploiting sample variability to enhance multivariate analysis of microarray data

2.50
Hdl Handle:
http://hdl.handle.net/10541/70475
Title:
Exploiting sample variability to enhance multivariate analysis of microarray data
Authors:
Möller-Levet, Carla S; West, Catharine M L; Miller, Crispin J
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. AVAILABILITY: Matlab script files are available from the author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Affiliation:
Paterson Institute for Cancer Research, Cancer Research UK, Manchester, M20 4BX, UK. cmoller@picr.man.ac.uk
Citation:
Exploiting sample variability to enhance multivariate analysis of microarray data. 2007, 23 (20):2733-40 Bioinformatics
Journal:
Bioinformatics
Issue Date:
15-Oct-2007
URI:
http://hdl.handle.net/10541/70475
DOI:
10.1093/bioinformatics/btm441
PubMed ID:
17827205
Type:
Article
Language:
en
ISSN:
1460-2059
Appears in Collections:
All Paterson Institute for Cancer Research

Full metadata record

DC FieldValue Language
dc.contributor.authorMöller-Levet, Carla S-
dc.contributor.authorWest, Catharine M L-
dc.contributor.authorMiller, Crispin J-
dc.date.accessioned2009-06-15T12:23:58Z-
dc.date.available2009-06-15T12:23:58Z-
dc.date.issued2007-10-15-
dc.identifier.citationExploiting sample variability to enhance multivariate analysis of microarray data. 2007, 23 (20):2733-40 Bioinformaticsen
dc.identifier.issn1460-2059-
dc.identifier.pmid17827205-
dc.identifier.doi10.1093/bioinformatics/btm441-
dc.identifier.urihttp://hdl.handle.net/10541/70475-
dc.description.abstractMOTIVATION: 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. AVAILABILITY: Matlab script files are available from the author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.en
dc.language.isoenen
dc.subject.meshAlgorithms-
dc.subject.meshComputer Simulation-
dc.subject.meshData Interpretation, Statistical-
dc.subject.meshGene Expression Profiling-
dc.subject.meshGenetic Variation-
dc.subject.meshModels, Genetic-
dc.subject.meshModels, Statistical-
dc.subject.meshOligonucleotide Array Sequence Analysis-
dc.subject.meshReproducibility of Results-
dc.subject.meshSample Size-
dc.subject.meshSensitivity and Specificity-
dc.titleExploiting sample variability to enhance multivariate analysis of microarray dataen
dc.typeArticleen
dc.contributor.departmentPaterson Institute for Cancer Research, Cancer Research UK, Manchester, M20 4BX, UK. cmoller@picr.man.ac.uken
dc.identifier.journalBioinformaticsen

Related articles on PubMed

All Items in Christie are protected by copyright, with all rights reserved, unless otherwise indicated.