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Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods

The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by “batch effects,” the systematic error introduced when samples are processed in multiple batc...

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Autores principales: Chen, Chao, Grennan, Kay, Badner, Judith, Zhang, Dandan, Gershon, Elliot, Jin, Li, Liu, Chunyu
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3046121/
https://www.ncbi.nlm.nih.gov/pubmed/21386892
http://dx.doi.org/10.1371/journal.pone.0017238
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author Chen, Chao
Grennan, Kay
Badner, Judith
Zhang, Dandan
Gershon, Elliot
Jin, Li
Liu, Chunyu
author_facet Chen, Chao
Grennan, Kay
Badner, Judith
Zhang, Dandan
Gershon, Elliot
Jin, Li
Liu, Chunyu
author_sort Chen, Chao
collection PubMed
description The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by “batch effects,” the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.
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spelling pubmed-30461212011-03-08 Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods Chen, Chao Grennan, Kay Badner, Judith Zhang, Dandan Gershon, Elliot Jin, Li Liu, Chunyu PLoS One Research Article The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by “batch effects,” the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples. Public Library of Science 2011-02-28 /pmc/articles/PMC3046121/ /pubmed/21386892 http://dx.doi.org/10.1371/journal.pone.0017238 Text en Chen et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Chen, Chao
Grennan, Kay
Badner, Judith
Zhang, Dandan
Gershon, Elliot
Jin, Li
Liu, Chunyu
Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods
title Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods
title_full Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods
title_fullStr Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods
title_full_unstemmed Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods
title_short Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods
title_sort removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3046121/
https://www.ncbi.nlm.nih.gov/pubmed/21386892
http://dx.doi.org/10.1371/journal.pone.0017238
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