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Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis

In the Cancer Genome Atlas (TCGA) project, gene expression of the same set of samples is measured multiple times on different microarray platforms. There are two main advantages to combining these measurements. First, we have the opportunity to obtain a more precise and accurate estimate of expressi...

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Autores principales: Wang, Xin Victoria, Verhaak, Roel G. W., Purdom, Elizabeth, Spellman, Paul T., Speed, Terence P.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3059153/
https://www.ncbi.nlm.nih.gov/pubmed/21436879
http://dx.doi.org/10.1371/journal.pone.0017691
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author Wang, Xin Victoria
Verhaak, Roel G. W.
Purdom, Elizabeth
Spellman, Paul T.
Speed, Terence P.
author_facet Wang, Xin Victoria
Verhaak, Roel G. W.
Purdom, Elizabeth
Spellman, Paul T.
Speed, Terence P.
author_sort Wang, Xin Victoria
collection PubMed
description In the Cancer Genome Atlas (TCGA) project, gene expression of the same set of samples is measured multiple times on different microarray platforms. There are two main advantages to combining these measurements. First, we have the opportunity to obtain a more precise and accurate estimate of expression levels than using the individual platforms alone. Second, the combined measure simplifies downstream analysis by eliminating the need to work with three sets of expression measures and to consolidate results from the three platforms. We propose to use factor analysis (FA) to obtain a unified gene expression measure (UE) from multiple platforms. The UE is a weighted average of the three platforms, and is shown to perform well in terms of accuracy and precision. In addition, the FA model produces parameter estimates that allow the assessment of the model fit. The R code is provided in File S2. Gene-level FA measurements for the TCGA data sets are available from http://tcga-data.nci.nih.gov/docs/publications/unified_expression/.
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spelling pubmed-30591532011-03-23 Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis Wang, Xin Victoria Verhaak, Roel G. W. Purdom, Elizabeth Spellman, Paul T. Speed, Terence P. PLoS One Research Article In the Cancer Genome Atlas (TCGA) project, gene expression of the same set of samples is measured multiple times on different microarray platforms. There are two main advantages to combining these measurements. First, we have the opportunity to obtain a more precise and accurate estimate of expression levels than using the individual platforms alone. Second, the combined measure simplifies downstream analysis by eliminating the need to work with three sets of expression measures and to consolidate results from the three platforms. We propose to use factor analysis (FA) to obtain a unified gene expression measure (UE) from multiple platforms. The UE is a weighted average of the three platforms, and is shown to perform well in terms of accuracy and precision. In addition, the FA model produces parameter estimates that allow the assessment of the model fit. The R code is provided in File S2. Gene-level FA measurements for the TCGA data sets are available from http://tcga-data.nci.nih.gov/docs/publications/unified_expression/. Public Library of Science 2011-03-11 /pmc/articles/PMC3059153/ /pubmed/21436879 http://dx.doi.org/10.1371/journal.pone.0017691 Text en Wang 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
Wang, Xin Victoria
Verhaak, Roel G. W.
Purdom, Elizabeth
Spellman, Paul T.
Speed, Terence P.
Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis
title Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis
title_full Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis
title_fullStr Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis
title_full_unstemmed Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis
title_short Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis
title_sort unifying gene expression measures from multiple platforms using factor analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3059153/
https://www.ncbi.nlm.nih.gov/pubmed/21436879
http://dx.doi.org/10.1371/journal.pone.0017691
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