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Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis

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  • Xin Victoria Wang
  • Roel G W Verhaak
  • Elizabeth Purdom
  • Paul T Spellman
  • Terence P Speed

Abstract

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/.

Suggested Citation

  • Xin Victoria Wang & Roel G W Verhaak & Elizabeth Purdom & Paul T Spellman & Terence P Speed, 2011. "Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-14, March.
  • Handle: RePEc:plo:pone00:0017691
    DOI: 10.1371/journal.pone.0017691
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    References listed on IDEAS

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    1. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    2. Scharpf, Robert B. & Tjelmeland, HÃ¥kon & Parmigiani, Giovanni & Nobel, Andrew B., 2009. "A Bayesian Model for Cross-Study Differential Gene Expression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1295-1310.
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    Cited by:

    1. Roberta De Vito & Ruggero Bellio & Lorenzo Trippa & Giovanni Parmigiani, 2019. "Multi‐study factor analysis," Biometrics, The International Biometric Society, vol. 75(1), pages 337-346, March.
    2. Gilbert S. Omenn, 2016. "Strategies for Genomic and Proteomic Profiling of Cancers," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 1-7, June.

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