IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0017691.html
   My bibliography  Save this article

Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0017691
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0017691&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0017691?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aaron C Ericsson & J Wade Davis & William Spollen & Nathan Bivens & Scott Givan & Catherine E Hagan & Mark McIntosh & Craig L Franklin, 2015. "Effects of Vendor and Genetic Background on the Composition of the Fecal Microbiota of Inbred Mice," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-19, February.
    2. Hossain, Ahmed & Beyene, Joseph & Willan, Andrew R. & Hu, Pingzhao, 2009. "A flexible approximate likelihood ratio test for detecting differential expression in microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3685-3695, August.
    3. Xiaohong Li & Guy N Brock & Eric C Rouchka & Nigel G F Cooper & Dongfeng Wu & Timothy E O’Toole & Ryan S Gill & Abdallah M Eteleeb & Liz O’Brien & Shesh N Rai, 2017. "A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-22, May.
    4. Kerr Kathleen F., 2012. "Optimality Criteria for the Design of 2-Color Microarray Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-9, January.
    5. Ambroise Jérôme & Bearzatto Bertrand & Robert Annie & Macq Benoit & Gala Jean-Luc, 2012. "Combining Multiple Laser Scans of Spotted Microarrays by Means of a Two-Way ANOVA Model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-20, February.
    6. J. McClatchy & R. Strogantsev & E. Wolfe & H. Y. Lin & M. Mohammadhosseini & B. A. Davis & C. Eden & D. Goldman & W. H. Fleming & P. Conley & G. Wu & L. Cimmino & H. Mohammed & A. Agarwal, 2023. "Clonal hematopoiesis related TET2 loss-of-function impedes IL1β-mediated epigenetic reprogramming in hematopoietic stem and progenitor cells," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    7. Alexandra Gyurdieva & Stefan Zajic & Ya-Fang Chang & E. Andres Houseman & Shan Zhong & Jaegil Kim & Michael Nathenson & Thomas Faitg & Mary Woessner & David C. Turner & Aisha N. Hasan & John Glod & Ro, 2022. "Biomarker correlates with response to NY-ESO-1 TCR T cells in patients with synovial sarcoma," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    8. Sora Yoon & Seon-Young Kim & Dougu Nam, 2016. "Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-16, November.
    9. Yu Lianbo & Gulati Parul & Fernandez Soledad & Pennell Michael & Kirschner Lawrence & Jarjoura David, 2011. "Fully Moderated T-statistic for Small Sample Size Gene Expression Arrays," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-22, September.
    10. Chaofeng Yuan & Wensheng Zhu & Xuming He & Jianhua Guo, 2019. "A mixture factor model with applications to microarray data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 60-76, March.
    11. Nan Li & Matthew N. McCall & Zhijin Wu, 2017. "Establishing Informative Prior for Gene Expression Variance from Public Databases," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 160-177, June.
    12. Brian Caffo & Liu Dongmei & Giovanni Parmigiani, 2004. "Power Conjugate Multilevel Models with Applications to Genomics," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1062, Berkeley Electronic Press.
    13. Nott, David J. & Yu, Zeming & Chan, Eva & Cotsapas, Chris & Cowley, Mark J. & Pulvers, Jeremy & Williams, Rohan & Little, Peter, 2007. "Hierarchical Bayes variable selection and microarray experiments," Journal of Multivariate Analysis, Elsevier, vol. 98(4), pages 852-872, April.
    14. Santu Ghosh & Alan M. Polansky, 2022. "Large-Scale Simultaneous Testing Using Kernel Density Estimation," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 808-843, August.
    15. Qianxing Mo & Faming Liang, 2010. "Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model," Biometrics, The International Biometric Society, vol. 66(4), pages 1284-1294, December.
    16. Ahmed Hossain & Hafiz T.A. Khan, 2016. "Identification of genomic markers correlated with sensitivity in solid tumors to Dasatinib using sparse principal components," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(14), pages 2538-2549, October.
    17. Alexander Kaever & Manuel Landesfeind & Kirstin Feussner & Burkhard Morgenstern & Ivo Feussner & Peter Meinicke, 2014. "Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-12, February.
    18. Iqbal Mahmud & Guimei Tian & Jia Wang & Tarun E. Hutchinson & Brandon J. Kim & Nikee Awasthee & Seth Hale & Chengcheng Meng & Allison Moore & Liming Zhao & Jessica E. Lewis & Aaron Waddell & Shangtao , 2023. "DAXX drives de novo lipogenesis and contributes to tumorigenesis," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    19. Nyangoma Stephen O. & Collins Stuart I. & Altman Douglas G. & Johnson Philip & Billingham Lucinda J., 2012. "Sample Size Calculations for Designing Clinical Proteomic Profiling Studies Using Mass Spectrometry," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-42, February.
    20. Erminia Donnarumma & Michael Kohlhaas & Elodie Vimont & Etienne Kornobis & Thibault Chaze & Quentin Giai Gianetto & Mariette Matondo & Maryse Moya-Nilges & Christoph Maack & Timothy Wai, 2022. "Mitochondrial Fission Process 1 controls inner membrane integrity and protects against heart failure," Nature Communications, Nature, vol. 13(1), pages 1-24, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0017691. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.