IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v64y2002i4p717-736.html
   My bibliography  Save this article

A statistical framework for expression‐based molecular classification in cancer

Author

Listed:
  • Giovanni Parmigiani
  • Elizabeth S. Garrett
  • Ramaswamy Anbazhagan
  • Edward Gabrielson

Abstract

Summary. Genome‐wide measurement of gene expression is a promising approach to the identification of subclasses of cancer that are currently not differentiable, but potentially biologically heterogeneous. This type of molecular classification gives hope for highly individualized and more effective prognosis and treatment of cancer. Statistically, the analysis of gene expression data from unclassified tumours is a complex hypothesis‐generating activity, involving data exploration, modelling and expert elicitation. We propose a modelling framework that can be used to inform and organize the development of exploratory tools for classification. Our framework uses latent categories to provide both a statistical definition of differential expression and a precise, experiment‐independent, definition of a molecular profile. It also generates natural similarity measures for traditional clustering and gives probabilistic statements about the assignment of tumours to molecular profiles.

Suggested Citation

  • Giovanni Parmigiani & Elizabeth S. Garrett & Ramaswamy Anbazhagan & Edward Gabrielson, 2002. "A statistical framework for expression‐based molecular classification in cancer," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 717-736, October.
    • Handle: RePEc:bla:jorssb:v:64:y:2002:i:4:p:717-736
    DOI: 10.1111/1467-9868.00358
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9868.00358
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-9868.00358?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
    ---><---

    Citations

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


    Cited by:

    1. E. M. Conlon & B. L. Postier & B. A. Methe & K. P. Nevin & D. R. Lovley, 2009. "Hierarchical Bayesian meta-analysis models for cross-platform microarray studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(10), pages 1067-1085.
    2. 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.
    3. Khalili, Abbas & Huang, Tim & Lin, Shili, 2009. "A robust unified approach to analyzing methylation and gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1701-1710, March.
    4. Rossell David & Guerra Rudy & Scott Clayton, 2008. "Semi-Parametric Differential Expression Analysis via Partial Mixture Estimation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-29, April.
    5. Caffo Brian S & Liu Dongmei & Scharpf Robert B. & Parmigiani Giovanni, 2009. "Likelihood Estimation of Conjugacy Relationships in Linear Models with Applications to High-Throughput Genomics," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-25, May.
    6. Oscar M Rueda & Ramón Díaz-Uriarte, 2007. "Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH," PLOS Computational Biology, Public Library of Science, vol. 3(6), pages 1-8, June.
    7. Vinícius Diniz Mayrink & Flávio Bambirra Gonçalves, 2017. "A Bayesian hidden Markov mixture model to detect overexpressed chromosome regions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 387-412, February.
    8. Peter Langfelder & Paul S Mischel & Steve Horvath, 2013. "When Is Hub Gene Selection Better than Standard Meta-Analysis?," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-16, April.
    9. Alexander M. Franks & Gábor Csárdi & D. Allan Drummond & Edoardo M. Airoldi, 2015. "Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 27-44, March.
    10. Donatello Telesca & Peter Müller & Steven M. Kornblau & Marc A. Suchard & Yuan Ji, 2012. "Modeling Protein Expression and Protein Signaling Pathways," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1372-1384, December.
    11. Raphael Gottardo & Wei Li & W. Evan Johnson & X. Shirley Liu, 2008. "A Flexible and Powerful Bayesian Hierarchical Model for ChIP–Chip Experiments," Biometrics, The International Biometric Society, vol. 64(2), pages 468-478, June.
    12. Donatello Telesca & Lurdes Y.T. Inoue & Mauricio Neira & Ruth Etzioni & Martin Gleave & Colleen Nelson, 2009. "Differential Expression and Network Inferences through Functional Data Modeling," Biometrics, The International Biometric Society, vol. 65(3), pages 793-804, September.

    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:bla:jorssb:v:64:y:2002:i:4:p:717-736. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.