IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v90y2004i1p19-43.html
   My bibliography  Save this article

Analyzing factorial designed microarray experiments

Author

Listed:
  • Scholtens, Denise
  • Miron, Alexander
  • M. Merchant, Faisal
  • Miller, Arden
  • L. Miron, Penelope
  • Dirk Iglehart, J.
  • Gentleman, Robert

Abstract

High-throughput quantification of gene expression using microarray technology has dramatically changed biological investigation into the roles of genes in normal cell functioning, as well as the mechanisms of disease. We discuss an analytic approach for framing biological questions in terms of statistical parameters to efficiently and confidently answer questions of interest using microarray data from factorial designed experiments. Investigators can extract pertinent and interpretable information from the data about the effects of the factors, their interactions with each other, and the statistical significance of these effects, rather than rely solely on clustering techniques or fold change point estimates in hopes of finding co-expressed genes. By first examining how biological mechanisms are reflected in mRNA transcript abundance, investigators can better design microarray experiments to answer the most interesting questions.

Suggested Citation

  • Scholtens, Denise & Miron, Alexander & M. Merchant, Faisal & Miller, Arden & L. Miron, Penelope & Dirk Iglehart, J. & Gentleman, Robert, 2004. "Analyzing factorial designed microarray experiments," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 19-43, July.
  • Handle: RePEc:eee:jmvana:v:90:y:2004:i:1:p:19-43
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(04)00031-4
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Leland H. Hartwell & John J. Hopfield & Stanislas Leibler & Andrew W. Murray, 1999. "From molecular to modular cell biology," Nature, Nature, vol. 402(6761), pages 47-52, December.
    2. Ibrahim J. G. & Chen M-H. & Gray R. J., 2002. "Bayesian Models for Gene Expression With DNA Microarray Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 88-99, March.
    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. Montazeri Zahra & Yanofsky Corey M. & Bickel David R., 2010. "Shrinkage Estimation of Effect Sizes as an Alternative to Hypothesis Testing Followed by Estimation in High-Dimensional Biology: Applications to Differential Gene Expression," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-33, June.

    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. 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.
    2. 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.
    3. Nicola Bellomo & Richard Bingham & Mark A.J. Chaplain & Giovanni Dosi & Guido Forni & Damian A. Knopoff & John Lowengrub & Reidun Twarock & Maria Enrica Virgillito, 2020. "A multi-scale model of virus pandemic: Heterogeneous interactive entities in a globally connected world," LEM Papers Series 2020/16, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    4. HyungJun Cho & Jaewoo Kang & Jae Lee, 2009. "Empirical Bayes analysis of unreplicated microarray data," Computational Statistics, Springer, vol. 24(3), pages 393-408, August.
    5. Lazaros K Gallos & Fabricio Q Potiguar & José S Andrade Jr & Hernan A Makse, 2013. "IMDB Network Revisited: Unveiling Fractal and Modular Properties from a Typical Small-World Network," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-8, June.
    6. T. Ochiai & J. C. Nacher, 2007. "Stochastic analysis of autoregulatory gene expression dynamics," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 14(4), pages 377-388, November.
    7. Qing-Ju Jiao & Yan-Kai Zhang & Lu-Ning Li & Hong-Bin Shen, 2011. "BinTree Seeking: A Novel Approach to Mine Both Bi-Sparse and Cohesive Modules in Protein Interaction Networks," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-12, November.
    8. Manfred Füllsack, 2011. "Firstness - As seen from the perspective of Complexity Research," E-LOGOS, Prague University of Economics and Business, vol. 2011(1), pages 1-19.
    9. 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.
    10. Erin M Conlon & Bradley L Postier & Barbara A Methé & Kelly P Nevin & Derek R Lovley, 2012. "A Bayesian Model for Pooling Gene Expression Studies That Incorporates Co-Regulation Information," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.
    11. Simeon D. Castle & Michiel Stock & Thomas E. Gorochowski, 2024. "Engineering is evolution: a perspective on design processes to engineer biology," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    12. Romualdo Pastor-Satorras & Eric Smith & Ricard V. Solé, 2002. "Evolving Protein Interaction Networks through Gene Duplication," Working Papers 02-02-008, Santa Fe Institute.
    13. Izmirlian, Grant, 2020. "Strong consistency and asymptotic normality for quantities related to the Benjamini–Hochberg false discovery rate procedure," Statistics & Probability Letters, Elsevier, vol. 160(C).
    14. Frederic Li Mow Chee & Bruno Beernaert & Billie G. C. Griffith & Alexander E. P. Loftus & Yatendra Kumar & Jimi C. Wills & Martin Lee & Jessica Valli & Ann P. Wheeler & J. Douglas Armstrong & Maddy Pa, 2023. "Mena regulates nesprin-2 to control actin–nuclear lamina associations, trans-nuclear membrane signalling and gene expression," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    15. Joshua S Weitz & Philip N Benfey & Ned S Wingreen, 2007. "Evolution, Interactions, and Biological Networks," PLOS Biology, Public Library of Science, vol. 5(1), pages 1-3, January.
    16. Bo Xu & Hongfei Lin & Yang Chen & Zhihao Yang & Hongfang Liu, 2013. "Protein Complex Identification by Integrating Protein-Protein Interaction Evidence from Multiple Sources," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    17. Miguel Fribourg & Diomedes E Logothetis & Javier González-Maeso & Stuart C Sealfon & Belén Galocha-Iragüen & Fernando Las-Heras Andrés & Vladimir Brezina, 2017. "Elucidation of molecular kinetic schemes from macroscopic traces using system identification," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-34, February.
    18. Robrecht Cannoodt & Joeri Ruyssinck & Jan Ramon & Katleen De Preter & Yvan Saeys, 2018. "IncGraph: Incremental graphlet counting for topology optimisation," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-11, April.
    19. Margaritis Voliotis & Philipp Thomas & Ramon Grima & Clive G Bowsher, 2016. "Stochastic Simulation of Biomolecular Networks in Dynamic Environments," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-18, June.
    20. Pilar García-Peñarrubia & Juan J Gálvez & Jesús Gálvez, 2011. "Spatio-Temporal Dependence of the Signaling Response in Immune-Receptor Trafficking Networks Regulated by Cell Density: A Theoretical Model," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-12, July.

    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:eee:jmvana:v:90:y:2004:i:1:p:19-43. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.