Identifying and Quantifying Sources of Variation in Microarray Data Using High-Density cDNA Membrane Arrays-Reference-Cited by-同舟云学术

Identifying and Quantifying Sources of Variation in Microarray Data Using High-Density cDNA Membrane Arrays

Published:2002-08 Issue:4 Volume:9 Page:655-669
ISSN:1066-5277
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Coombes Kevin R.¹,Highsmith W. Edward²,Krogmann Tammy A.²,Baggerly Keith A.³,Stivers David N.³,Abruzzo Lynne V.⁴

Affiliation:

1. Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, Houston, TX 77030 and Department of Biomathematics, University of Texas, M.D. Anderson Cancer Center, Houston, TX 77030

2. Department of Pathology, University of Maryland Medical School, Baltimore, MD 21208

3. Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, Houston, TX 77030

4. Department of Hematopathology, University of Texas, M.D. Anderson Cancer Center, Houston, TX 77030

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modelling and Simulation

Link

http://www.liebertpub.com/doi/pdf/10.1089/106652702760277372

Reference31 articles.

1. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

2. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays

3. Global Gene Expression Profiling in Escherichia coliK12

4. Identifying Differentially Expressed Genes in cDNA Microarray Experiments

5. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes

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