Literature DB >> 12176829

A conditional density error model for the statistical analysis of microarray data.

Brad Love1, David R Rank, Sharron G Penn, David A Jenkins, Russell S Thomas.   

Abstract

MOTIVATION: In many microarray experiments, relatively few intra- and inter-array replicate measurements are made due to significant cost limitations and sample availability. Compounding this problem is a lack of robust statistical methods for analyzing gene expression data with limited experimental replicates. As a result, the interpretation of the results of these experiments are difficult with little understanding of the probability of type I and type II errors.
RESULTS: The variability in a series of replicate microarray measurements was modelled using a combination of parametric and non-parametric methods. A 3-dimensional surface was created for the conditional distribution of the variability given the mean signal intensity in both the Cy3 and Cy5 channels. The results were used as the basis for developing statistical methods for analyzing gene expression data with limited experimental replicates. AVAILABILITY: The statistical analysis scripts are available upon request.

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Year:  2002        PMID: 12176829     DOI: 10.1093/bioinformatics/18.8.1064

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  4 in total

Review 1.  Application of microarrays in high-throughput enzymatic profiling.

Authors:  Mahesh Uttamchandani; Xuan Huang; Grace Y J Chen; Lay-Pheng Tan; Shao Q Yao
Journal:  Mol Biotechnol       Date:  2004-11       Impact factor: 2.695

2.  Microarray analyses of gene expression during chondrocyte differentiation identifies novel regulators of hypertrophy.

Authors:  Claudine G James; C Thomas G Appleton; Veronica Ulici; T Michael Underhill; Frank Beier
Journal:  Mol Biol Cell       Date:  2005-08-31       Impact factor: 4.138

3.  Analysis of host response to bacterial infection using error model based gene expression microarray experiments.

Authors:  Dov J Stekel; Donatella Sarti; Victor Trevino; Lihong Zhang; Mike Salmon; Chris D Buckley; Mark Stevens; Mark J Pallen; Charles Penn; Francesco Falciani
Journal:  Nucleic Acids Res       Date:  2005-03-30       Impact factor: 16.971

4.  De-regulation of common housekeeping genes in hepatocellular carcinoma.

Authors:  Samuel Waxman; Elisa Wurmbach
Journal:  BMC Genomics       Date:  2007-07-18       Impact factor: 3.969

  4 in total

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