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

Brad Love; David R Rank; Sharron G Penn; David A Jenkins; Russell S Thomas

doi:10.1093/bioinformatics/18.8.1064

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

Brad Love·David R Rank·Sharron G Penn·David A Jenkins·Russell S Thomas

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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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Computer Simulation DNA DNA Replication Gene Expression Gene Expression Profiling Gene Expression Regulation Humans Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis Reproducibility of Results Sensitivity and Specificity Comparative Study Journal Article

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Brad Love, David R Rank, Sharron G Penn, David A Jenkins, Russell S Thomas. 2002. A conditional density error model for the statistical analysis of microarray data.. https://doi.org/10.1093/bioinformatics%2F18.8.1064

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