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Propagating uncertainty in microarray data analysis
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DOI：10.1093/bib/bbk003.png)
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Abstract
En 中文
Microarray technology is associated with many sources of experimental uncertainty. In this review we discuss a number of approaches for dealing with this uncertainty in the processing of data from microarray experiments. We focus here on the analysis of high-density oligonucleotide arrays, such as the popular Affymetrix GeneChip((R)) array, which contain multiple probes for each target. This set of probes can be used to determine an estimate for the target concentration and can also be used to determine the experimental uncertainty associated with this measurement. This measurement uncertainty can then be propagated through the downstream analysis using probabilistic methods. We give examples showing how these credibility intervals can be used to help identify differential expression, to combine information from replicated experiments and to improve the performance of principal component analysis.
Keywords:
microarray
Affymetrix GeneChip (R)
probabilistic model
gene expression
bayesian inference
principal component analysis
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