VAMPIRE (variance-modeled posterior inference with regional exponentials) was originally developed to interpret one-channel microarray data, such as Affymetrix oligonucleotide arrays. Given a summary measure of gene expression, such as the Affymetrix MAS 5.0 scores for each microarray feature (or probe set), it determines the optimal variance model parameters for a two-component variance model. The expression-independent variance represents a constant “background” noise that affects all array features to the same extent, while the expression-dependent variance represents a proportional noise that increases with gene expression. Low-intensity features thus have larger proportional of noise, because of the influence of expression-independent variance. With this optimized model, VAMPIRE then computes a Bayesian statistical test to determine whether observed changes in intensity are statistically significant.
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Albert Hsiao, Trey Ideker, Jerrold M. Olefsky and Shankar Subramaniam
VAMPIRE microarray suite: a web-based platform for the interpretation of gene expression data
Nucleic Acids Research 33 (suppl 2): W627-W632.