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AffyMiner effectively deals with multiple replicates in the experiment and takes into account both quantitative and qualitative data in identifying significant genes. AffyMiner has been used for analyzing the GeneChip data and the results were presented in several publications.ĪffyMiner fills an important gap in finding differentially expressed genes in Affymetrix GeneChip microarray data.

AffyMiner consists of the functional modules, GeneFinder for detecting significant genes in a treatment versus control experiment and GOTree for mapping genes of interest onto the Gene Ontology (GO) space and interfaces to run Cluster, a program for clustering analysis, and GenMAPP, a program for pathway analysis. These methods rely solely on the quantitative data, i.e., signal intensity however, qualitative data are also important parameters in detecting differentially expressed genes.ĪffyMiner is a tool developed for detecting differentially expressed genes in Affymetrix GeneChip microarray data and for associating gene annotation and gene ontology information with the genes detected. Affymetrix GeneChips are widely used microarrays, where a variety of statistical algorithms have been explored and used for detecting significant genes in the experiment. With the advance of microarray technology, the challenge issue becomes how to analyze a large amount of microarray data and make biological sense of them. DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously.

Visual studio express 2012 for windows desktop