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• W1569352169 abstract "Summary Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high‐dimensional DNA microarray experiments. We discuss methods using a regularized t ‐test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t ‐test when only a few experimental replicates are available. We also describe a computational method for calculating the global false‐positive and false‐negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment‐wide false‐positive and ‐negative levels driven by experimental error and biological variance." @default.
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• W1569352169 date "2003-02-01" @default.
• W1569352169 modified "2023-10-15" @default.
• W1569352169 title "Differential analysis of DNA microarray gene expression data" @default.
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• W1569352169 doi "https://doi.org/10.1046/j.1365-2958.2003.03298.x" @default.
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