Differential analysis of DNA microarray gene expression data.
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.
Hatfield
G Wesley
GW
Department of Microbiology, Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697, USA. gwhatfie@uci.edu
Hung
She-Pin
SP
Baldi
Pierre
P
D000704:Analysis of Variance; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D004926:Escherichia coli / Q000235:genetics; D005188:False Negative Reactions; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D011336:Probability