Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations

We present statistical methods for analyzing replicated cDNA microarray exp ression data and report the results of a controlled experiment, The study w as conducted to investigate inherent variability in gene expression data an d the extent to which replication in an experiment produces more consistent and reliable findings. We introduce a statistical model to describe the pr obability that mRNA is contained in the target sample tissue, converted to probe, and ultimately detected on the slide. We also introduce a method to analyze the combined data from all replicates, Of the 288 genes considered in this controlled experiment, 32 would be expected to produce strong hybri dization signals because of the known presence of repetitive sequences with in them. Results based on individual replicates, however, show that there a re 55, 36, and 58 highly expressed genes in replicates 1, 2, and 3, respect ively. On the other hand, an analysis by using the combined data from all 3 replicates reveals that only 2 of the 288 genes are incorrectly classified as expressed, Our experiment shows that any single microarray output is su bject to substantial variability. By pooling data from replicates, we can p rovide a more reliable analysis of gene expression data. Therefore, we conc lude that designing experiments with replications will greatly reduce miscl assification rates. We recommend that at least three replicates be used in designing experiments by using cDNA microarrays, particularly when gene exp ression data from single specimens are being analyzed.