Importance of Replication in Microarray Gene Expression Studies: Statistical Methods and Evidence from Repetitive cDNA Hybridizations

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 97; no. 18; pp. 9834 - 9839
• Main Authors: Lee, Mei-Ling Ting, Kuo, Frank C., Whitmore, G. A., Sklar, Jeffrey
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences of the United States of America 29.08.2000; National Acad Sciences; National Academy of Sciences; The National Academy of Sciences
• Subjects: Background noise; Biochemistry; Complementary DNA; DNA, Complementary - genetics; Evolution; Experiment design; Experimental replication; False positive errors; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation; Mathematics; Maximum likelihood estimation; Messenger RNA; Models, Statistical; Molecular biology; Oligonucleotide Array Sequence Analysis; Physical Sciences; Probability; Reproducibility of Results; RNA, Messenger - genetics; Statistical methods; Statistical models; Statistics as Topic - methods; Tissue samples
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.97.18.9834

We present statistical methods for analyzing replicated cDNA microarray expression data and report the results of a controlled experiment. The study was conducted to investigate inherent variability in gene expression data and the extent to which replication in an experiment produces more consistent and reliable findings. We introduce a statistical model to describe the probability 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 hybridization signals because of the known presence of repetitive sequences within them. Results based on individual replicates, however, show that there are 55, 36, and 58 highly expressed genes in replicates 1, 2, and 3, respectively. 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 subject to substantial variability. By pooling data from replicates, we can provide a more reliable analysis of gene expression data. Therefore, we conclude that designing experiments with replications will greatly reduce misclassification rates. We recommend that at least three replicates be used in designing experiments by using cDNA microarrays, particularly when gene expression data from single specimens are being analyzed.