Analysis Issues for Gene Expression Array Data (Special Report‪)‬

Gene expression array technologies are rapidly emerging for use in various genome-wide studies in biology and medicine (1-3). Results from the recently completed sequencing of the human genome predict that the number of genes in the human genome is much smaller (~33 000 genes) than anticipated (4). This implies that the major complexity in biological mechanisms in humans lies in the synergistic effects among various genes. Therefore, to decipher the secrets of our life and, ultimately, to find the cures for many human diseases, biologists must deal with multiple genes and their interactive transcripts simultaneously. High-throughput biotechnologies, including gene chip approaches, will play an important role in these studies (5). However, quality control over thousands of gene expression values and full utilization of the information from these high-throughput data are extremely difficult, and important issues in quality control and bioinformatic approaches have not been resolved (6, 7). A series of careful analyses on the variability of array instrumentation and on the statistical evaluation of gene expression intensities are required for reliable and consistent inference on gene chip data. Specifically, sources of error and their confidence levels on these high-throughput measurements need to be better understood because some can significantly alter our inferences and conclusions (8). The purpose of this report is to dispel three common misconceptions about array experiments.