Multiplicative background correction for spotted microarrays to improve reproducibility.

We propose a simple approach, the multiplicative background correction, to solve a perplexing problem in spotted microarray data analysis: correcting the foreground intensities for the background noise, especially for spots with genes that are weakly expressed or not at all. The conventional approach, the additive background correction, directly subtracts the background intensities from foreground intensities. When the foreground intensities marginally dominate the background intensities, the additive background correction provides unreliable estimates of the differential gene expression levels and usually presents M-A plots with fishtails or fans. Unreliable additive background correction makes it preferable to ignore the background noise, which may increase the number of false positives. Based on the more realistic multiplicative assumption instead of the conventional additive assumption, we propose to logarithmically transform the intensity readings before the background correction, with the logarithmic transformation symmetrizing the skewed intensity readings. This approach not only precludes the fishtails and fans in the M-A plots, but provides highly reproducible background-corrected intensities for both strongly and weakly expressed genes. The superiority of the multiplicative background correction to the additive one as well as the no background correction is justified by publicly available self-hybridization datasets.