Background Adjustment for DNA Microarrays Using a Database of Microarray Experiments - Sui, Yunxia (Mary Ann Liebert, Inc., 2009)
DNA microarrays have become an indispensable technique in biomedical research. The raw measurements from microarrays undergo a number of preprocessing steps before the data are converted to the genomic level for further analysis. Background adjustment is an important step in preprocessing. Estimating background noise has been challenging because background levels vary a lot from probe to probe, yet there are limited observations on each probe. Most current methods have used the empirical Bayes approach to borrow information across probes on the same array. These approaches shrink the background estimate for either the entire sample or probes sharing similar sequence structures. In this article, we present a solution that is truly probe specific by using a database of large number of microarray experiments. Information is borrowed across samples and background noise is estimated for each probe individually. The ability to obtain probe specific background distributions allows us to extend the dynamic range of gene expression levels. We illustrate the improvement in detecting gene expression variation on two datasets: a Latin Square spike-in experiment from Affymetrix and an Estrogen Receptor experiment with biological replicates. An R package dbRMA implementing our method can be obtained from the authors.