  Quality of microarray gene expression data has emerged as a new research
topic. As in other areas, microarray quality is assessed by comparing suitable
numerical summaries across microarrays, so that outliers and trends can be
visualized, and poor quality arrays or variable quality sets of arrays can be
identified. Since each single array comprises tens or hundreds of thousands of
measurements, the challenge is to find numerical summaries which can be used to
make accurate quality calls. To this end, several new quality measures are
introduced based on probe level and probeset level information, all obtained as
a by-product of the low-level analysis algorithms RMA/fitPLM for Affymetrix
GeneChips. Quality landscapes spatially localize chip or hybridization
problems. Numerical chip quality measures are derived from the distributions of
Normalized Unscaled Standard Errors and of Relative Log Expressions. Quality of
chip batches is assessed by Residual Scale Factors. These quality assessment
measures are demonstrated on a variety of datasets (spike-in experiments, small
lab experiments, multi-site studies). They are compared with Affymetrix's
individual chip quality report.
