Normalization and Technical Variation in Gene Expression Measurements.
Using data from the Microarray Quality Control (MAQC) project, we demonstrate two data-analysis methods that shed light on the normalization of gene expression measurements and thereby on their technical variation. One is an improved method for normalization of multiple assays with mRNA concentrations related by a parametric model. The other is a method for characterizing limitations on the effectiveness of normalization in reducing technical variation. We apply our improved normalization to the four project materials as part of testing the linearity of the probe responses. We find that the lack of linearity is statistically significant but small enough that its sources cannot be easily identified. Applying our characterization method to assays of the same material, we show that there is a source of variation that cannot be eliminated by normalization and therefore must be dealt with by other means. Four high-density, single probe, one-color microarray platforms underlie our demonstration.
National Institute of Standards and Technology, Gaithersburg, MD 20899-8980.
Walter
W
Liggett
MAQC; calibration linearity; classifier performance; high-density microarrays; inter-laboratory study; latent variables; microarray quality control; simultaneous F tests; sources of variation; statistical dependence