PQN and DQN: algorithms for expression microarrays.
An ideal expression algorithm should be able to tell truly different expression levels with small false positive errors and be robust to assay changes. We propose two algorithms. PQN is the non-central trimmed mean of perfect match intensities with quantile normalization. DQN is the non-central trimmed mean of differences between perfect match and mismatch intensities with quantile normalization. The quantiles for normalization can be either empirical or theoretical. When array types and/or assay change in a study, the normalization to common quantiles at the probe set level is essential. We compared DQN, PQN, RMA, GCRMA, DCHIP, PLIER and MAS5 for the Affymetrix Latin square data and our data of two sets of experiments using the same bone marrow but different types of microarrays and different assay. We found the computation for AUC of ROC at affycomp.biostat.jhsph.edu can be improved.
Liu
Wei-min
WM
Roche Molecular Systems, Inc., 4300 Hacienda Drive, Pleasanton, CA 94588, USA. wei-min.liu@roche.com
Li
Rui
R
Sun
James Z
JZ
Wang
Jing
J
Tsai
Julie
J
Wen
Wei
W
Kohlmann
Alexander
A
Williams
P Mickey
PM
D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012372:ROC Curve