Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data.
BACKGROUND
Many researchers are concerned with the comparability and reliability of microarray gene expression data. Recent completion of the MicroArray Quality Control (MAQC) project provides a unique opportunity to assess reproducibility across multiple sites and the comparability across multiple platforms. The MAQC analysis presented for the conclusion of inter- and intra-platform comparability/reproducibility of microarray gene expression measurements is inadequate. We evaluate the reproducibility/comparability of the MAQC data for 12901 common genes in four titration samples generated from five high-density one-color microarray platforms and the TaqMan technology. We discuss some of the problems with the use of correlation coefficient as metric to evaluate the inter- and intra-platform reproducibility and the percent of overlapping genes (POG) as a measure for evaluation of a gene selection procedure by MAQC.


RESULTS
A total of 293 arrays were used in the intra- and inter-platform analysis. A hierarchical cluster analysis shows distinct differences in the measured intensities among the five platforms. A number of genes show a small fold-change in one platform and a large fold-change in another platform, even though the correlations between platforms are high. An analysis of variance shows thirty percent of gene expressions of the samples show inconsistent patterns across the five platforms. We illustrated that POG does not reflect the accuracy of a selected gene list. A non-overlapping gene can be truly differentially expressed with a stringent cut, and an overlapping gene can be non-differentially expressed with non-stringent cutoff. In addition, POG is an unusable selection criterion. POG can increase or decrease irregularly as cutoff changes; there is no criterion to determine a cutoff so that POG is optimized.


CONCLUSIONS
Using various statistical methods we demonstrate that there are differences in the intensities measured by different platforms and different sites within platform. Within each platform, the patterns of expression are generally consistent, but there is site-by-site variability. Evaluation of data analysis methods for use in regulatory decision should take no treatment effect into consideration, when there is no treatment effect, "a fold-change cutoff with a non-stringent p-value cutoff" could result in 100% false positive error selection.
Chen
James J
JJ
Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, USA. jamesj.chen@fda.hhs.gov
Hsueh
Huey-Miin
HM
Delongchamp
Robert R
RR
Lin
Chien-Ju
CJ
Tsai
Chen-An
CA
D016000:Cluster Analysis; D015342:DNA Probes / Q000032:analysis; D030541:Databases, Genetic; D019544:Equipment Failure Analysis; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000295:instrumentation / Q000379:methods / Q000592:standards; D020411:Oligonucleotide Array Sequence Analysis / Q000295:instrumentation / Q000379:methods* / Q000592:standards; D040081:Protein Array Analysis / Q000295:instrumentation / Q000379:methods* / Q000592:standards; D011786:Quality Control; D012015:Reference Standards; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D015342:DNA Probes