Correction of technical bias in clinical microarray data improves concordance with known biological information.
The performance of gene expression microarrays has been well characterized using controlled reference samples, but the performance on clinical samples remains less clear. We identified sources of technical bias affecting many genes in concert, thus causing spurious correlations in clinical data sets and false associations between genes and clinical variables. We developed a method to correct for technical bias in clinical microarray data, which increased concordance with known biological relationships in multiple data sets.
Eklund
Aron C
AC
Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology (CHIP@HST), Harvard Medical School, Boston, MA 02115, USA. eklund@cbs.dtu.dk
Szallasi
Zoltan
Z
D001943:Breast Neoplasms / Q000175:diagnosis; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000592:standards* / Q000706:statistics & numerical data*; D005910:Glioma / Q000175:diagnosis; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis*; D020411:Oligonucleotide Array Sequence Analysis / Q000592:standards* / Q000706:statistics & numerical data*; D012015:Reference Standards; D015203:Reproducibility of Results