Compensating for unknown confounders in microarray data analysis using filtered permutations.
Permutation of class labels is a common approach in microarray analysis. It is assumed to produce random score distributions, which are not affected by biological differences between samples. However, hidden confounding variables like the genetic background of patients or undetected experimental artifacts leave traces in the expression data contaminating the score distributions obtained from random permutations. While the effects of known confounders can be compensated using established methodology, little is known on how to deal with unknown confounders. We discuss a computational method called permutation filtering, which aims to borrow information across genes to detect and compensate the effects of unknown confounders.
Max Planck Institute for Molecular Genetics, Computational Diagnostics Group, Berlin, Germany. stefanie.scheid@merck.de
Stefanie
S
Scheid
Rainer
R
Spang
D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data* / Q000639:trends; D006801:Humans; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data* / Q000639:trends; D011897:Random Allocation; D012680:Sensitivity and Specificity