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Compensating for unknown confounders in microarray data analysis using filtered permutations

Scheid, Stefanie ; Spang, Rainer



Abstract

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 ...

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