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Compensating for unknown confounders in microarray data analysis using filtered permutations

Scheid, Stefanie and Spang, Rainer (2007) Compensating for unknown confounders in microarray data analysis using filtered permutations. Journal of Computational Biology 14 (5), pp. 669-681.

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Other URL: http://online.liebertpub.com/doi/pdf/10.1089/cmb.2007.R009


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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Item type:Article
Date:June 2007
Institutions:Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Funktionelle Genomik (Prof. Oefner)
Identification Number:
ValueType
17683267PubMed ID
10.1089/cmb.2007.R009DOI
Dewey Decimal Classification:600 Technology > 610 Medical sciences Medicine
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Partially
Item ID:34329
Owner only: item control page
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