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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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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:UNSPECIFIED
Identification Number:
ValueType
17683267PubMed ID
10.1089/cmb.2007.R009DOI
Classification:
NotationType
AlgorithmsMESH
Breast Neoplasms/geneticsMESH
FemaleMESH
Gene Expression Profiling/trendsMESH
HumansMESH
Models, GeneticMESH
Oligonucleotide Array Sequence Analysis/trendsMESH
Random AllocationMESH
Sensitivity and SpecificityMESH
Dewey Decimal Classification:500 Science > 570 Life sciences
600 Technology > 610 Medical sciences Medicine
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:No
Item ID:30685
Owner only: item control page
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