A1 Refereed original research article in a scientific journal
Resampling reveals sample-level differential expression in clinical genome-wide studies
Authors: Hiissa J, Elo LL, Huhtinen K, Perheentupa A, Poutanen M, Aittokallio T
Publication year: 2009
Journal: OMICS
Journal name in source: Omics : a journal of integrative biology
Journal acronym: OMICS
Volume: 13
Issue: 5
First page : 381
Last page: 96
Number of pages: 16
ISSN: 1536-2310
eISSN: 1557-8100
DOI: https://doi.org/10.1089/omi.2009.0027
Abstract
Genome-scale molecular profiling of clinical sample material often results in heterogeneous datasets beyond the capability of standard statistical procedures. Statistical tests for differential expression, in particular, rely upon the assumption that the sample groups being compared are relatively homogeneous. Such assumption rarely holds in clinical materials, which leads to detection of secondary findings (false positives) or loss of significant targets (false negatives). Here, we introduce a resampling-based procedure, named ReScore, which aggregates individual changes across all the samples while preserving their clinical classes, and thereby provides multiple sets of markers that can effectively characterize distinct sample subsets. When applied to a public leukemia microarray study, the procedure could accurately reveal hidden subgroup structures associated with underlying genotypic abnormalities. The procedure improved both the sensitivity and specificity of the findings, as well as helped us to identify several disease subtype-specific genes that have remained undetected in the conventional analyses. In our endometriosis study, we were able to accurately distinguish between various sources of systematic variation, linked, for example, to tissue-specificity and disease-related factors, many of which would have been missed with standard approaches. The generic procedure should benefit also other global profiling experiments such as those based on mass spectrometry-based proteomic assays.
Genome-scale molecular profiling of clinical sample material often results in heterogeneous datasets beyond the capability of standard statistical procedures. Statistical tests for differential expression, in particular, rely upon the assumption that the sample groups being compared are relatively homogeneous. Such assumption rarely holds in clinical materials, which leads to detection of secondary findings (false positives) or loss of significant targets (false negatives). Here, we introduce a resampling-based procedure, named ReScore, which aggregates individual changes across all the samples while preserving their clinical classes, and thereby provides multiple sets of markers that can effectively characterize distinct sample subsets. When applied to a public leukemia microarray study, the procedure could accurately reveal hidden subgroup structures associated with underlying genotypic abnormalities. The procedure improved both the sensitivity and specificity of the findings, as well as helped us to identify several disease subtype-specific genes that have remained undetected in the conventional analyses. In our endometriosis study, we were able to accurately distinguish between various sources of systematic variation, linked, for example, to tissue-specificity and disease-related factors, many of which would have been missed with standard approaches. The generic procedure should benefit also other global profiling experiments such as those based on mass spectrometry-based proteomic assays.
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