Exploring homogeneity of correlation structures of gene expression datasets within and between etiological disease categories

Jong Victor L. (), Novianti Putri W., Roes Kit C.B. and Eijkemans Marinus J.C.
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Jong Victor L.: Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands Erasmus Medical Center Rotterdam, Department of Viroscience, ‘s Gravendijkwal 230, 3015 CE Rotterdam, The Netherlands
Novianti Putri W.: Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
Roes Kit C.B.: Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
Eijkemans Marinus J.C.: Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 6, 717-732

Abstract: The literature shows that classifiers perform differently across datasets and that correlations within datasets affect the performance of classifiers. The question that arises is whether the correlation structure within datasets differ significantly across diseases. In this study, we evaluated the homogeneity of correlation structures within and between datasets of six etiological disease categories; inflammatory, immune, infectious, degenerative, hereditary and acute myeloid leukemia (AML). We also assessed the effect of filtering; detection call and variance filtering on correlation structures. We downloaded microarray datasets from ArrayExpress for experiments meeting predefined criteria and ended up with 12 datasets for non-cancerous diseases and six for AML. The datasets were preprocessed by a common procedure incorporating platform-specific recommendations and the two filtering methods mentioned above. Homogeneity of correlation matrices between and within datasets of etiological diseases was assessed using the Box’s M statistic on permuted samples. We found that correlation structures significantly differ between datasets of the same and/or different etiological disease categories and that variance filtering eliminates more uncorrelated probesets than detection call filtering and thus renders the data highly correlated.

Keywords: clustering on correlation; gene expression data; homogeneity of correlation structures; microarray analysis (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/sagmb-2014-0003

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