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

  • Victor L. Jong EMAIL logo , Putri W. Novianti , Kit C.B. Roes and Marinus J.C. Eijkemans
Published/Copyright: December 2, 2014
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Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 13 Issue 6

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
Corresponding author: Victor L. Jong, Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands; and Erasmus Medical Center Rotterdam, Department of Viroscience, ‘s Gravendijkwal 230, 3015 CE Rotterdam, The Netherlands, e-mail:

Acknowledgments

This study was financially supported by the VIRGO consortium, which is funded by the Netherlands Genomics Initiative and by the Dutch Government (FES0908). The funding agencies in no way influenced the outcome or conclusions of the study.

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Supplemental Material

The online version of this article (DOI: 10.1515/sagmb-2014-0003) offers supplementary material, available to authorized users.

Published Online: 2014-12-2
Published in Print: 2014-12-1

©2014 by De Gruyter

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https://doi.org/10.1515/sagmb-2014-0003
Keywords for this article
clustering on correlation; gene expression data; homogeneity of correlation structures; microarray analysis

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. When is Menzerath-Altmann law mathematically trivial? A new approach
  4. Covariate adjusted differential variability analysis of DNA methylation with propensity score method
  5. P-value calibration for multiple testing problems in genomics
  6. Robust methods to detect disease-genotype association in genetic association studies: calculate p-values using exact conditional enumeration instead of simulated permutations or asymptotic approximations
  7. Markovianness and conditional independence in annotated bacterial DNA
  8. Exploring homogeneity of correlation structures of gene expression datasets within and between etiological disease categories
  9. Corrigendum
  10. Biological pathway selection through Bayesian integrative modeling
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