On the Robustness of the Biological Correlation Network Model

Kathryn M. Dempsey, Hesham H. Ali

Abstract

Recent progress in high-throughput technology has resulted in a significant data overload. Determining how to obtain valuable knowledge from such massive raw data has become one of the most challenging issues in biomedical research. As a result, bioinformatics researchers continue to look for advanced data analysis tools to analysis and mine the available data. Correlation network models obtained from various biological assays, such as those measuring gene expression levels, are a powerful method for representing correlated expression. Although correlation does not always imply causation, the correlation network has been shown to be effective in identifying elements of interest in various bioinformatics applications. While these models have found success, little to no investigation has been made into the robustness of relationships in the correlation network with regard to vulnerability of the model according to manipulation of sample values. Particularly, reservations about the correlation network model stem from a lack of testing on the reliability of the model. In this work, we probe the robustness of the model by manipulating samples to create six different expression networks and find a slight inverse relationship between sample count and network size/density. When samples are iteratively removed during model creation, the results suggest that network edges may or may not remain within the statistical parameters of the model, suggesting that there is room for improvement in the filtering of these networks. A cursory investigation into a secondary robustness threshold using these measures confirms the existence of a positive relationship between sample size and edge robustness. This work represents an important step toward better understanding of the critical noise versus signal issue in the correlation network model.

References

  1. Halappanavar M., Feo J., Dempsey K., Ali H., Bhowmick S. A Novel Multithreaded Algorithm for Extracting Maximal Chordal Subgraphs. ICPP 2012:58-67.
  2. Dempsey K., Bonasera S., Bastola D., Ali H. H. A novel correlation networks approach for the identification of gene targets. HICSS 2011:1-8.
  3. Song L., Langfelder P., Horvath S. Comparison of coexpression measures: Mutual information, correlation, and model based indices. BMC Bioinformatics. 2012;13(1):328.
  4. Opgen-Rhein R., Strimmer K. From correlation to causation networks: A simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst Biol. 2007;1:37.
  5. Horvath S., Dong J. Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol. 2008;4(8):e1000117.
  6. Verbitsky M., Pavlidis P., Kandel E., Gilliam C., Yonan A., Malleret G. Altered Hippocampal transcript profile accompanies an age-related spatial memory deficit in mice. Learn Mem 2004 May-Jun;11(3):253-60.
  7. Bender A, Beckers J, Schneider I, Hölter SM et al. Creatine improves health and survival of mice. Neurobiol Aging 2008 Sep;29(9):1404-11.
  8. Ikushima M., Misaizu A. GEO Accession GSE46384. http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=G SE46384
  9. Bader G. D., Hogue C. W. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 2003 Jan 13; 4:2.
  10. Backes C., Keller A., Kuentzer J., Kneissl B., Comtesse N., Elnakady Y. A., Müller R., Meese E., Lenhof H. P. Genetrail - advanced gene set enrichment analysis. Nucleic Acids Res 2007 Jul; 35(Web Server Issue):W186-92.
  11. Reverter A., Chan E. K. Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics. 2008;24(21):2491-2497.
  12. Song L., Langfelder P., Horvath S. Comparison of coexpression measures: Mutual information, correlation, and model based indices. BMC Bioinformatics. 2012;13(1):328.
  13. Opgen-Rhein R., Strimmer K. From correlation to causation networks: A simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst Biol. 2007;1:37.
  14. Song L., Langfelder P., Horvath S. Comparison of coexpression measures: Mutual information, correlation, and model based indices. BMC Bioinformatics. 2012;13(1):328.
  15. Opgen-Rhein R., Strimmer K.. From correlation to causation networks: A simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst Biol. 2007;1:37.
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Paper Citation


in Harvard Style

M. Dempsey K. and H. Ali H. (2014). On the Robustness of the Biological Correlation Network Model . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014) ISBN 978-989-758-012-3, pages 186-195. DOI: 10.5220/0004805801860195


in Bibtex Style

@conference{bioinformatics14,
author={Kathryn M. Dempsey and Hesham H. Ali},
title={On the Robustness of the Biological Correlation Network Model},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)},
year={2014},
pages={186-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004805801860195},
isbn={978-989-758-012-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)
TI - On the Robustness of the Biological Correlation Network Model
SN - 978-989-758-012-3
AU - M. Dempsey K.
AU - H. Ali H.
PY - 2014
SP - 186
EP - 195
DO - 10.5220/0004805801860195