Correlation networks: Biologically driven relationships from gene expression data

Grogan W. Huff, Kathryn Cooper

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Genes that share transcription factors are biologically driven to show a more likely measurable correlation in their gene expression. No modern method of visualization displays these intricate co-expression and correlation patterns better than a graph. Structural observations about a co-expression graph can reveal the secrets of the biological system that it models, but experimentally validated co-expression graphs are pain-staking work to produce. Present day correlation network analysis shows potential for drawing conclusions from large volumes of biological systems data in an inexpensive and easy-to-produce way; however, work remains to confirm the appropriateness and scope of such methods for specific, scientific application. Toward this effort, we generated a Pearson correlation network from gene expression data available to the public from the National Center for Biotechnology Information's Gene Expression Omnibus repository. From this dataset, we predicted shared transcription factor regulation among cliques of genes sharing upstream genomic motifs. Finally, our predictions, and thus the model itself, was contrasted against experimentally confirmed gene co-regulation data. Our process tested the hypothesis that the incorporation of correlation networks can enhance the prediction of transcription factor co-regulation from gene expression and upstream sequence data. Ultimately, our experimental results did not show a larger portion of true positive results when compared to a randomized control. These initial results indicate that correlation networks may not be an appropriate outlet for detecting co-expression motifs. Work remains to see if correlation networks can be constructed and normalized in a way that brings them closer to representing true co-expression.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
EditorsIllhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1712-1715
Number of pages4
ISBN (Electronic)9781509030491
DOIs
StatePublished - Dec 15 2017
Event2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States
Duration: Nov 13 2017Nov 16 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume2017-January

Other

Other2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
CountryUnited States
CityKansas City
Period11/13/1711/16/17

Keywords

  • Bioinformatics
  • Biology
  • Co-expression
  • Coregulation
  • Correlation Networks
  • Gene Expression
  • Graph Theory
  • Neo4j
  • Non-Relational Databases
  • Transcriptomics

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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  • Cite this

    Huff, G. W., & Cooper, K. (2017). Correlation networks: Biologically driven relationships from gene expression data. In I. Yoo, J. H. Zheng, Y. Gong, X. T. Hu, C-R. Shyu, Y. Bromberg, J. Gao, & D. Korkin (Eds.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (pp. 1712-1715). (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217918