Analysis of structural measurements in correlation networks built from gene expression data across different tissue types in Mus musculus

Qianran Li, Kathryn Cooper

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

1 Scopus citations

Abstract

Big data analysis has been pervasively adopted as a method to analyze the tremendous amount of daily generated high throughput data in an efficient and accurate manner. Among the series of tools available in the field of big biomedical data, correlation networks are one of the most powerful tools for modelling gene expression, which is important in the study of disease and ageing. With the help of the correlation networks, insightful research has been done, such as distinguishing target genes for study within gene co-expression data. However, the utility of this model has not been thoroughly investigated as it pertains to applicability across and within tissue types. In this project, we address this gap in knowledge by investigating the range of outputs from analyzing correlation networks constructed from gene expression data. A total of 43 correlation networks were built using the gene expression data from 5 different tissues in Mus musculus. Then we compared a number of network measurements (degree distribution, assortativity coefficient, and clustering coefficient) across tissues to identify the span of possible ranges of each measure. We find that the average assortativity coefficient over all the networks is significantly different for networks between series, while the remainder of the parameters show no difference in average measure. Finally, we summarize the overall measurement ranges for number of nodes, number of edges, assortativity coefficient, clustering coefficient, and network density. This work is an investigation into the ability of the correlation network to represent gene expression data accurately, and the results that there are some common structural characteristics of data built across different tissues.

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.
Pages1716-1722
Number of pages7
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

  • correlation Networks
  • parameters
  • robustness

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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  • Cite this

    Li, Q., & Cooper, K. (2017). Analysis of structural measurements in correlation networks built from gene expression data across different tissue types in Mus musculus. 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. 1716-1722). (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.2017.8217919