On the comparison of state-and transition-based analysis of biological relevance in gene co-expression networks

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

Abstract

Traditional correlation network analysis typically involves creating a network using gene expression data and then identifying biologically relevant clusters from that network by enrichment with Gene Ontology or pathway information. When one wants to examine these networks in a dynamic way-such as between controls versus treatment or over time-a "snapshot" approach is taken by comparing network structures at each time point. The biological relevance of these structures are then reported and compared. In this research, we examine the same "snapshot" networks but focus on the enrichment of changes in structure to determine if these results give any more insight into the mechanisms behind observed phenotypes. Our main hypothesis is that more information, particularly related to potential dynamic changes, can be obtained through transition-based analysis of biological networks. To test this hypothesis, we compare gene expression data from the mouse hippocampus at three different time points: young, middle-aged, and aged, and compare the traditional state-based approach to the dynamic transition-based enrichment approach. In this study we use a clustering approach (SPICi) designed specifically for clustering of large biological networks. The results of this study verify an inconsistency between traditional and dynamic structure identification approaches through biological enrichment. These results highlight an intriguing issue for those performing, critiquing, and using network based approaches in their research-that a black box or workflow type of approach typically used in network based research can be Supplemented with a transitionbased approach to support movement from in silico to in vivo experimentation of target genes.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Editorslng. Matthieu Schapranow, Jiayu Zhou, Xiaohua Tony Hu, Bin Ma, Sanguthevar Rajasekaran, Satoru Miyano, Illhoi Yoo, Brian Pierce, Amarda Shehu, Vijay K. Gombar, Brian Chen, Vinay Pai, Jun Huan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1197-1203
Number of pages7
ISBN (Electronic)9781467367981
DOIs
StatePublished - Dec 16 2015
EventIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States
Duration: Nov 9 2015Nov 12 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Other

OtherIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
CountryUnited States
CityWashington
Period11/9/1511/12/15

Fingerprint

Genes
Gene Expression
Cluster Analysis
Research
Gene expression
Gene Ontology
Workflow
Computer Simulation
Hippocampus
Electric network analysis
Phenotype
Ontology

Keywords

  • gene expression
  • network analysis
  • state-based
  • transition-based

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Health Informatics
  • Biomedical Engineering

Cite this

Cooper, K. M., Vemuri, P., & Ali, H. H. (2015). On the comparison of state-and transition-based analysis of biological relevance in gene co-expression networks. In L. M. Schapranow, J. Zhou, X. T. Hu, B. Ma, S. Rajasekaran, S. Miyano, I. Yoo, B. Pierce, A. Shehu, V. K. Gombar, B. Chen, V. Pai, ... J. Huan (Eds.), Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 (pp. 1197-1203). [7359852] (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2015.7359852

On the comparison of state-and transition-based analysis of biological relevance in gene co-expression networks. / Cooper, Kathryn M; Vemuri, Prasuna; Ali, Hesham H.

Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. ed. / lng. Matthieu Schapranow; Jiayu Zhou; Xiaohua Tony Hu; Bin Ma; Sanguthevar Rajasekaran; Satoru Miyano; Illhoi Yoo; Brian Pierce; Amarda Shehu; Vijay K. Gombar; Brian Chen; Vinay Pai; Jun Huan. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1197-1203 7359852 (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015).

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

Cooper, KM, Vemuri, P & Ali, HH 2015, On the comparison of state-and transition-based analysis of biological relevance in gene co-expression networks. in LM Schapranow, J Zhou, XT Hu, B Ma, S Rajasekaran, S Miyano, I Yoo, B Pierce, A Shehu, VK Gombar, B Chen, V Pai & J Huan (eds), Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015., 7359852, Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1197-1203, IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, Washington, United States, 11/9/15. https://doi.org/10.1109/BIBM.2015.7359852
Cooper KM, Vemuri P, Ali HH. On the comparison of state-and transition-based analysis of biological relevance in gene co-expression networks. In Schapranow LM, Zhou J, Hu XT, Ma B, Rajasekaran S, Miyano S, Yoo I, Pierce B, Shehu A, Gombar VK, Chen B, Pai V, Huan J, editors, Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 1197-1203. 7359852. (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015). https://doi.org/10.1109/BIBM.2015.7359852
Cooper, Kathryn M ; Vemuri, Prasuna ; Ali, Hesham H. / On the comparison of state-and transition-based analysis of biological relevance in gene co-expression networks. Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. editor / lng. Matthieu Schapranow ; Jiayu Zhou ; Xiaohua Tony Hu ; Bin Ma ; Sanguthevar Rajasekaran ; Satoru Miyano ; Illhoi Yoo ; Brian Pierce ; Amarda Shehu ; Vijay K. Gombar ; Brian Chen ; Vinay Pai ; Jun Huan. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 1197-1203 (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015).
@inproceedings{c9a5567688414a489dc29234b3cbe837,
title = "On the comparison of state-and transition-based analysis of biological relevance in gene co-expression networks",
abstract = "Traditional correlation network analysis typically involves creating a network using gene expression data and then identifying biologically relevant clusters from that network by enrichment with Gene Ontology or pathway information. When one wants to examine these networks in a dynamic way-such as between controls versus treatment or over time-a {"}snapshot{"} approach is taken by comparing network structures at each time point. The biological relevance of these structures are then reported and compared. In this research, we examine the same {"}snapshot{"} networks but focus on the enrichment of changes in structure to determine if these results give any more insight into the mechanisms behind observed phenotypes. Our main hypothesis is that more information, particularly related to potential dynamic changes, can be obtained through transition-based analysis of biological networks. To test this hypothesis, we compare gene expression data from the mouse hippocampus at three different time points: young, middle-aged, and aged, and compare the traditional state-based approach to the dynamic transition-based enrichment approach. In this study we use a clustering approach (SPICi) designed specifically for clustering of large biological networks. The results of this study verify an inconsistency between traditional and dynamic structure identification approaches through biological enrichment. These results highlight an intriguing issue for those performing, critiquing, and using network based approaches in their research-that a black box or workflow type of approach typically used in network based research can be Supplemented with a transitionbased approach to support movement from in silico to in vivo experimentation of target genes.",
keywords = "gene expression, network analysis, state-based, transition-based",
author = "Cooper, {Kathryn M} and Prasuna Vemuri and Ali, {Hesham H}",
year = "2015",
month = "12",
day = "16",
doi = "10.1109/BIBM.2015.7359852",
language = "English (US)",
series = "Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1197--1203",
editor = "Schapranow, {lng. Matthieu} and Jiayu Zhou and Hu, {Xiaohua Tony} and Bin Ma and Sanguthevar Rajasekaran and Satoru Miyano and Illhoi Yoo and Brian Pierce and Amarda Shehu and Gombar, {Vijay K.} and Brian Chen and Vinay Pai and Jun Huan",
booktitle = "Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015",

}

TY - GEN

T1 - On the comparison of state-and transition-based analysis of biological relevance in gene co-expression networks

AU - Cooper, Kathryn M

AU - Vemuri, Prasuna

AU - Ali, Hesham H

PY - 2015/12/16

Y1 - 2015/12/16

N2 - Traditional correlation network analysis typically involves creating a network using gene expression data and then identifying biologically relevant clusters from that network by enrichment with Gene Ontology or pathway information. When one wants to examine these networks in a dynamic way-such as between controls versus treatment or over time-a "snapshot" approach is taken by comparing network structures at each time point. The biological relevance of these structures are then reported and compared. In this research, we examine the same "snapshot" networks but focus on the enrichment of changes in structure to determine if these results give any more insight into the mechanisms behind observed phenotypes. Our main hypothesis is that more information, particularly related to potential dynamic changes, can be obtained through transition-based analysis of biological networks. To test this hypothesis, we compare gene expression data from the mouse hippocampus at three different time points: young, middle-aged, and aged, and compare the traditional state-based approach to the dynamic transition-based enrichment approach. In this study we use a clustering approach (SPICi) designed specifically for clustering of large biological networks. The results of this study verify an inconsistency between traditional and dynamic structure identification approaches through biological enrichment. These results highlight an intriguing issue for those performing, critiquing, and using network based approaches in their research-that a black box or workflow type of approach typically used in network based research can be Supplemented with a transitionbased approach to support movement from in silico to in vivo experimentation of target genes.

AB - Traditional correlation network analysis typically involves creating a network using gene expression data and then identifying biologically relevant clusters from that network by enrichment with Gene Ontology or pathway information. When one wants to examine these networks in a dynamic way-such as between controls versus treatment or over time-a "snapshot" approach is taken by comparing network structures at each time point. The biological relevance of these structures are then reported and compared. In this research, we examine the same "snapshot" networks but focus on the enrichment of changes in structure to determine if these results give any more insight into the mechanisms behind observed phenotypes. Our main hypothesis is that more information, particularly related to potential dynamic changes, can be obtained through transition-based analysis of biological networks. To test this hypothesis, we compare gene expression data from the mouse hippocampus at three different time points: young, middle-aged, and aged, and compare the traditional state-based approach to the dynamic transition-based enrichment approach. In this study we use a clustering approach (SPICi) designed specifically for clustering of large biological networks. The results of this study verify an inconsistency between traditional and dynamic structure identification approaches through biological enrichment. These results highlight an intriguing issue for those performing, critiquing, and using network based approaches in their research-that a black box or workflow type of approach typically used in network based research can be Supplemented with a transitionbased approach to support movement from in silico to in vivo experimentation of target genes.

KW - gene expression

KW - network analysis

KW - state-based

KW - transition-based

UR - http://www.scopus.com/inward/record.url?scp=84962459551&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84962459551&partnerID=8YFLogxK

U2 - 10.1109/BIBM.2015.7359852

DO - 10.1109/BIBM.2015.7359852

M3 - Conference contribution

AN - SCOPUS:84962459551

T3 - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

SP - 1197

EP - 1203

BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

A2 - Schapranow, lng. Matthieu

A2 - Zhou, Jiayu

A2 - Hu, Xiaohua Tony

A2 - Ma, Bin

A2 - Rajasekaran, Sanguthevar

A2 - Miyano, Satoru

A2 - Yoo, Illhoi

A2 - Pierce, Brian

A2 - Shehu, Amarda

A2 - Gombar, Vijay K.

A2 - Chen, Brian

A2 - Pai, Vinay

A2 - Huan, Jun

PB - Institute of Electrical and Electronics Engineers Inc.

ER -