Prior knowledge driven Granger causality analysis on gene regulatory network discovery
Abstract
Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, the propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods. In our research, we noticed a “ 1+1>2” effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be relatedmore »
- Authors:
-
- Stony Brook Univ., NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1259268
- Resource Type:
- Accepted Manuscript
- Journal Name:
- BMC Bioinformatics
- Additional Journal Information:
- Journal Volume: 16; Journal Issue: 1; Journal ID: ISSN 1471-2105
- Publisher:
- BioMed Central
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES; Time series; Gene expression data; Granger causality; Gene regulatory networks
Citation Formats
Yao, Shun, Yoo, Shinjae, and Yu, Dantong. Prior knowledge driven Granger causality analysis on gene regulatory network discovery. United States: N. p., 2015.
Web. doi:10.1186/s12859-015-0710-1.
Yao, Shun, Yoo, Shinjae, & Yu, Dantong. Prior knowledge driven Granger causality analysis on gene regulatory network discovery. United States. https://doi.org/10.1186/s12859-015-0710-1
Yao, Shun, Yoo, Shinjae, and Yu, Dantong. Fri .
"Prior knowledge driven Granger causality analysis on gene regulatory network discovery". United States. https://doi.org/10.1186/s12859-015-0710-1. https://www.osti.gov/servlets/purl/1259268.
@article{osti_1259268,
title = {Prior knowledge driven Granger causality analysis on gene regulatory network discovery},
author = {Yao, Shun and Yoo, Shinjae and Yu, Dantong},
abstractNote = {Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, the propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods. In our research, we noticed a “ 1+1>2” effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be related to the yeast’s responses to different levels of glucose. In conclusion, our research improves causality modeling by combining heterogeneous knowledge, which is well aligned with the future direction in system biology. Furthermore, we proposed a method of Monte Carlo significance estimation (MCSE) to calculate the edge significances which provide statistical meanings to the discovered causality networks. All of our data and source codes will be available under the link https://bitbucket.org/dtyu/granger-causality/wiki/Home.},
doi = {10.1186/s12859-015-0710-1},
journal = {BMC Bioinformatics},
number = 1,
volume = 16,
place = {United States},
year = {Fri Aug 28 00:00:00 EDT 2015},
month = {Fri Aug 28 00:00:00 EDT 2015}
}
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