ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks
Abstract
BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate acrossmore »
- Authors:
-
- Stony Brook Univ., NY (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Univ. of Wisconsin, Madison, WI (United States)
- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (BNL), Upton, NY (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1597250
- Alternate Identifier(s):
- OSTI ID: 1591857
- Report Number(s):
- BNL-213599-2020-JAAM
Journal ID: ISSN 1471-2164
- Grant/Contract Number:
- SC0012704; AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- BMC Genomics
- Additional Journal Information:
- Journal Volume: 20; Journal Issue: S12; Conference: The International Conference on Intelligent Biology and Medicine (ICIBM) 2019, Columbus, OH (United States), 9-11 Jun 2019; Journal ID: ISSN 1471-2164
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Manifold learning; Manifold regularization; Clustering; Multiview learning; Functional genomics; Comparative network analysis; Comparative genomics; Biofuel
Citation Formats
Nguyen, Nam D., Blaby, Ian K., and Wang, Daifeng. ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks. United States: N. p., 2019.
Web. doi:10.1186/s12864-019-6329-2.
Nguyen, Nam D., Blaby, Ian K., & Wang, Daifeng. ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks. United States. https://doi.org/10.1186/s12864-019-6329-2
Nguyen, Nam D., Blaby, Ian K., and Wang, Daifeng. Mon .
"ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks". United States. https://doi.org/10.1186/s12864-019-6329-2. https://www.osti.gov/servlets/purl/1597250.
@article{osti_1597250,
title = {ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks},
author = {Nguyen, Nam D. and Blaby, Ian K. and Wang, Daifeng},
abstractNote = {BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster.},
doi = {10.1186/s12864-019-6329-2},
journal = {BMC Genomics},
number = S12,
volume = 20,
place = {United States},
year = {Mon Dec 30 00:00:00 EST 2019},
month = {Mon Dec 30 00:00:00 EST 2019}
}
Web of Science
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