Network modeling of complex data sets

Piet Jones; Deborah Weighill; Manesh Shah; Sharlee Climer; Jeremy Schmutz; Avinash Sreedasyam; Gerald Tuskan; Daniel Jacobson

doi:10.1007/978-1-0716-0195-2_15

Network modeling of complex data sets

Piet Jones, Deborah Weighill, Manesh Shah, Sharlee Climer, Jeremy Schmutz, Avinash Sreedasyam, Gerald Tuskan, Daniel Jacobson

Computational and Predictive Biology

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

4 Scopus citations

Abstract

We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.

Original language	English
Title of host publication	Methods in Molecular Biology
Publisher	Humana Press Inc.
Pages	197-215
Number of pages	19
DOIs	https://doi.org/10.1007/978-1-0716-0195-2_15
State	Published - 2020

Publication series

Name	Methods in Molecular Biology
Volume	2096
ISSN (Print)	1064-3745
ISSN (Electronic)	1940-6029

Funding

We would like to acknowledge the Joint Genome Institute (JGI) for the sequencing of the Populus trichocarpa transcriptomes. The work conducted by the U.S. Department of Energy Joint Genome Institute is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Funding was provided by The Center for Bioenergy Innovation (CBI), U.S. Department of Energy Bioenergy Research Centers supported by the Office of Biological and Environmental Research in the DOE Office of Science. This research was also supported by the Plant-Microbe Interfaces Scientific Focus Area (http://pmi. ornl.gov) in the Genomic Science Program, the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science, and by the Department of Energy, Laboratory Directed Research and Development funding (7758), at the Oak Ridge National Laboratory. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the US DOE under contract DE-AC05-00OR22725. This research used resources of the Oak Ridge Leadership Computing Facility (OLCF) and the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/ doe-public-access-plan). The work conducted by the U.S. Department of Energy Joint Genome Institute is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The authors Piet Jones and Deborah Weighill contributed equally to this work.

Funders	Funder number
Compute and Data Environment for Science
DOE Office of Science
Joint Genome Institute
Office of Biological and Environmental Research
Plant-Microbe Interfaces Scientific Focus Area
U.S. Department of Energy Office of Science
U.S. Department of Energy	DE-AC05-00OR22725
National Institute on Aging	P50AG005681
Office of Science	DE-AC02-05CH11231
Biological and Environmental Research
Oak Ridge National Laboratory
Laboratory Directed Research and Development	7758
Center for Bioenergy Innovation

Keywords

Differential analysis
Enrichment
FCROS
Fisher exact test
Machine learning
Random forests
Similarity network

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10.1007/978-1-0716-0195-2_15

Cite this

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AU - Tuskan, Gerald

AU - Jacobson, Daniel

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Network modeling of complex data sets

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