Network inference with Granger causality ensembles on single-cell transcriptomics

Deshpande, Atul; Chu, Li-Fang; Stewart, Ron; Gitter, Anthony

doi:10.1016/j.celrep.2022.110333

Cited by 130Gene Regulatory Network AnalysisSingle-cell and spatial transcriptomicsBioinformatics and Genomic Networks

Network inference with Granger causality ensembles on single-cell transcriptomics

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Deshpande, Atul, Chu, Li-Fang, Stewart, Ron, Gitter, Anthony·2022·Cell Reports

DOI 10.1016/j.celrep.2022.110333PMC PMC9093087

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Abstract

Cellular gene expression changes throughout a dynamic biological process, such as differentiation. Pseudotimes estimate cells' progress along a dynamic process based on their individual gene expression states. Ordering the expression data by pseudotime provides information about the underlying regulator-gene interactions. Because the pseudotime distribution is not uniform, many standard mathematical methods are inapplicable for analyzing the ordered gene expression states. Here we present single-cell inference of networks using Granger ensembles (SINGE), an algorithm for gene regulatory network inference from ordered single-cell gene expression data. SINGE uses kernel-based Granger causality regression to smooth irregular pseudotimes and missing expression values. It aggregates predictions from an ensemble of regression analyses to compile a ranked list of candidate interactions between transcriptional regulators and target genes. In two mouse embryonic stem cell differentiation datasets, SINGE outperforms other contemporary algorithms. However, a more detailed examination reveals caveats about poor performance for individual regulators and uninformative pseudotimes.

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Pseudotime-based GRN inference approaches generally have inferior performance to those that rely solely on scRNA-seq data.
“Although there exist other GRN inference approaches based on pseudotime analysis,23,24,25 their performance is generally inferior to those that rely solely on the scRNA-seq data.13”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [25]

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Authors: Deshpande, A., Chu, L.-F., Stewart, R., & Gitter, A.
Affiliations: University of Wisconsin–Madison (US)
Morgridge Institute for Research (US)
Corresponding author: Gitter, Anthony
ORCID: Deshpande, Atul
Chu, Li-Fang
Stewart, Ron
Gitter, Anthony
Venue: Cell Reports
Publisher: Elsevier BV
Published: 2022-02
Volume: 38
Issue: 6
Pages: 110333
ISSN: 2211-1247 (ISSN-L)
DOI: 10.1016/j.celrep.2022.110333
PMC: PMC9093087
OpenAlex: W4210881488
Cited by: 130
References: 141
Citations by year: 2026: 12 · 2025: 24 · 2024: 24 · 2023: 36 · 2022: 14 · 2021: 10 · 2020: 9 · 2019: 1
FWCI: 8.08
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Cell Reports (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Science Foundation (CAREER, 1553206, 1148698)
U.S. Department of Energy
Wisconsin Alumni Research Foundation
National Institutes of Health (UH3TR000506, NIH P50, DE026787)
Office of Science
National Heart, Lung, and Blood Institute (U01HL099773)
National Cancer Institute (P50CA278595)
National Institute of Dental and Craniofacial Research (P50DE026787)
Division of Graduate Education
National Center for Advancing Translational Sciences
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: gene regulatory network inference, Granger causality, single-cell transcriptomics, pseudotime, kernel regression, ensemble regression, transcription factors, mouse embryonic stem cell differentiation
MeSH terms: Algorithms, Animals, Cell Differentiation, Software, Computational Biology, Gene Expression Profiling, Mice, Gene Regulatory Networks, Transcriptome

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