Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Inferring cell-specific gene regulatory networks from single cell gene expression data

View ORCID ProfileZiqi Zhang, Jongseok Han, Le Song, View ORCID ProfileXiuwei Zhang
doi: https://doi.org/10.1101/2022.03.03.482887
Ziqi Zhang
1Dept of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ziqi Zhang
Jongseok Han
1Dept of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Le Song
2Biomap
3MBZUAI
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xiuwei Zhang
1Dept of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Xiuwei Zhang
  • For correspondence: xiuwei.zhang@gatech.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Single cell gene expression datasets have been used to uncover differences between single cells, leading to discoveries of new cell types and cell identities, which are usually defined by the transcriptome profiles of cells. Biological networks, in particular, gene regulatory networks (GRNs), can be viewed as another feature of the cells, that contributes to the uniqueness of each single cell. However, methods that reconstruct cell-specific GRNs are still missing.

We propose CeSpGRN (Cell Specific GRN), which infers cell-specific GRNs from single cell gene expression data. CeSpGRN uses a Gaussian weighted kernel which allows the GRN of a given cell to be learned from the gene expression profile of this cell and cells that are upstream and downstream of this cell in the developmental process. CeSpGRN can be applied to infer cell-specific GRNs in cell populations of any trajectory or cluster structure, and it does not require time information of cells as additional input. We compared the performance of CeSpGRN and baseline methods on simulated datasets obtained under various settings. CeSpGRN showed superior performance in reconstructing the GRN for each cell, as well as in detecting the regulatory interactions that differ between cells. We also applied CeSpGRN to real datasets including THP-1 human myeloid monocytic leukemia cells and mouse embryonic stem cells, where CeSpGRN suggested a set of interactions between genes that rewire during the differentiation process in these cells. CeSpGRN is available at https://github.com/PeterZZQ/CeSpGRN.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Changing author name

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted March 04, 2022.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Inferring cell-specific gene regulatory networks from single cell gene expression data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Inferring cell-specific gene regulatory networks from single cell gene expression data
Ziqi Zhang, Jongseok Han, Le Song, Xiuwei Zhang
bioRxiv 2022.03.03.482887; doi: https://doi.org/10.1101/2022.03.03.482887
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Inferring cell-specific gene regulatory networks from single cell gene expression data
Ziqi Zhang, Jongseok Han, Le Song, Xiuwei Zhang
bioRxiv 2022.03.03.482887; doi: https://doi.org/10.1101/2022.03.03.482887

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4234)
  • Biochemistry (9128)
  • Bioengineering (6774)
  • Bioinformatics (23989)
  • Biophysics (12117)
  • Cancer Biology (9523)
  • Cell Biology (13773)
  • Clinical Trials (138)
  • Developmental Biology (7627)
  • Ecology (11686)
  • Epidemiology (2066)
  • Evolutionary Biology (15506)
  • Genetics (10638)
  • Genomics (14322)
  • Immunology (9479)
  • Microbiology (22832)
  • Molecular Biology (9089)
  • Neuroscience (48987)
  • Paleontology (355)
  • Pathology (1480)
  • Pharmacology and Toxicology (2568)
  • Physiology (3844)
  • Plant Biology (8327)
  • Scientific Communication and Education (1471)
  • Synthetic Biology (2296)
  • Systems Biology (6187)
  • Zoology (1300)