Using topic modeling to detect cellular crosstalk in scRNA-seq

Pancheva, A., Wheadon, H. , Rogers, S. and Otto, T. D. (2022) Using topic modeling to detect cellular crosstalk in scRNA-seq. PLoS Computational Biology, 18(4), e1009975. (doi: 10.1371/journal.pcbi.1009975) (PMID:35395014)

[img] Text
269288.pdf - Published Version
Available under License Creative Commons Attribution.

3MB

Abstract

Cell-cell interactions are vital for numerous biological processes including development, differentiation, and response to inflammation. Currently, most methods for studying interactions on scRNA-seq level are based on curated databases of ligands and receptors. While those methods are useful, they are limited to our current biological knowledge. Recent advances in single cell protocols have allowed for physically interacting cells to be captured, and as such we have the potential to study interactions in a complimentary way without relying on prior knowledge. We introduce a new method based on Latent Dirichlet Allocation (LDA) for detecting genes that change as a result of interaction. We apply our method to synthetic datasets to demonstrate its ability to detect genes that change in an interacting population compared to a reference population. Next, we apply our approach to two datasets of physically interacting cells to identify the genes that change as a result of interaction, examples include adhesion and co-stimulatory molecules which confirm physical interaction between cells. For each dataset we produce a ranking of genes that are changing in subpopulations of the interacting cells. In addition to the genes discussed in the original publications, we highlight further candidates for interaction in the top 100 and 300 ranked genes. Lastly, we apply our method to a dataset generated by a standard droplet-based protocol not designed to capture interacting cells, and discuss its suitability for analysing interactions. We present a method that streamlines detection of interactions and does not require prior clustering and generation of synthetic reference profiles to detect changes in expression.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Pancheva, Alex and Rogers, Dr Simon and Wheadon, Professor Helen and Otto, Dr Thomas
Creator Roles:
Pancheva, A.Conceptualization, Formal analysis, Visualization, Writing – original draft, Writing – review and editing
Wheadon, H.Supervision, Writing – review and editing
Rogers, S.Conceptualization, Supervision, Writing – review and editing
Otto, T. D.Conceptualization, Supervision, Writing – review and editing
Authors: Pancheva, A., Wheadon, H., Rogers, S., and Otto, T. D.
College/School:College of Medical Veterinary and Life Sciences > Institute of Cancer Sciences
College of Medical Veterinary and Life Sciences > Institute of Infection Immunity and Inflammation
College of Science and Engineering > School of Computing Science
Journal Name:PLoS Computational Biology
Publisher:Public Library of Science
ISSN:1553-734X
ISSN (Online):1553-7358
Published Online:08 April 2022
Copyright Holders:Copyright © 2022 Pancheva et al.
First Published:First published in PLoS Computational Biology 18(4): e1009975
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
304005MRC Precision Medicine Training GrantMorven BarlassMedical Research Council (MRC)MR/N013166/1CAMS - Cardiovascular Science
170547The Wellcome Centre for Molecular Parasitology ( Core Support )Andrew WatersWellcome Trust (WELLCOTR)104111/Z/14/ZIII - Parasitology