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) (PMCID:PMC9064087)
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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: | Otto, Professor Thomas and Wheadon, Professor Helen and Pancheva, Alex and Rogers, Dr Simon |
| 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 > School of Cancer Sciences College of Medical Veterinary and Life Sciences > School of Infection & Immunity College of Science and Engineering > School of Computing Science |
| Research Centre: | College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Immunobiology |
| 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 |
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