<i>Community</i>assesses differential cell communication using large multi-sample case-control scRNAseq datasets
Abstract Cell-cell communication is essential for physiological tissue function. In disease, this communication often gets disbalances by changes in the tissue cell type composition, fraction of cell engaged in communication and the rising or dropping expression levels of ligands, receptors and adhesion molecules. The changes in all these components of communication can be studied using single cell RNA-sequencing (scRNAseq) methods. With dropping sequencing costs, it is now possible to perform scRNAseq studies in larger cohorts of case and control samples to better address the heterogeneity of diseases. Here we presentcommunity, an R-based tool that is designed to perform differential communication analysis using scRNAseq between large cohorts of cases and controls.Communityis able to reconstruct communication between different cell types both in the case and the control cohort of a dataset, and subsequently analyze which communication channels are affected in disease.Communityis the first tool that integrates cell type abundance into the calculation of an interaction strength.Communityis also able to disentangle the mechanisms underlying these changes, as well as detect interactions that are kept compensated by a sender or a receiver despite the disbalanced signaling from the counterpart. We testedcommunityon two disease entities, ulcerative colitis and acute myeloid leukemia, using published scRNAseq datasets. We compared the performance of our tool to other differential communication pipelines, whichcommunityoutperformed in speed and robustness. Overall,communityis a fast, well-scalable, user-friendly R tool to assess differential cell-cell communication using large case-control scRNAseq datasets disentangling the driving mechanisms of communication shifts in disease..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 07. März Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Solovey, Maria [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2024.03.01.582941 |
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| PPN (Katalog-ID): |
XBI042711177 |
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| 245 | 1 | 0 | |a <i>Community</i>assesses differential cell communication using large multi-sample case-control scRNAseq datasets |
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| 520 | |a Abstract Cell-cell communication is essential for physiological tissue function. In disease, this communication often gets disbalances by changes in the tissue cell type composition, fraction of cell engaged in communication and the rising or dropping expression levels of ligands, receptors and adhesion molecules. The changes in all these components of communication can be studied using single cell RNA-sequencing (scRNAseq) methods. With dropping sequencing costs, it is now possible to perform scRNAseq studies in larger cohorts of case and control samples to better address the heterogeneity of diseases. Here we presentcommunity, an R-based tool that is designed to perform differential communication analysis using scRNAseq between large cohorts of cases and controls.Communityis able to reconstruct communication between different cell types both in the case and the control cohort of a dataset, and subsequently analyze which communication channels are affected in disease.Communityis the first tool that integrates cell type abundance into the calculation of an interaction strength.Communityis also able to disentangle the mechanisms underlying these changes, as well as detect interactions that are kept compensated by a sender or a receiver despite the disbalanced signaling from the counterpart. We testedcommunityon two disease entities, ulcerative colitis and acute myeloid leukemia, using published scRNAseq datasets. We compared the performance of our tool to other differential communication pipelines, whichcommunityoutperformed in speed and robustness. Overall,communityis a fast, well-scalable, user-friendly R tool to assess differential cell-cell communication using large case-control scRNAseq datasets disentangling the driving mechanisms of communication shifts in disease. | ||
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| 700 | 1 | |a Celik, Muhammet A. |4 aut | |
| 700 | 1 | |a Salcher, Felix R. |4 aut | |
| 700 | 1 | |a Abdalfattah, Mohmed |4 aut | |
| 700 | 1 | |a Ismail, Mostafa |0 (orcid)0009-0009-8914-2358 |4 aut | |
| 700 | 1 | |a Scialdone, Antonio |0 (orcid)0000-0002-4956-2843 |4 aut | |
| 700 | 1 | |a Ziemann, Frank |0 (orcid)0009-0004-8724-2076 |4 aut | |
| 700 | 1 | |a Colomé-Tatché, Maria |4 aut | |
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