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Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES

MOTIVATION: Recent years have seen the release of several toolsets that reveal cell–cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Nic...

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Autores principales: Raredon, Micha Sam Brickman, Yang, Junchen, Kothapalli, Neeharika, Lewis, Wesley, Kaminski, Naftali, Niklason, Laura E, Kluger, Yuval
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825783/
https://www.ncbi.nlm.nih.gov/pubmed/36458905
http://dx.doi.org/10.1093/bioinformatics/btac775
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author Raredon, Micha Sam Brickman
Yang, Junchen
Kothapalli, Neeharika
Lewis, Wesley
Kaminski, Naftali
Niklason, Laura E
Kluger, Yuval
author_facet Raredon, Micha Sam Brickman
Yang, Junchen
Kothapalli, Neeharika
Lewis, Wesley
Kaminski, Naftali
Niklason, Laura E
Kluger, Yuval
author_sort Raredon, Micha Sam Brickman
collection PubMed
description MOTIVATION: Recent years have seen the release of several toolsets that reveal cell–cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling), a tool to explore extracellular signaling at the truly single-cell level. RESULTS: NICHES allows embedding of ligand–receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand–receptor signaling mechanisms, is compatible with existing single-cell packages, and allows rapid, flexible analysis of cell–cell signaling at single-cell resolution. AVAILABILITY AND IMPLEMENTATION: NICHES is an open-source software implemented in R under academic free license v3.0 and it is available at http://github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-98257832023-01-10 Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES Raredon, Micha Sam Brickman Yang, Junchen Kothapalli, Neeharika Lewis, Wesley Kaminski, Naftali Niklason, Laura E Kluger, Yuval Bioinformatics Applications Note MOTIVATION: Recent years have seen the release of several toolsets that reveal cell–cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling), a tool to explore extracellular signaling at the truly single-cell level. RESULTS: NICHES allows embedding of ligand–receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand–receptor signaling mechanisms, is compatible with existing single-cell packages, and allows rapid, flexible analysis of cell–cell signaling at single-cell resolution. AVAILABILITY AND IMPLEMENTATION: NICHES is an open-source software implemented in R under academic free license v3.0 and it is available at http://github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-12-02 /pmc/articles/PMC9825783/ /pubmed/36458905 http://dx.doi.org/10.1093/bioinformatics/btac775 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Raredon, Micha Sam Brickman
Yang, Junchen
Kothapalli, Neeharika
Lewis, Wesley
Kaminski, Naftali
Niklason, Laura E
Kluger, Yuval
Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES
title Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES
title_full Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES
title_fullStr Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES
title_full_unstemmed Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES
title_short Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES
title_sort comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with niches
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825783/
https://www.ncbi.nlm.nih.gov/pubmed/36458905
http://dx.doi.org/10.1093/bioinformatics/btac775
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