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Inferring spatial and signaling relationships between cells from single cell transcriptomic data
Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however, crucial spatial information is often lost. We present SpaOTsc, a method relying on structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively sma...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190659/ https://www.ncbi.nlm.nih.gov/pubmed/32350282 http://dx.doi.org/10.1038/s41467-020-15968-5 |
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author | Cang, Zixuan Nie, Qing |
author_facet | Cang, Zixuan Nie, Qing |
author_sort | Cang, Zixuan |
collection | PubMed |
description | Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however, crucial spatial information is often lost. We present SpaOTsc, a method relying on structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively small number of genes. A spatial metric for individual cells in scRNA-seq data is first established based on a map connecting it with the spatial measurements. The cell–cell communications are then obtained by “optimally transporting” signal senders to target signal receivers in space. Using partial information decomposition, we next compute the intercellular gene–gene information flow to estimate the spatial regulations between genes across cells. Four datasets are employed for cross-validation of spatial gene expression prediction and comparison to known cell–cell communications. SpaOTsc has broader applications, both in integrating non-spatial single-cell measurements with spatial data, and directly in spatial single-cell transcriptomics data to reconstruct spatial cellular dynamics in tissues. |
format | Online Article Text |
id | pubmed-7190659 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-71906592020-05-01 Inferring spatial and signaling relationships between cells from single cell transcriptomic data Cang, Zixuan Nie, Qing Nat Commun Article Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however, crucial spatial information is often lost. We present SpaOTsc, a method relying on structured optimal transport to recover spatial properties of scRNA-seq data by utilizing spatial measurements of a relatively small number of genes. A spatial metric for individual cells in scRNA-seq data is first established based on a map connecting it with the spatial measurements. The cell–cell communications are then obtained by “optimally transporting” signal senders to target signal receivers in space. Using partial information decomposition, we next compute the intercellular gene–gene information flow to estimate the spatial regulations between genes across cells. Four datasets are employed for cross-validation of spatial gene expression prediction and comparison to known cell–cell communications. SpaOTsc has broader applications, both in integrating non-spatial single-cell measurements with spatial data, and directly in spatial single-cell transcriptomics data to reconstruct spatial cellular dynamics in tissues. Nature Publishing Group UK 2020-04-29 /pmc/articles/PMC7190659/ /pubmed/32350282 http://dx.doi.org/10.1038/s41467-020-15968-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Cang, Zixuan Nie, Qing Inferring spatial and signaling relationships between cells from single cell transcriptomic data |
title | Inferring spatial and signaling relationships between cells from single cell transcriptomic data |
title_full | Inferring spatial and signaling relationships between cells from single cell transcriptomic data |
title_fullStr | Inferring spatial and signaling relationships between cells from single cell transcriptomic data |
title_full_unstemmed | Inferring spatial and signaling relationships between cells from single cell transcriptomic data |
title_short | Inferring spatial and signaling relationships between cells from single cell transcriptomic data |
title_sort | inferring spatial and signaling relationships between cells from single cell transcriptomic data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190659/ https://www.ncbi.nlm.nih.gov/pubmed/32350282 http://dx.doi.org/10.1038/s41467-020-15968-5 |
work_keys_str_mv | AT cangzixuan inferringspatialandsignalingrelationshipsbetweencellsfromsinglecelltranscriptomicdata AT nieqing inferringspatialandsignalingrelationshipsbetweencellsfromsinglecelltranscriptomicdata |