SpatialDWLS: accurate deconvolution of spatial transcriptomic data

Recent development of spatial transcriptomic technologies has made it possible to characterize cellular heterogeneity with spatial information. However, the technology often does not have sufficient resolution to distinguish neighboring cell types. Here, we present spatialDWLS, to quantitatively est...

Descripción completa

Detalles Bibliográficos
Autores principales: Dong, Rui, Yuan, Guo-Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8108367/
https://www.ncbi.nlm.nih.gov/pubmed/33971932
http://dx.doi.org/10.1186/s13059-021-02362-7
_version_ 1783690116659675136
author Dong, Rui
Yuan, Guo-Cheng
author_facet Dong, Rui
Yuan, Guo-Cheng
author_sort Dong, Rui
collection PubMed
description Recent development of spatial transcriptomic technologies has made it possible to characterize cellular heterogeneity with spatial information. However, the technology often does not have sufficient resolution to distinguish neighboring cell types. Here, we present spatialDWLS, to quantitatively estimate the cell-type composition at each spatial location. We benchmark the performance of spatialDWLS by comparing it with a number of existing deconvolution methods and find that spatialDWLS outperforms the other methods in terms of accuracy and speed. By applying spatialDWLS to a human developmental heart dataset, we observe striking spatial temporal changes of cell-type composition during development. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02362-7.
format Online
Article
Text
id pubmed-8108367
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-81083672021-05-11 SpatialDWLS: accurate deconvolution of spatial transcriptomic data Dong, Rui Yuan, Guo-Cheng Genome Biol Method Recent development of spatial transcriptomic technologies has made it possible to characterize cellular heterogeneity with spatial information. However, the technology often does not have sufficient resolution to distinguish neighboring cell types. Here, we present spatialDWLS, to quantitatively estimate the cell-type composition at each spatial location. We benchmark the performance of spatialDWLS by comparing it with a number of existing deconvolution methods and find that spatialDWLS outperforms the other methods in terms of accuracy and speed. By applying spatialDWLS to a human developmental heart dataset, we observe striking spatial temporal changes of cell-type composition during development. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02362-7. BioMed Central 2021-05-10 /pmc/articles/PMC8108367/ /pubmed/33971932 http://dx.doi.org/10.1186/s13059-021-02362-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Dong, Rui
Yuan, Guo-Cheng
SpatialDWLS: accurate deconvolution of spatial transcriptomic data
title SpatialDWLS: accurate deconvolution of spatial transcriptomic data
title_full SpatialDWLS: accurate deconvolution of spatial transcriptomic data
title_fullStr SpatialDWLS: accurate deconvolution of spatial transcriptomic data
title_full_unstemmed SpatialDWLS: accurate deconvolution of spatial transcriptomic data
title_short SpatialDWLS: accurate deconvolution of spatial transcriptomic data
title_sort spatialdwls: accurate deconvolution of spatial transcriptomic data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8108367/
https://www.ncbi.nlm.nih.gov/pubmed/33971932
http://dx.doi.org/10.1186/s13059-021-02362-7
work_keys_str_mv AT dongrui spatialdwlsaccuratedeconvolutionofspatialtranscriptomicdata
AT yuanguocheng spatialdwlsaccuratedeconvolutionofspatialtranscriptomicdata

Ejemplares similares