Computational challenges and opportunities in spatially resolved transcriptomic data analysis

Computational challenges and opportunities in spatially resolved transcriptomic data analysis

Spatially resolved transcriptomic data demand new computational analysis methods to derive biological insights. Here, we comment on these associated computational challenges as well as highlight the opportunities for standardized benchmarking metrics and data-sharing infrastructure in spurring innov...

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Main Authors: Lyla Atta, Jean Fan
Format: Article
Language:English
Published: Nature Publishing Group 2021-09-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-021-25557-9
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spelling doaj-30b39018bedf4a9f9b290b200f3c98572021-09-12T11:46:45ZengNature Publishing GroupNature Communications2041-17232021-09-011211510.1038/s41467-021-25557-9Computational challenges and opportunities in spatially resolved transcriptomic data analysisLyla Atta0Jean Fan1Department of Biomedical Engineering, Johns Hopkins UniversityDepartment of Biomedical Engineering, Johns Hopkins UniversitySpatially resolved transcriptomic data demand new computational analysis methods to derive biological insights. Here, we comment on these associated computational challenges as well as highlight the opportunities for standardized benchmarking metrics and data-sharing infrastructure in spurring innovation moving forward.https://doi.org/10.1038/s41467-021-25557-9
collection DOAJ
language English
format Article
sources DOAJ
author Lyla Atta
Jean Fan
spellingShingle Lyla Atta
Jean Fan
Computational challenges and opportunities in spatially resolved transcriptomic data analysis
Nature Communications
author_facet Lyla Atta
Jean Fan
author_sort Lyla Atta
title Computational challenges and opportunities in spatially resolved transcriptomic data analysis
title_short Computational challenges and opportunities in spatially resolved transcriptomic data analysis
title_full Computational challenges and opportunities in spatially resolved transcriptomic data analysis
title_fullStr Computational challenges and opportunities in spatially resolved transcriptomic data analysis
title_full_unstemmed Computational challenges and opportunities in spatially resolved transcriptomic data analysis
title_sort computational challenges and opportunities in spatially resolved transcriptomic data analysis
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2021-09-01
description Spatially resolved transcriptomic data demand new computational analysis methods to derive biological insights. Here, we comment on these associated computational challenges as well as highlight the opportunities for standardized benchmarking metrics and data-sharing infrastructure in spurring innovation moving forward.
url https://doi.org/10.1038/s41467-021-25557-9
work_keys_str_mv AT lylaatta computationalchallengesandopportunitiesinspatiallyresolvedtranscriptomicdataanalysis
AT jeanfan computationalchallengesandopportunitiesinspatiallyresolvedtranscriptomicdataanalysis
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