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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Detalles Bibliográficos
Autores principales: Atta, Lyla, Fan, Jean
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Comment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421472/
https://www.ncbi.nlm.nih.gov/pubmed/34489425
http://dx.doi.org/10.1038/s41467-021-25557-9
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author Atta, Lyla
Fan, Jean
author_facet Atta, Lyla
Fan, Jean
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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.
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spelling pubmed-84214722021-09-22 Computational challenges and opportunities in spatially resolved transcriptomic data analysis Atta, Lyla Fan, Jean Nat Commun Comment 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. Nature Publishing Group UK 2021-09-06 /pmc/articles/PMC8421472/ /pubmed/34489425 http://dx.doi.org/10.1038/s41467-021-25557-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Comment
Atta, Lyla
Fan, Jean
Computational challenges and opportunities in spatially resolved transcriptomic data analysis
title 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_short Computational challenges and opportunities in spatially resolved transcriptomic data analysis
title_sort computational challenges and opportunities in spatially resolved transcriptomic data analysis
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421472/
https://www.ncbi.nlm.nih.gov/pubmed/34489425
http://dx.doi.org/10.1038/s41467-021-25557-9
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