Computational challenges and opportunities in spatially resolved transcriptomic data analysis
Lyla Atta and
Jean Fan ()
Additional contact information
Lyla Atta: Johns Hopkins University
Jean Fan: Johns Hopkins University
Nature Communications, 2021, vol. 12, issue 1, 1-5
Abstract:
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.
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
https://www.nature.com/articles/s41467-021-25557-9 Abstract (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25557-9
Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/
DOI: 10.1038/s41467-021-25557-9
Access Statistics for this article
Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie
More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().