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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Bibliographic Details
Published in:Nature communications Vol. 12; no. 1; pp. 5283 - 5
Main Authors: Atta, Lyla, Fan, Jean
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 06.09.2021
Nature Publishing Group
Nature Portfolio
Subjects:
14/32
45/91
631/114
631/61/212/2019
Animals
Benchmarking
Cell Communication - genetics
Cell cycle
Cells, Cultured
Comment
Computer applications
Data analysis
Demand analysis
Gene expression
Gene Expression Profiling - statistics & numerical data
Humanities and Social Sciences
Humans
Ligands
Mathematical Computing
Morphology
multidisciplinary
Open source software
Science
Science (multidisciplinary)
Single-Cell Analysis - methods
Single-Cell Analysis - statistics & numerical data
Transcriptome
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Description
Summary: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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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-021-25557-9