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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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
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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.
AbstractList 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.
ArticleNumber 5283
Author Fan, Jean
Atta, Lyla
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  fullname: Atta, Lyla
  organization: Department of Biomedical Engineering, Johns Hopkins University, Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Medical Scientist Training Program, Johns Hopkins University School of Medicine
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  orcidid: 0000-0002-0212-5451
  surname: Fan
  fullname: Fan, Jean
  email: jeanfan@jhu.edu
  organization: Department of Biomedical Engineering, Johns Hopkins University, Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Department of Computer Science, Johns Hopkins University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/34489425$$D View this record in MEDLINE/PubMed
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Snippet Spatially resolved transcriptomic data demand new computational analysis methods to derive biological insights. Here, we comment on these associated...
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StartPage 5283
SubjectTerms 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
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Title Computational challenges and opportunities in spatially resolved transcriptomic data analysis
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