Computational challenges and opportunities in spatially resolved transcriptomic data analysis

Lyla Atta; Jean Fan

doi:10.1038/s41467-021-25557-9

Computational challenges and opportunities in spatially resolved transcriptomic data analysis

Nat Commun. 2021 Sep 6;12(1):5283. doi: 10.1038/s41467-021-25557-9.

Authors

Lyla Atta^{1

2

3}, Jean Fan^{4

5

6}

Affiliations

¹ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
² Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
³ Medical Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁴ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. jeanfan@jhu.edu.
⁵ Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA. jeanfan@jhu.edu.
⁶ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. jeanfan@jhu.edu.

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Animals
Benchmarking
Cell Communication / genetics
Cells, Cultured
Gene Expression Profiling / statistics & numerical data*
Humans
Mathematical Computing*
Single-Cell Analysis / methods
Single-Cell Analysis / statistics & numerical data*
Transcriptome*

Grants and funding

T32 GM136577/GM/NIGMS NIH HHS/United States