Computational challenges and opportunities in spatially resolved transcriptomic data analysis

被引：0
|
作者：
Lyla Atta
Jean Fan
机构：
[1] Johns Hopkins University,Department of Biomedical Engineering
[2] Johns Hopkins University,Center for Computational Biology, Whiting School of Engineering
[3] Johns Hopkins University School of Medicine,Medical Scientist Training Program
[4] Johns Hopkins University,Department of Computer Science
来源：
Nature Communications | / 12卷
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摘要：
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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