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Statistical and machine learning methods for spatially resolved transcriptomics data analysis
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DOI：10.1186/s13059-022-02653-7.png)
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Abstract
En 中文
The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and spatial locations. As the capacity and efficiency of the experimental technologies continue to improve, there is an emerging need for the development of analytical approaches. Furthermore, with the continuous evolution of sequencing protocols, the underlying assumptions of current analytical methods need to be re-evaluated and adjusted to harness the increasing data complexity. To motivate and aid future model development, we herein review the recent development of statistical and machine learning methods in spatial transcriptomics, summarize useful resources, and highlight the challenges and opportunities ahead.
Keywords:
GENE-EXPRESSION
CELL
RNA
TISSUE
REVEALS
SEQ
IDENTIFICATION
CHROMATIN
ATLAS
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