Statistical and machine learning methods for spatially resolved transcriptomics data analysis
Posted on 2022-03-26 - 04:24
Abstract 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.
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Zeng, Zexian; Li, Yawei; Li, Yiming; Luo, Yuan (2022). Statistical and machine learning methods for spatially resolved transcriptomics data analysis. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5916255.v1
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summarize useful resourcesmachine learning methodsincreasing data complexityexperimental technologies continueenabled multiplexed profilingspatial transcriptomics technologyspatial transcriptomicsspatial locationsunderlying assumptionssequencing protocolsrecent advancementopportunities aheadherein reviewemerging needcontinuous evolutioncellular transcriptomesanalytical approaches