Statistical and machine learning methods for spatially resolved transcriptomics data analysis

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...

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Bibliographic Details
Published inGenome Biology Vol. 23; no. 1; p. 83
Main Authors Zeng, Zexian, Li, Yawei, Li, Yiming, Luo, Yuan
Format Journal Article
LanguageEnglish
Published England BioMed Central 25.03.2022
BMC
Subjects
Clustering
Data Analysis
Decomposition
evolution
Gene expression
Generalized linear models
genome
Learning algorithms
Machine Learning
Methods
Review
Sequence Analysis
Single-Cell Analysis - methods
Statistics
Transcriptome
Transcriptomes
Transcriptomics
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Summary: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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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-022-02653-7