Statistical and machine learning methods for spatially resolved transcriptomics data analysis

Zexian Zeng, Yawei Li, Yiming Li, Yuan Luo*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

20 Scopus citations


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.

Original languageEnglish (US)
Article number83
JournalGenome biology
Issue number1
StatePublished - Dec 2022

ASJC Scopus subject areas

  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Cell Biology


Dive into the research topics of 'Statistical and machine learning methods for spatially resolved transcriptomics data analysis'. Together they form a unique fingerprint.

Cite this