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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zeng, Zexian, Li, Yawei, Li, Yiming, Luo, Yuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8951701/
https://www.ncbi.nlm.nih.gov/pubmed/35337374
http://dx.doi.org/10.1186/s13059-022-02653-7
_version_ 1784675451712569344
author Zeng, Zexian
Li, Yawei
Li, Yiming
Luo, Yuan
author_facet Zeng, Zexian
Li, Yawei
Li, Yiming
Luo, Yuan
author_sort Zeng, Zexian
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02653-7.
format Online
Article
Text
id pubmed-8951701
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-89517012022-03-26 Statistical and machine learning methods for spatially resolved transcriptomics data analysis Zeng, Zexian Li, Yawei Li, Yiming Luo, Yuan Genome Biol Review 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02653-7. BioMed Central 2022-03-25 /pmc/articles/PMC8951701/ /pubmed/35337374 http://dx.doi.org/10.1186/s13059-022-02653-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Review
Zeng, Zexian
Li, Yawei
Li, Yiming
Luo, Yuan
Statistical and machine learning methods for spatially resolved transcriptomics data analysis
title Statistical and machine learning methods for spatially resolved transcriptomics data analysis
title_full Statistical and machine learning methods for spatially resolved transcriptomics data analysis
title_fullStr Statistical and machine learning methods for spatially resolved transcriptomics data analysis
title_full_unstemmed Statistical and machine learning methods for spatially resolved transcriptomics data analysis
title_short Statistical and machine learning methods for spatially resolved transcriptomics data analysis
title_sort statistical and machine learning methods for spatially resolved transcriptomics data analysis
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8951701/
https://www.ncbi.nlm.nih.gov/pubmed/35337374
http://dx.doi.org/10.1186/s13059-022-02653-7
work_keys_str_mv AT zengzexian statisticalandmachinelearningmethodsforspatiallyresolvedtranscriptomicsdataanalysis
AT liyawei statisticalandmachinelearningmethodsforspatiallyresolvedtranscriptomicsdataanalysis
AT liyiming statisticalandmachinelearningmethodsforspatiallyresolvedtranscriptomicsdataanalysis
AT luoyuan statisticalandmachinelearningmethodsforspatiallyresolvedtranscriptomicsdataanalysis

Ejemplares similares