SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies

Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptom...

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Detalles Bibliográficos
Autores principales: Zhu, Jiaqiang, Sun, Shiquan, Zhou, Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218388/
https://www.ncbi.nlm.nih.gov/pubmed/34154649
http://dx.doi.org/10.1186/s13059-021-02404-0
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author Zhu, Jiaqiang
Sun, Shiquan
Zhou, Xiang
author_facet Zhu, Jiaqiang
Sun, Shiquan
Zhou, Xiang
author_sort Zhu, Jiaqiang
collection PubMed
description Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02404-0.
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spelling pubmed-82183882021-06-23 SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies Zhu, Jiaqiang Sun, Shiquan Zhou, Xiang Genome Biol Method Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02404-0. BioMed Central 2021-06-21 /pmc/articles/PMC8218388/ /pubmed/34154649 http://dx.doi.org/10.1186/s13059-021-02404-0 Text en © The Author(s) 2021 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 Method
Zhu, Jiaqiang
Sun, Shiquan
Zhou, Xiang
SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
title SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
title_full SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
title_fullStr SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
title_full_unstemmed SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
title_short SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
title_sort spark-x: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218388/
https://www.ncbi.nlm.nih.gov/pubmed/34154649
http://dx.doi.org/10.1186/s13059-021-02404-0
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AT sunshiquan sparkxnonparametricmodelingenablesscalableandrobustdetectionofspatialexpressionpatternsforlargespatialtranscriptomicstudies
AT zhouxiang sparkxnonparametricmodelingenablesscalableandrobustdetectionofspatialexpressionpatternsforlargespatialtranscriptomicstudies

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