Identification of spatial expression trends in single-cell gene expression data

Methods for spatial gene expression analyses at single-cell resolution are becoming available, whereas computational strategies for spatial gene expression analyses are lacking. We present a computational method (trendsceek) based on marked point processes that identifies genes with significant spat...

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Autores principales: Edsgärd, Daniel, Johnsson, Per, Sandberg, Rickard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6314435/
https://www.ncbi.nlm.nih.gov/pubmed/29553578
http://dx.doi.org/10.1038/nmeth.4634
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author Edsgärd, Daniel
Johnsson, Per
Sandberg, Rickard
author_facet Edsgärd, Daniel
Johnsson, Per
Sandberg, Rickard
author_sort Edsgärd, Daniel
collection PubMed
description Methods for spatial gene expression analyses at single-cell resolution are becoming available, whereas computational strategies for spatial gene expression analyses are lacking. We present a computational method (trendsceek) based on marked point processes that identifies genes with significant spatial expression trends. Trendsceek identifies significant genes in spatial transcriptomics and sequential FISH data and also reveal significant gene expression gradients and hotspots in low-dimensional projections of dissociated single-cell RNA-seq data.
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spelling pubmed-63144352019-01-02 Identification of spatial expression trends in single-cell gene expression data Edsgärd, Daniel Johnsson, Per Sandberg, Rickard Nat Methods Article Methods for spatial gene expression analyses at single-cell resolution are becoming available, whereas computational strategies for spatial gene expression analyses are lacking. We present a computational method (trendsceek) based on marked point processes that identifies genes with significant spatial expression trends. Trendsceek identifies significant genes in spatial transcriptomics and sequential FISH data and also reveal significant gene expression gradients and hotspots in low-dimensional projections of dissociated single-cell RNA-seq data. 2018-03-19 2018-05 /pmc/articles/PMC6314435/ /pubmed/29553578 http://dx.doi.org/10.1038/nmeth.4634 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Edsgärd, Daniel
Johnsson, Per
Sandberg, Rickard
Identification of spatial expression trends in single-cell gene expression data
title Identification of spatial expression trends in single-cell gene expression data
title_full Identification of spatial expression trends in single-cell gene expression data
title_fullStr Identification of spatial expression trends in single-cell gene expression data
title_full_unstemmed Identification of spatial expression trends in single-cell gene expression data
title_short Identification of spatial expression trends in single-cell gene expression data
title_sort identification of spatial expression trends in single-cell gene expression data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6314435/
https://www.ncbi.nlm.nih.gov/pubmed/29553578
http://dx.doi.org/10.1038/nmeth.4634
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AT johnssonper identificationofspatialexpressiontrendsinsinglecellgeneexpressiondata
AT sandbergrickard identificationofspatialexpressiontrendsinsinglecellgeneexpressiondata

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