Inferring histology-associated gene expression gradients in spatial transcriptomic studies.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
23 Aug 2024
23 Aug 2024
Historique:
received:
29
05
2023
accepted:
24
07
2024
medline:
24
8
2024
pubmed:
24
8
2024
entrez:
23
8
2024
Statut:
epublish
Résumé
Spatially resolved transcriptomics has revolutionized RNA studies by aligning RNA abundance with tissue structure, enabling direct comparisons between histology and gene expression. Traditional approaches to identifying signature genes often involve preliminary data grouping, which can overlook subtle expression patterns in complex tissues. We present Spatial Gradient Screening, an algorithm which facilitates the supervised detection of histology-associated gene expression patterns without prior data grouping. Utilizing spatial transcriptomic data along with single-cell deconvolution from injured mouse cortex, and TCR-seq data from brain tumors, we compare our methodology to standard differential gene expression analysis. Our findings illustrate both the advantages and limitations of cluster-free detection of gene expression, offering more profound insights into the spatial architecture of transcriptomes. The algorithm is embedded in SPATA2, an open-source framework written in R, which provides a comprehensive set of tools for investigating gene expression within tissue.
Identifiants
pubmed: 39179527
doi: 10.1038/s41467-024-50904-x
pii: 10.1038/s41467-024-50904-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7280Informations de copyright
© 2024. The Author(s).
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