Alignment of spatial genomics data using deep Gaussian processes.


Journal

Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604

Informations de publication

Date de publication:
09 2023
Historique:
received: 10 01 2022
accepted: 06 07 2023
medline: 8 9 2023
pubmed: 18 8 2023
entrez: 17 8 2023
Statut: ppublish

Résumé

Spatially resolved genomic technologies have allowed us to study the physical organization of cells and tissues, and promise an understanding of local interactions between cells. However, it remains difficult to precisely align spatial observations across slices, samples, scales, individuals and technologies. Here, we propose a probabilistic model that aligns spatially-resolved samples onto a known or unknown common coordinate system (CCS) with respect to phenotypic readouts (for example, gene expression). Our method, Gaussian Process Spatial Alignment (GPSA), consists of a two-layer Gaussian process: the first layer maps observed samples' spatial locations onto a CCS, and the second layer maps from the CCS to the observed readouts. Our approach enables complex downstream spatially aware analyses that are impossible or inaccurate with unaligned data, including an analysis of variance, creation of a dense three-dimensional (3D) atlas from sparse two-dimensional (2D) slices or association tests across data modalities.

Identifiants

pubmed: 37592182
doi: 10.1038/s41592-023-01972-2
pii: 10.1038/s41592-023-01972-2
pmc: PMC10482692
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1379-1387

Subventions

Organisme : NIEHS NIH HHS
ID : P30 ES010126
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002489
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL149683
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL133218
Pays : United States
Organisme : NCI NIH HHS
ID : U2C CA233195
Pays : United States

Informations de copyright

© 2023. The Author(s).

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Auteurs

Andrew Jones (A)

Department of Computer Science, Princeton University, Princeton, NJ, USA.

F William Townes (FW)

Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA.

Didong Li (D)

Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.

Barbara E Engelhardt (BE)

Gladstone Institutes, San Francisco, CA, USA. bengelhardt@stanford.edu.
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. bengelhardt@stanford.edu.

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