Partial alignment of multislice spatially resolved transcriptomics data.


Journal

Genome research
ISSN: 1549-5469
Titre abrégé: Genome Res
Pays: United States
ID NLM: 9518021

Informations de publication

Date de publication:
07 2023
Historique:
received: 06 01 2023
accepted: 27 03 2023
pmc-release: 01 01 2024
medline: 28 8 2023
pubmed: 9 8 2023
entrez: 8 8 2023
Statut: ppublish

Résumé

Spatially resolved transcriptomics (SRT) technologies measure messenger RNA (mRNA) expression at thousands of locations in a tissue slice. However, nearly all SRT technologies measure expression in two-dimensional (2D) slices extracted from a 3D tissue, thus losing information that is shared across multiple slices from the same tissue. Integrating SRT data across multiple slices can help recover this information and improve downstream expression analyses, but multislice alignment and integration remains a challenging task. Existing methods for integrating SRT data either do not use spatial information or assume that the morphology of the tissue is largely preserved across slices, an assumption that is often violated because of biological or technical reasons. We introduce PASTE2, a method for

Identifiants

pubmed: 37553263
pii: gr.277670.123
doi: 10.1101/gr.277670.123
pmc: PMC10538490
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

1124-1132

Subventions

Organisme : NCI NIH HHS
ID : U24 CA248453
Pays : United States
Organisme : NCI NIH HHS
ID : U24 CA264027
Pays : United States

Commentaires et corrections

Type : UpdateOf

Informations de copyright

© 2023 Liu et al.; Published by Cold Spring Harbor Laboratory Press.

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Auteurs

Xinhao Liu (X)

Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA.

Ron Zeira (R)

Verily Life Sciences, Tel Aviv 6789141, Israel.

Benjamin J Raphael (BJ)

Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA; braphael@princeton.edu.

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