Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model

Spatial transcriptomics technologies have recently emerged as a powerful tool for measuring spatially resolved gene expression directly in tissues sections, revealing cell types and their dysfunction in unprecedented detail. However, spatial transcriptomics technologies are limited in their ability...

Descripción completa

Detalles Bibliográficos
Autores principales: Zubair, Asif, Chapple, Richard H, Natarajan, Sivaraman, Wright, William C, Pan, Min, Lee, Hyeong-Min, Tillman, Heather, Easton, John, Geeleher, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Methods Online
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371936/
https://www.ncbi.nlm.nih.gov/pubmed/35536287
http://dx.doi.org/10.1093/nar/gkac320

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371936/
https://www.ncbi.nlm.nih.gov/pubmed/35536287
http://dx.doi.org/10.1093/nar/gkac320

Ejemplares similares