Mapping the topography of spatial gene expression with interpretable deep learning

Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a topographic m...

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Detalles Bibliográficos
Autores principales: Chitra, Uthsav, Arnold, Brian J., Sarkar, Hirak, Ma, Cong, Lopez-Darwin, Sereno, Sanno, Kohei, Raphael, Benjamin J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592770/
https://www.ncbi.nlm.nih.gov/pubmed/37873258
http://dx.doi.org/10.1101/2023.10.10.561757

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592770/
https://www.ncbi.nlm.nih.gov/pubmed/37873258
http://dx.doi.org/10.1101/2023.10.10.561757

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