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  3. DEEPsc: A Deep Learning-Based Map Connecting Single-Cell Transcriptomics and Spatial Imaging Data

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DEEPsc: A Deep Learning-Based Map Connecting Single-Cell Transcriptomics and Spatial Imaging Data

What is being modeled?
Spatial locations of scRNA-seq data
Description & purpose of resource

DEEPsc uses a neural network to obtain a data-adaptive projection of cells in scRNA-seq to the corresponding spatial imaging or spatial transcriptomic data of the same tissue.

Spatial scales
cellular
tissue
This resource is currently
mature and useful in ongoing research
Has this resource been validated?
Yes
How has the resource been validated?

Validated on four pairs of scRNA-seq data and spatial data including drosophila embryo, mouse hair follicle, zebrafish embryo, and mouse cortex.

Link to Resource Credibility Assessment
DEEPsc: A Deep Learning-Based Map Connecting Single-Cell Transcriptomics and Spatial Imaging Data
Key publications (e.g. describing or using resource)

Maseda, Floyd, Zixuan Cang, and Qing Nie. "DEEPsc: A Deep Learning-based Map Connecting Single-Cell Transcriptomics and Spatial Imaging Data." Frontiers in Genetics 12 (2021): 348.

DOI link to publication describing this resource

https://doi.org/10.3389/fgene.2021.636743

Link to resource
DEEPsc package
Collaborators
Qing Nie (PI)
PI contact information
qnie@uci.edu
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