Unsupervised spatially embedded deep representation of spatial transcriptomics
By Huan Fu, Hang Xu, Kelvin Chong, Mengwei Li, Kok Siong Ang, H. Lee, Jin Ling, Ao Chen, Ling Shao, Longqi Liu and 1 more. bioRxiv, 2021
403 citations recorded by the source corpus.
Spatial transcriptomics enable us to dissect tissue heterogeneity and map out inter-cellular communications. Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting the data. We present SEDR, an unsupervised spatially embedded deep representation of both transcript and spatial information. The SEDR pipeline uses a deep autoencoder to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. We applied SEDR on human dorsolateral prefrontal cortex data and achieved better clustering accuracy, and correctly retraced the prenatal cortex development order with trajectory analysis. We also found the SEDR representation to be eminently suited for batch integration. Applying SEDR to human breast cancer data, we discerned heterogeneous sub-regions within a visually homogenous tumor region, identifying a tumor core with pro-inflammatory microenvironment and an outer ring region enriched with tumor associated macrophages which drives an immune suppressive microenvironment.
Subfields: Immunology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/b186a927d5af3c68049d2b18e656539a909b48f7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
Citations in the corpus
- DeepST: identifying spatial domains in spatial transcriptomics by deep learning (2022, methodology): “SEDR (8) employs a deep auto-encoder network and a graph auto-encoder to embed spatial information.”
- Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder (2021, methodology): “Some recent algorithms adapt the clustering methods by considering the similarity between adjacent spots to better account for the spatial dependency of gene expressions 10-12 .”
- Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, methodology): “Specifically, in the simulations, we set cells_per_spot to be a random number from a uniform distribution U(8, 12) with an expected value of 10.”