Spage2vec: Unsupervised representation of localized spatial gene expression signatures.

ABSTRACT: Investigations of spatial cellular composition of tissue architectures revealed by multiplexed in situ RNA detection often rely on inaccurate cell segmentation or prior biological knowledge from complementary single-cell sequencing experiments. Here, we present spage2vec, an unsupervised segmentation-free approach for decrypting the spatial transcriptomic heterogeneity of complex tissues at subcellular resolution. Spage2vec represents the spatial transcriptomic landscape of tissue samples as a graph and leverages a powerful machine learning graph representation technique to create a lower dimensional representation of local spatial gene expression. We apply spage2vec to mouse brain data from three different in situ transcriptomic assays and to a spatial gene expression dataset consisting o

SUBMITTER: Partel G 

PROVIDER: S-EPMC7983892 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature