NICHEVI: A PROBABILISTIC FRAMEWORK TO EMBED CELLULAR INTERACTION IN SPATIAL TRANSCRIPTOMICS

Nathan Levy, Florian Ingelfinger, Can Ergen-Behr, Boaz Nadler

Keywords: Generative models, representation learning, cell interactions, spatial transcriptomics

TL;DR: We introduce NicheVI, a deep learning model that decodes gene expression, niche cell-type composition, and variation in cell state of other cells within a niche.

Abstract:

Spatial transcriptomics has the potential to reveal cellular interactions by measuring gene expression in situ while maintaining the tissue context of each cell.
Existing deep learning methods for non-spatial single-cell omics optimize cellular
embeddings of gene expression. They enable the harmonization between experimental batches while embedding the variation of the cell state. Spatial transcrip-
tomics allows one to study the cell state composition of a spatial neighborhood.
These cellular niches confine the tissue organization and encompass functional
units of an organ. However, computational methods for encoding meaningful low-
dimensional representations of both gene expression and cell states of neighboring
cells a are currently lacking. Here, we introduce NicheVI, a deep learning model
that decodes gene expression, niche cell-type composition, and variation in cell
state of other cells within a niche. In case studies, NicheVI uncovered additional
fine-grained heterogeneity of cell-types not captured by non-spatial and other spatially aware models and corresponding to the cellular niche

Submission Number: 51