SQUINT: Spatial Quantization for Understanding and IN-painting Tissues

Arpit Merchant, Sebastian Birk, Amirhossein Vahidi, Daniyal Jafree, Lloyd Steele, April Rose Foster, Vijaya Baskar MS, Muzlifah Haniffa, Mohammad Lotfollahi

Abstract:

Generative modeling of spatially-resolved transcriptomics (SRT) at single-cell
resolution is a key challenge in genomics towards profiling microenvironments
in tissues with various biological and clinical applications. Typically, graph auto-
encoders have been employed for learning continuous cell representations towards
downstream tasks such as niche identification. However, these provide limited
utility for modeling SRT data at the tissue level. To address this challenge, we
propose a novel method called SQUINT which uses vector quantization on latent
cell embeddings obtained from graph neural networks. By masking gene expression
for a fraction of cells during training and conditioning on relative spatial distances,
SQUINT learns a set of informative codes that can be used as cell tokens to
model tissues as token sequences. During inference, SQUINT uses these codes
to impute gene expression of unseen microenvironments at user-specified spatial
locations in a tissue section and outperforms a benchmark method for generating
gene expression. We further showcase the translational relevance of these codes
capturing meaningful tissue structures beyond individual cells through downstream
tasks such as 3D imputation, tumour stratification, and cell-type perturbation.

Submission Number: 9