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💫 Graph-based foundation model for spatial transcriptomics data

Novae is a deep learning model for spatial domain assignments of spatial transcriptomics data (at both single-cell or spot resolution). It works across multiple gene panels, tissues, and technologies. Novae offers several additional features, including: (i) native batch-effect correction, (ii) analysis of spatially variable genes and pathways, and (iii) architecture analysis of tissue slides.

Info

Novae was developed by the authors of sopa and is part of the scverse ecosystem.

Overview

(a) Novae was trained on a large dataset, and is shared on Hugging Face Hub. (b) Illustration of the main tasks and properties of Novae. (c) Illustration of the method behind Novae (self-supervision on graphs, adapted from SwAV).

Why using Novae

• It is already pretrained on a large dataset (pan human/mouse tissues, brain, ...). Therefore, you can compute spatial domains in a zero-shot manner (i.e., without fine-tuning).
• It has been developed to find consistent domains across many slides. This also works if you have different technologies (e.g., MERSCOPE/Xenium) and multiple gene panels.
• You can natively correct batch-effect, without using external tools.
• After inference, the spatial domain assignment is super fast, allowing you to try multiple resolutions easily.
• It supports many downstream tasks, all included inside one framework.