Graph-based foundation model for spatial transcriptomics data
Project description
💫 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.
Documentation
Check Novae's documentation to get started. It contains installation explanations, API details, and tutorials.
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 behing Novae (self-supervision on graphs, adapted from SwAV).
Installation
PyPI
novae
can be installed via PyPI
on all OS, for python>=3.9
.
pip install novae
Editable mode
To install novae
in editable mode (e.g., to contribute), clone the repository and choose among the options below.
pip install -e . # pip, minimal dependencies
pip install -e '.[dev]' # pip, all extras
poetry install # poetry, minimal dependencies
poetry install --all-extras # poetry, all extras
Usage
Here is a minimal usage example. For more details, refer to the documentation.
import novae
model = novae.Novae.from_pretrained("MICS-Lab/novae-human-0")
model.compute_representations(adata, zero_shot=True)
model.assign_domains(adata)
Cite us
TODO: submit preprint (coming soon)
Project details
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