SageNet: Spatial reconstruction of single-cell dissociated datasets using graph neural networks¶
SageNet is a robust and generalizable graph neural network approach that probabilistically maps dissociated single cells from an scRNAseq dataset to their hypothetical tissue of origin using one or more reference datasets aquired by spatially resolved transcriptomics techniques. It is compatible with both high-plex imaging (e.g., seqFISH, MERFISH, etc.) and spatial barcoding (e.g., 10X visium, Slide-seq, etc.) datasets as the spatial reference.
SageNet is implemented with pytorch and pytorch-geometric to be modular, fast, and scalable. Also, it uses anndata to be compatible with scanpy and squidpy for pre- and post-processing steps.
Installation¶
You can get the latest development version of our toolkit from Github using the following steps:
First, clone the repository using git:
git clone https://github.com/MarioniLab/sagenet
Then, cd to the sagenet folder and run the install command:
cd sagenet
python setup.py install #or pip install `
The dependency torch-geometric should be installed separately, corresponding the system specefities, look at this link for instructions.
Notebooks¶
To see some examples of our pipeline’s capability, look at the notebooks directory. The notebooks are also avaialble on google colab:
Support and contribute¶
If you have a question or new architecture or a model that could be integrated into our pipeline, you can post an issue or reach us by email.
Contributions¶
This work is led by Elyas Heidari and Shila Ghazanfar as a joint effort between MarioniLab@CRUK@EMBL-EBI and RobinsonLab@UZH.