|PyPI| |Docs|
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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.
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<p align="center">
<a href="https://github.com/MarioniLab/sagenet/blob/main/">
<img src="https://user-images.githubusercontent.com/55977725/145551267-2611c05f-0f7f-49e5-8859-0e6f5994bdb0.png"
width="300px" alt="sagenet logo">
</a>
</p>SageNet is implemented with pytorch <https://pytorch.org/docs/stable/index.html> and pytorch-geometric <https://pytorch-geometric.readthedocs.io/en/latest/> to be modular, fast, and scalable. Also, it uses anndata to be compatible with scanpy <https://scanpy.readthedocs.io/en/stable/> and squidpy <https://squidpy.readthedocs.io/en/stable/> for pre- and post-processing steps.
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<p align="center">
<a href="https://github.com/MarioniLab/sagenet/blob/main/">
<img src="https://user-images.githubusercontent.com/55977725/160540580-7a477850-004a-4909-91ac-c9ffbc8d9929.jpg"
width="700px" alt="sagenet workflow">
</a>
</p>.. note::
v1.0
The dependency torch-geometric should be installed separately, corresponding the system specefities, look at this link <https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html>_ for instructions. We recommend to use Miniconda.
First, clone the repository using git::
git clone https://github.com/MarioniLab/sagenetThen, cd to the sagenet folder and run the install command::
cd sagenet
python setup.py install #or pip install .The easiest way to get SageNet is through pip using the following command::
pip install sagenet::
import sagenet as sg
import scanpy as sc
import squidpy as sq
import anndata as ad
import random
random.seed(10)Input:
anndata object)::
adata_r = sg.MGA_data.seqFISH1()::
glasso(adata_r, [0.5, 0.75, 1])::
adata_r.obsm['spatial'] = np.array(adata_r.obs[['x','y']])
sq.gr.spatial_neighbors(adata_r, coord_type="generic")
sc.tl.leiden(adata_r, resolution=.01, random_state=0, key_added='leiden_0.01', adjacency=adata_r.obsp["spatial_connectivities"])
sc.tl.leiden(adata_r, resolution=.05, random_state=0, key_added='leiden_0.05', adjacency=adata_r.obsp["spatial_connectivities"])
sc.tl.leiden(adata_r, resolution=.1, random_state=0, key_added='leiden_0.1', adjacency=adata_r.obsp["spatial_connectivities"])
sc.tl.leiden(adata_r, resolution=.5, random_state=0, key_added='leiden_0.5', adjacency=adata_r.obsp["spatial_connectivities"])
sc.tl.leiden(adata_r, resolution=1, random_state=0, key_added='leiden_1', adjacency=adata_r.obsp["spatial_connectivities"])Training: ::
sg_obj = sg.sage.sage(device=device)
sg_obj.add_ref(adata_r, comm_columns=['leiden_0.01', 'leiden_0.05', 'leiden_0.1', 'leiden_0.5', 'leiden_1'], tag='seqFISH_ref', epochs=20, verbose = False)Output:
::
!mkdir models
!mkdir models/seqFISH_ref
sg_obj.save_model_as_folder('models/seqFISH_ref') ::
ind = np.where(adata_r.var['ST_all_importance'] <= 5)[0]
SIGs = list(adata_r.var_names[ind])
with rc_context({'figure.figsize': (4, 4)}):
sc.pl.spatial(adata_r, color=SIGs, ncols=4, spot_size=0.03, legend_loc=None).. raw:: html
<p align="center">
<a href="https://github.com/MarioniLab/sagenet/blob/main/">
<img src="https://user-images.githubusercontent.com/55977725/145543540-23a51e03-c860-422f-b2e5-14da5f07669d.png"
width="800px" alt="spatial markers">
</a>
</p>Input:
Expression matrix associated with the (dissociated) query dataset (an anndata object)
::
adata_q = sg.MGA_data.scRNAseq()
Mapping: ::
sg_obj.map_query(adata_q)Output:
The reconstructed cell-cell spatial distance matrix ::
adata_q.obsm['dist_map']
A consensus scoring of mappability (uncertainity of mapping) of each cell to the references ::
adata_q.obs
::
import anndata
dist_adata = anndata.AnnData(adata_q.obsm['dist_map'], obs = adata_q.obs)
knn_indices, knn_dists, forest = sc.neighbors.compute_neighbors_umap(dist_adata.X, n_neighbors=50, metric='precomputed')
dist_adata.obsp['distances'], dist_adata.obsp['connectivities'] = sc.neighbors._compute_connectivities_umap(
knn_indices,
knn_dists,
dist_adata.shape[0],
50, # change to neighbors you plan to use
)
sc.pp.neighbors(dist_adata, metric='precomputed', use_rep='X')
sc.tl.umap(dist_adata)
sc.pl.umap(dist_adata, color='cell_type', palette=celltype_colours).. raw:: html
<p align="center">
<a href="https://github.com/MarioniLab/sagenet/blob/main/">
<img src="https://github.com/MarioniLab/sagenet/files/7687712/umapeli-11.pdf"
width="900px" alt="reconstructed space">
</a>
</p>To see some examples of our pipeline's capability, look at the notebooks <https://github.com/MarioniLab/sagenet/notebooks>_ directory. The notebooks are also available on google colab:
Spatial reconstruction of the developing human heart <https://colab.research.google.com/drive/1x8mXIaUxx9JdolbBIKU_4zqPvM3Wz5Zp?usp=sharing>_Spatial reconstruction of the mouse embryo <https://colab.research.google.com/drive/1H4gVFfxzZgilk6nbUhzFlrFsa1vEHNTl?usp=sharing>_Spatial mapping of the mouse gastrulation atlas <https://www.dropbox.com/s/krjgp19i62p7nfx/joint_mapping-2_interactive.html?dl=0>_ If you have a question or new architecture or a model that could be integrated into our pipeline, you can
post an issue <https://github.com/MarioniLab/sagenet/issues/new>_ or reach us by email <mailto:eheidari@student.ethz.ch>.
This work is led by Elyas Heidari and Shila Ghazanfar as a joint effort between MarioniLab@CRUK@EMBL-EBI <https://www.ebi.ac.uk/research-beta/marioni/> and RobinsonLab@UZH <https://robinsonlabuzh.github.io>.
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.. |travis| image:: https://travis-ci.com/MarioniLab/sagenet.svg?branch=main :target: https://travis-ci.com/MarioniLab/sagenet