sagenet 0.1.1

Spatial reconstruction of dissociated single-cell data

|PyPI| |Docs|

SageNet: Spatial reconstruction of dissociated single-cell datasets using graph neural networks

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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="">
        <img src="https://user-images.githubusercontent.com/55977725/145551267-2611c05f-0f7f-49e5-8859-0e6f5994bdb0.png"
         width="700px" 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.

Installation

.. note::

0.1.0 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.

PyPI

The easiest way to get SageNet is through pip using the following command::

pip install sagenet

Development

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 .

Usage

::

import sagenet as sg
import scanpy as sc
import squidpy as sq
import anndata as ad
import random
random.seed(10)

Training phase:

Input:

• Expression matrix associated with the (spatial) reference dataset (an anndata object)

::

adata_r = sg.datasets.seqFISH1()

• gene-gene interaction network

::

glasso(adata_r, [0.5, 0.75, 1])

• one or more partitionings of the spatial reference into distinct connected neighborhoods of cells or spots

::

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:

• A set of pre-trained models (one for each partitioning)

::

!mkdir models
!mkdir models/seqFISH_ref
sg_obj.save_model_as_folder('models/seqFISH_ref')	

• A concensus scoring of spatially informativity of each gene

::

ind = np.argsort(-adata_r.var['seqFISH_ref_entropy'])[0:12]
with rc_context({'figure.figsize': (4, 4)}):
	sc.pl.spatial(adata_r, color=list(adata_r.var_names[ind]), ncols=4, spot_size=0.03, legend_loc=None)

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<p align="center">
    <a href="">
        <img src="https://user-images.githubusercontent.com/55977725/145543540-23a51e03-c860-422f-b2e5-14da5f07669d.png"
         width="800px" alt="spatial markers">
    </a>
</p>

Mapping phase

Input:

• Expression matrix associated with the (dissociated) query dataset (an anndata object) ::

  adata_q = sg.datasets.MGA()

Mapping: ::

sg_obj.map_query(adata_q)

Output:

• The reconstructed cell-cell spatial distance matrix ::

  adata_q.obsm['dist_map']

• A concensus scoring of mapability (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)

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<p align="center">
    <a href="">
        <img src="https://github.com/MarioniLab/sagenet/files/7687712/umapeli-11.pdf"
         width="900px" alt="reconstructed space">
    </a>
</p>

Notebooks

To see some examples of our pipeline's capability, look at the notebooks <https://github.com/MarioniLab/sagenet/notebooks>_ directory. The notebooks are also avaialble on google colab:

#. Intro to SageNet <https://colab.research.google.com/drive/1H4gVFfxzZgilk6nbUhzFlrFsa1vEHNTl?usp=sharing>_ #. Using multiple references <https://colab.research.google.com/drive/1H4gVFfxzZgilk6nbUhzFlrFsa1vEHNTl?usp=sharing>_

Interactive examples

• Spatial mapping of the mouse gastrulation atlas <https://www.dropbox.com/s/krjgp19i62p7nfx/joint_mapping-2_interactive.html?dl=0>_

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 <https://github.com/MarioniLab/sagenet/issues/new>__ or reach us by email <mailto:[e-mail]>_.

Contributions

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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