Hello World!

In this notebook we show installation and basic usage of SageNet.

pyG dependencies

pytorch geometric has specefic dependencies which we highly recommend the user to install them following this.

!pip install -q torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cu111.html
!pip install -q torch-sparse -f https://data.pyg.org/whl/torch-1.10.0+cu111.html
!pip install -q git+https://github.com/pyg-team/pytorch_geometric.git

Install SageNet

The use can clone SageNet from GitHub and install it as follows.

!git clone https://github.com/MarioniLab/sagenet
%cd sagenet
!pip install .

Import packages

We use `anndata <https://anndata.readthedocs.io/en/latest/>`__ to be compatible with `scanpy <https://scanpy.readthedocs.io>`__ and `squidpy <https://squidpy.readthedocs.io/en/stable/>`__ for pre- and post-processing steps.

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

Load datasets

from copy import copy
adata_r = sg.datasets.seqFISH()
adata_q1 = copy(adata_r)
adata_q2 = sg.datasets.MGA()
sc.pp.subsample(adata_q1, fraction=0.25)
sc.pp.subsample(adata_q2, fraction=0.25)
adata_q = ad.concat([adata_q1, adata_q2], join="inner")
del adata_q1
del adata_q2

celltype_colours = {
  "Epiblast" : "#635547",
  "Primitive Streak" : "#DABE99",
  "Caudal epiblast" : "#9e6762",
  "PGC" : "#FACB12",
  "Anterior Primitive Streak" : "#c19f70",
  "Notochord" : "#0F4A9C",
  "Def. endoderm" : "#F397C0",
  "Definitive endoderm" : "#F397C0",
  "Gut" : "#EF5A9D",
  "Gut tube" : "#EF5A9D",
  "Nascent mesoderm" : "#C594BF",
  "Mixed mesoderm" : "#DFCDE4",
  "Intermediate mesoderm" : "#139992",
  "Caudal Mesoderm" : "#3F84AA",
  "Paraxial mesoderm" : "#8DB5CE",
  "Somitic mesoderm" : "#005579",
  "Pharyngeal mesoderm" : "#C9EBFB",
  "Splanchnic mesoderm" : "#C9EBFB",
  "Cardiomyocytes" : "#B51D8D",
  "Allantois" : "#532C8A",
  "ExE mesoderm" : "#8870ad",
  "Lateral plate mesoderm" : "#8870ad",
  "Mesenchyme" : "#cc7818",
  "Mixed mesenchymal mesoderm" : "#cc7818",
  "Haematoendothelial progenitors" : "#FBBE92",
  "Endothelium" : "#ff891c",
  "Blood progenitors 1" : "#f9decf",
  "Blood progenitors 2" : "#c9a997",
  "Erythroid1" : "#C72228",
  "Erythroid2" : "#f79083",
  "Erythroid3" : "#EF4E22",
  "Erythroid" : "#f79083",
  "Blood progenitors" : "#f9decf",
  "NMP" : "#8EC792",
  "Rostral neurectoderm" : "#65A83E",
  "Caudal neurectoderm" : "#354E23",
  "Neural crest" : "#C3C388",
  "Forebrain/Midbrain/Hindbrain" : "#647a4f",
  "Spinal cord" : "#CDE088",
  "Surface ectoderm" : "#f7f79e",
  "Visceral endoderm" : "#F6BFCB",
  "ExE endoderm" : "#7F6874",
  "ExE ectoderm" : "#989898",
  "Parietal endoderm" : "#1A1A1A",
  "Unknown" : "#FFFFFF",
  "Low quality" : "#e6e6e6",
  # somitic and paraxial types
  # colour from T chimera paper Guibentif et al Developmental Cell 2021
  "Cranial mesoderm" : "#77441B",
  "Anterior somitic tissues" : "#F90026",
  "Sclerotome" : "#A10037",
  "Dermomyotome" : "#DA5921",
  "Posterior somitic tissues" : "#E1C239",
  "Presomitic mesoderm" : "#9DD84A"
}

Preprocess the reference dataset

from sagenet.utils import glasso
import numpy as np
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"])

from matplotlib.pyplot import rc_context
with rc_context({'figure.figsize': (3, 3)}):
  sc.pl.spatial(adata_r, color=['cell_type', 'leiden_0.01', 'leiden_0.05', 'leiden_0.1', 'leiden_0.5', 'leiden_1'], ncols=3, spot_size=0.03, legend_loc=None)

Check for GPU availibility

import torch
if torch.cuda.is_available():
  dev = "cuda:0"
else:
  dev = "cpu"
device = torch.device(dev)
print(device)

cuda:0

Define the sagenet object

sg_obj = sg.sage.sage(device=device)

Train on the reference dataset

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)

Spatially informative genes

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)

Save the trained model

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

mkdir: cannot create directory ‘models’: File exists
mkdir: cannot create directory ‘models/seqFISH_ref’: File exists

Map the query dataset to space

sg_obj.map_query(adata_q)

/usr/local/lib/python3.7/dist-packages/torch_geometric/deprecation.py:13: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead
  warnings.warn(out)

Compute UMAP of the embedded cells

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, save='eli.pdf')

WARNING: saving figure to file figures/umapeli.pdf