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Introduction to gimVI

Imputing missing genes in spatial data from sequencing data with gimVI

import sys

#if branch is stable, will install via pypi, else will install from source
branch = "stable"
IN_COLAB = "google.colab" in sys.modules

if IN_COLAB and branch == "stable":
    !pip install --quiet scvi-tools[tutorials]
elif IN_COLAB and branch != "stable":
    !pip install --quiet --upgrade jsonschema
    !pip install --quiet git+$branch#egg=scvi-tools[tutorials]
import scanpy
import anndata
import numpy as np
import copy
import matplotlib.pyplot as plt

from scipy.stats import spearmanr
from import smfish, cortex
from scvi.external import GIMVI

train_size = 0.8
Global seed set to 0
spatial_data = smfish(run_setup_anndata=False)
seq_data = cortex(run_setup_anndata=False)
INFO     File
         already downloaded
INFO     Loading smFISH dataset
INFO     Downloading file at /data/yosef2/users/jhong/scvi-tutorials/data/expression.bin
Downloading...: 121130it [00:02, 53483.10it/s]
INFO     Loading Cortex data from /data/yosef2/users/jhong/scvi-tutorials/data/expression.bin
/data/yosef2/users/jhong/miniconda3/envs/r_tutorial/lib/python3.7/site-packages/anndata/_core/ ImplicitModificationWarning: Transforming to str index.
  warnings.warn("Transforming to str index.", ImplicitModificationWarning)
INFO     Finished loading Cortex data

Preparing the data

In this section, we hold out some of the genes in the spatial dataset in order to test the imputation results

#only use genes in both datasets
seq_data = seq_data[:, spatial_data.var_names].copy()

seq_gene_names = seq_data.var_names
n_genes = seq_data.n_vars
n_train_genes = int(n_genes*train_size)

#randomly select training_genes
rand_train_gene_idx = np.random.choice(range(n_genes), n_train_genes, replace = False)
rand_test_gene_idx = sorted(set(range(n_genes)) - set(rand_train_gene_idx))
rand_train_genes = seq_gene_names[rand_train_gene_idx]
rand_test_genes = seq_gene_names[rand_test_gene_idx]

#spatial_data_partial has a subset of the genes to train on
spatial_data_partial = spatial_data[:,rand_train_genes].copy()

#remove cells with no counts
scanpy.pp.filter_cells(spatial_data_partial, min_counts= 1)
scanpy.pp.filter_cells(seq_data, min_counts = 1)

#setup_anndata for spatial and sequencing data
GIMVI.setup_anndata(spatial_data_partial, labels_key='labels', batch_key='batch')
GIMVI.setup_anndata(seq_data, labels_key='labels')

#spatial_data should use the same cells as our training data
#cells may have been removed by scanpy.pp.filter_cells()
spatial_data = spatial_data[spatial_data_partial.obs_names]
INFO     Using batches from adata.obs["batch"]
INFO     Using labels from adata.obs["labels"]
INFO     Using data from adata.X
INFO     Successfully registered anndata object containing 4530 cells, 26 vars, 1 batches, 6
         labels, and 0 proteins. Also registered 0 extra categorical covariates and 0 extra
         continuous covariates.
INFO     Please do not further modify adata until model is trained.
INFO     No batch_key inputted, assuming all cells are same batch
INFO     Using labels from adata.obs["labels"]
INFO     Using data from adata.X
INFO     Successfully registered anndata object containing 2996 cells, 33 vars, 1 batches, 7
         labels, and 0 proteins. Also registered 0 extra categorical covariates and 0 extra
         continuous covariates.
INFO     Please do not further modify adata until model is trained.
/data/yosef2/users/jhong/miniconda3/envs/r_tutorial/lib/python3.7/site-packages/pandas/core/arrays/ FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.
  res = method(*args, **kwargs)

Creating the model and training

#create our model
model = GIMVI(seq_data, spatial_data_partial)

#train for 200 epochs

GPU available: True, used: True
TPU available: False, using: 0 TPU cores
Epoch 200/200: 100%|██████████| 200/200 [04:30<00:00,  1.35s/it, loss=24.5, v_num=1]

Analyzing the results

Getting the latent representations and plotting UMAPs

#get the latent representations for the sequencing and spatial data
latent_seq, latent_spatial = model.get_latent_representation()

#concatenate to one latent representation
latent_representation = np.concatenate([latent_seq, latent_spatial])
latent_adata = anndata.AnnData(latent_representation)

#labels which cells were from the sequencing dataset and which were from the spatial dataset
latent_labels = (['seq'] * latent_seq.shape[0]) + (['spatial'] * latent_spatial.shape[0])
latent_adata.obs['labels'] = latent_labels

#compute umap
scanpy.pp.neighbors(latent_adata, use_rep = 'X')

#save umap representations to original seq and spatial_datasets
seq_data.obsm['X_umap'] = latent_adata.obsm['X_umap'][:seq_data.shape[0]]
spatial_data.obsm['X_umap'] = latent_adata.obsm['X_umap'][seq_data.shape[0]:]
#umap of the combined latent space, color = 'labels', show = True)
... storing 'labels' as categorical
#umap of sequencing dataset, color = 'cell_type')
... storing 'precise_labels' as categorical
... storing 'cell_type' as categorical
#umap of spatial dataset, color = 'str_labels')

Getting Imputation Score

imputation_score() returns the median spearman r correlation over all the cells

# utility function for scoring the imputation
def imputation_score(model, data_spatial, gene_ids_test, normalized=True):
    _, fish_imputation = model.get_imputed_values(normalized=normalized)
    original, imputed = (
        data_spatial.X[:, gene_ids_test],
        fish_imputation[:, gene_ids_test],

    if normalized:
        original /= data_spatial.X.sum(axis=1).reshape(-1, 1)

    spearman_gene = []
    for g in range(imputed.shape[1]):
        if np.all(imputed[:, g] == 0):
            correlation = 0
            correlation = spearmanr(original[:, g], imputed[:, g])[0]
    return np.median(np.array(spearman_gene))

imputation_score(model, spatial_data, rand_test_gene_idx, True)

Plot imputation for Lamp5, which should have been hidden in the training

#utility function for plotting spatial genes
def plot_gene_spatial(model, data_spatial, gene):
    data_seq = model.adatas[0]
    data_fish = data_spatial

    fig, (ax_gt, ax) = plt.subplots(1, 2)

    if type(gene) == str:
        gene_id = list(data_seq.gene_names).index(gene)
        gene_id = gene

    x_coord = data_fish.obs["x_coord"]
    y_coord = data_fish.obs["y_coord"]

    def order_by_strenght(x, y, z):
        ind = np.argsort(z)
        return x[ind], y[ind], z[ind]

    s = 20

    def transform(data):
        return np.log(1 + 100 * data)

    # Plot groundtruth
    x, y, z = order_by_strenght(
        x_coord, y_coord, data_fish.X[:, gene_id] / (data_fish.X.sum(axis=1) + 1)
    ax_gt.scatter(x, y, c=transform(z), s=s, edgecolors="none", marker="s", cmap="Reds")

    _, imputed = model.get_imputed_values(normalized=True)
    x, y, z = order_by_strenght(x_coord, y_coord, imputed[:, gene_id])
    ax.scatter(x, y, c=transform(z), s=s, edgecolors="none", marker="s", cmap="Reds")

assert 'Lamp5' in rand_test_genes
plot_gene_spatial(model, spatial_data, 9)
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