Bio Spatial Transcriptomics Spatial Deconvolution

import scanpy as sc
import anndata as ad
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import cell2location
from cell2location.utils.filtering import filter_genes
from cell2location.models import RegressionModel

# Load reference scRNA-seq
adata_ref = sc.read_h5ad('reference_scrna.h5ad')
adata_ref.obs['cell_type'] = adata_ref.obs['cell_type'].astype('category')

# Load spatial data
adata_vis = sc.read_h5ad('spatial_data.h5ad')

# Find shared genes
intersect = np.intersect1d(adata_vis.var_names, adata_ref.var_names)
adata_ref = adata_ref[:, intersect].copy()
adata_vis = adata_vis[:, intersect].copy()

# Select genes for deconvolution
selected = filter_genes(adata_ref, cell_count_cutoff=5, cell_percentage_cutoff2=0.03,
                        nonz_mean_cutoff=1.12)
adata_ref = adata_ref[:, selected].copy()

# Prepare reference
cell2location.models.RegressionModel.setup_anndata(
    adata_ref,
    labels_key='cell_type',
)

# Train reference model
mod = RegressionModel(adata_ref)
mod.train(max_epochs=250, use_gpu=True)

# Export reference signatures
adata_ref = mod.export_posterior(adata_ref, sample_kwargs={'num_samples': 1000})
ref_sig = adata_ref.varm['means_per_cluster_mu_fg']

# Ensure spatial data has same genes
adata_vis = adata_vis[:, adata_ref.var_names].copy()

# Setup spatial data
cell2location.models.Cell2location.setup_anndata(adata_vis)

# Train deconvolution model
mod_spatial = cell2location.models.Cell2location(
    adata_vis,
    cell_state_df=ref_sig,
    N_cells_per_location=10,  # Expected cells per spot
    detection_alpha=20,
)
mod_spatial.train(max_epochs=30000, use_gpu=True)

# Export results
adata_vis = mod_spatial.export_posterior(adata_vis, sample_kwargs={'num_samples': 1000})

# Cell type abundances stored in obsm
abundances = adata_vis.obsm['q05_cell_abundance_w_sf']
print(f'Cell types: {abundances.shape[1]}')

# Convert to proportions
proportions = abundances / abundances.sum(axis=1, keepdims=True)
adata_vis.obsm['cell_type_proportions'] = proportions

# Add dominant cell type
cell_types = adata_ref.obs['cell_type'].cat.categories
adata_vis.obs['dominant_cell_type'] = cell_types[proportions.argmax(axis=1)]

import tangram as tg

# Load data
adata_sc = sc.read_h5ad('reference_scrna.h5ad')
adata_sp = sc.read_h5ad('spatial_data.h5ad')

# Preprocess
sc.pp.normalize_total(adata_sc)
sc.pp.log1p(adata_sc)

# Find marker genes
sc.tl.rank_genes_groups(adata_sc, groupby='cell_type', method='wilcoxon')
markers = sc.get.rank_genes_groups_df(adata_sc, group=None)
markers = markers[markers['pvals_adj'] < 0.01].groupby('group').head(100)
marker_genes = markers['names'].unique().tolist()

# Prepare for Tangram
tg.pp_adatas(adata_sc, adata_sp, genes=marker_genes)

# Map single cells to spatial locations
ad_map = tg.map_cells_to_space(
    adata_sc,
    adata_sp,
    mode='clusters',
    cluster_label='cell_type',
    device='cuda:0',
)

# Get cell type proportions
tg.project_cell_annotations(ad_map, adata_sp, annotation='cell_type')
# Results in adata_sp.obsm['tangram_ct_pred']

# RCTD runs in R; use rpy2 for integration
import rpy2.robjects as ro
from rpy2.robjects import pandas2ri
pandas2ri.activate()

# Save data for R
adata_vis.write_h5ad('spatial_for_rctd.h5ad')
adata_ref.write_h5ad('reference_for_rctd.h5ad')

# R code for RCTD
r_code = '''
library(spacexr)
library(Seurat)

# Load data (convert from h5ad first)
# ... R-specific loading code ...

# Create RCTD object
rctd <- create.RCTD(puck, reference, max_cores=4)
rctd <- run_RCTD(rctd, doublet_mode='full')

# Get results
results <- rctd@results
weights <- normalize_weights(results$weights)
'''

# Plot cell type abundances spatially
cell_types_to_plot = ['T_cell', 'Macrophage', 'Epithelial', 'Fibroblast']

fig, axes = plt.subplots(2, 2, figsize=(12, 12))
for ax, ct in zip(axes.flatten(), cell_types_to_plot):
    ct_idx = list(adata_ref.obs['cell_type'].cat.categories).index(ct)
    adata_vis.obs[f'{ct}_proportion'] = proportions[:, ct_idx]
    sc.pl.spatial(adata_vis, color=f'{ct}_proportion', ax=ax, show=False,
                  title=ct, cmap='Reds', vmin=0, vmax=1)
plt.tight_layout()
plt.savefig('cell_type_proportions.png', dpi=150)

from matplotlib.patches import Wedge

def plot_pie_spatial(adata, proportions, cell_types, spot_size=0.5):
    fig, ax = plt.subplots(figsize=(12, 12))
    colors = plt.cm.tab20(np.linspace(0, 1, len(cell_types)))

    coords = adata.obsm['spatial']
    for i in range(adata.n_obs):
        x, y = coords[i]
        props = proportions[i]
        start_angle = 0
        for j, prop in enumerate(props):
            if prop > 0.01:  # Skip tiny proportions
                wedge = Wedge((x, y), spot_size * 50, start_angle,
                             start_angle + prop * 360, color=colors[j])
                ax.add_patch(wedge)
                start_angle += prop * 360

    ax.set_xlim(coords[:, 0].min() - 100, coords[:, 0].max() + 100)
    ax.set_ylim(coords[:, 1].min() - 100, coords[:, 1].max() + 100)
    ax.set_aspect('equal')
    ax.invert_yaxis()

    # Legend
    handles = [plt.Rectangle((0, 0), 1, 1, color=colors[i]) for i in range(len(cell_types))]
    ax.legend(handles, cell_types, loc='upper right')
    plt.savefig('pie_chart_spatial.png', dpi=150)

# Check correlation between expected and observed cell counts
# (if you have known cell type markers)

marker_genes = {
    'T_cell': ['CD3D', 'CD3E', 'CD4', 'CD8A'],
    'Macrophage': ['CD68', 'CD14', 'CSF1R'],
    'Epithelial': ['EPCAM', 'KRT8', 'KRT18'],
}

for ct, markers in marker_genes.items():
    available_markers = [m for m in markers if m in adata_vis.var_names]
    if available_markers:
        marker_expr = adata_vis[:, available_markers].X.mean(axis=1)
        ct_idx = list(cell_types).index(ct)
        ct_prop = proportions[:, ct_idx]
        corr = np.corrcoef(marker_expr.flatten(), ct_prop)[0, 1]
        print(f'{ct}: marker-proportion correlation = {corr:.3f}')

# Store results from different methods
adata_vis.obsm['cell2location'] = cell2location_proportions
adata_vis.obsm['tangram'] = tangram_proportions

# Correlation between methods
for ct_idx, ct in enumerate(cell_types):
    c2l = adata_vis.obsm['cell2location'][:, ct_idx]
    tg = adata_vis.obsm['tangram'][:, ct_idx]
    corr = np.corrcoef(c2l, tg)[0, 1]
    print(f'{ct}: cell2location vs tangram = {corr:.3f}')

# Save proportions as CSV
prop_df = pd.DataFrame(
    proportions,
    index=adata_vis.obs_names,
    columns=cell_types
)
prop_df.to_csv('cell_type_proportions.csv')

# Save annotated AnnData
adata_vis.write_h5ad('spatial_deconvolved.h5ad')