bio-spatial-transcriptomics-image-analysis

name	bio-spatial-transcriptomics-image-analysis
description	Process and analyze tissue images from spatial transcriptomics data using Squidpy. Extract image features, segment cells/nuclei, and compute morphological features from H&E or IF images. Use when processing tissue images for spatial transcriptomics.
tool_type	python
primary_tool	squidpy

Image Analysis for Spatial Transcriptomics

Extract features and segment tissue images in spatial transcriptomics data.

Required Imports

import squidpy as sq
import scanpy as sc
import numpy as np
import matplotlib.pyplot as plt
from skimage import io, filters, segmentation

Access Tissue Images

# Get image from Visium data
library_id = list(adata.uns['spatial'].keys())[0]
img_dict = adata.uns['spatial'][library_id]['images']

# High and low resolution images
hires = img_dict['hires']
lowres = img_dict['lowres']

print(f'Hires shape: {hires.shape}')
print(f'Lowres shape: {lowres.shape}')

# Get scale factors
scalef = adata.uns['spatial'][library_id]['scalefactors']
spot_diameter = scalef['spot_diameter_fullres']
hires_scale = scalef['tissue_hires_scalef']

Create ImageContainer

# Squidpy's ImageContainer for organized image handling
img = sq.im.ImageContainer(adata.uns['spatial'][library_id]['images']['hires'])
print(img)

# Or load from file
img = sq.im.ImageContainer('tissue_image.tif')

# Access the image array
arr = img['image'].values

Extract Image Features per Spot

# Calculate image features for each spot
sq.im.calculate_image_features(
    adata,
    img,
    features=['summary', 'histogram', 'texture'],
    key_added='img_features',
    spot_scale=1.0,  # Fraction of spot diameter
    n_jobs=4,
)

# Features stored in adata.obsm['img_features']
print(f"Image features shape: {adata.obsm['img_features'].shape}")

Available Image Features

# Summary statistics
sq.im.calculate_image_features(adata, img, features='summary')
# Mean, std, etc. per channel

# Histogram features
sq.im.calculate_image_features(adata, img, features='histogram', features_kwargs={'histogram': {'bins': 16}})
# Intensity distribution

# Texture features (GLCM)
sq.im.calculate_image_features(adata, img, features='texture')
# Contrast, homogeneity, correlation, ASM

# Custom features
sq.im.calculate_image_features(
    adata, img,
    features=['summary', 'texture'],
    features_kwargs={
        'summary': {'quantiles': [0.1, 0.5, 0.9]},
        'texture': {'distances': [1, 2], 'angles': [0, np.pi/4, np.pi/2]},
    }
)

Segment Cells/Nuclei

# Segment using watershed
sq.im.segment(
    img,
    layer='image',
    method='watershed',
    channel=0,  # Use first channel
    thresh=0.5,
)

# Access segmentation mask
seg_mask = img['segmented_watershed'].values

Segment with Cellpose

# Cellpose provides better cell segmentation
from cellpose import models

# Load model
model = models.Cellpose(model_type='nuclei')

# Get image array
image = img['image'].values[:, :, 0]  # Single channel

# Segment
masks, flows, styles, diams = model.eval(image, diameter=30, channels=[0, 0])

# Add to ImageContainer
img.add_img(masks, layer='cellpose_masks')

Extract Spot Image Crops

# Get image crop around each spot
def get_spot_crop(adata, img_arr, spot_idx, crop_size=100):
    coords = adata.obsm['spatial'][spot_idx]
    scalef = adata.uns['spatial'][library_id]['scalefactors']['tissue_hires_scalef']

    x, y = int(coords[0] * scalef), int(coords[1] * scalef)
    half = crop_size // 2

    crop = img_arr[max(0, y-half):y+half, max(0, x-half):x+half]
    return crop

# Get crop for spot 0
crop = get_spot_crop(adata, hires, 0)
plt.imshow(crop)

Color Deconvolution (H&E)

from skimage.color import rgb2hed, hed2rgb

# Separate H&E stains
hed = rgb2hed(hires)
hematoxylin = hed[:, :, 0]
eosin = hed[:, :, 1]
dab = hed[:, :, 2]

# Visualize
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
axes[0].imshow(hematoxylin, cmap='gray')
axes[0].set_title('Hematoxylin')
axes[1].imshow(eosin, cmap='gray')
axes[1].set_title('Eosin')
axes[2].imshow(hires)
axes[2].set_title('Original')
plt.tight_layout()

Compute Morphological Features

from skimage.measure import regionprops_table

# Get properties from segmentation
props = regionprops_table(
    seg_mask,
    intensity_image=hires[:, :, 0],
    properties=['label', 'area', 'eccentricity', 'solidity', 'mean_intensity']
)

import pandas as pd
morph_df = pd.DataFrame(props)
print(morph_df.describe())

Use Image Features for Clustering

# Combine expression and image features
import numpy as np

# Get expression PCA
expr_pca = adata.obsm['X_pca'][:, :20]

# Get image features
img_features = adata.obsm['img_features']

# Scale and combine
from sklearn.preprocessing import StandardScaler
expr_scaled = StandardScaler().fit_transform(expr_pca)
img_scaled = StandardScaler().fit_transform(img_features)

# Weight combination
alpha = 0.3  # Image weight
combined = np.hstack([
    (1 - alpha) * expr_scaled,
    alpha * img_scaled
])

adata.obsm['X_combined'] = combined

# Cluster on combined features
sc.pp.neighbors(adata, use_rep='X_combined')
sc.tl.leiden(adata, key_added='combined_leiden')

Smooth Expression with Image

# Use image similarity to smooth expression
from scipy.spatial.distance import cdist

# Compute image similarity matrix
img_features = adata.obsm['img_features']
img_sim = 1 / (1 + cdist(img_features, img_features, metric='euclidean'))

# Normalize
img_sim = img_sim / img_sim.sum(axis=1, keepdims=True)

# Smooth expression
X_smoothed = img_sim @ adata.X

adata.layers['img_smoothed'] = X_smoothed

Related Skills

• spatial-data-io - Load spatial data with images
• spatial-visualization - Visualize images with expression
• spatial-domains - Use image features for domain detection