菜单
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.
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| 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 |
Reference examples tested with: Cellpose 3.0+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scikit-learn 1.4+, scipy 1.12+, squidpy 1.3+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturesIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Segment cells in my tissue image" → Extract image features, segment nuclei/cells, and compute morphological features from H&E or immunofluorescence images paired with spatial data.
squidpy.im.process(), squidpy.im.segment() with Cellpose backendExtract features and segment tissue images in spatial transcriptomics data.
import squidpy sq
scanpy sc
numpy np
matplotlib.pyplot plt
skimage io, filters, segmentation
# 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']
Goal: Wrap tissue images in Squidpy's ImageContainer for structured access and feature extraction.
Approach: Initialize an ImageContainer from the AnnData image data or a TIFF file.
# 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
Goal: Compute image-derived features (summary statistics, texture) for each spatial spot.
Approach: Use Squidpy's calculate_image_features to extract per-spot features from the tissue image within each spot's footprint.
# 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}")
# 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]},
}
)
Goal: Segment individual cells or nuclei from tissue images using classical methods.
Approach: Apply watershed segmentation through Squidpy's segment method on a selected image channel.
# 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
Goal: Perform deep learning-based cell segmentation for higher accuracy than classical methods.
Approach: Use Cellpose's pretrained nuclei model to detect and label individual cells in the tissue image.
# 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')
# 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)
Goal: Separate hematoxylin and eosin stain channels from an H&E tissue image.
Approach: Convert RGB to HED color space using scikit-image, then extract individual stain channels.
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()
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())
Goal: Improve spatial domain detection by combining gene expression and image morphology features.
Approach: Scale and concatenate expression PCA and image features with a tunable weight, then cluster on the combined representation.
# 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')
# 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