--- 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 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- # Image Analysis for Spatial Transcriptomics Extract features and segment tissue images in spatial transcriptomics data. ## Required Imports ```python 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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) ```python 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 ```python 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 ```python # 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 ```python # 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