Agent skill

bio-spatial-transcriptomics-image-analysis

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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Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-spatial-transcriptomics-image-analysis

SKILL.md

Version Compatibility

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:

  • Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Image Analysis for Spatial Transcriptomics

"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.

  • Python: squidpy.im.process(), squidpy.im.segment() with Cellpose backend

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

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.

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

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.

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

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.

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

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.

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)

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.

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

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.

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

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