AgentSkillsCN

bio-spatial-transcriptomics-spatial-data-io

使用 Squidpy 和 SpatialData 加载 Visium、Xenium、MERFISH、Slide-seq 等平台的空间转录组学数据。读取 Space Ranger 输出，转换数据格式，并访问空间坐标。在加载 Visium、Xenium、MERFISH，或其他空间数据时使用。

SKILL.md
--- frontmatter
name: bio-spatial-transcriptomics-spatial-data-io
description: Load spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms using Squidpy and SpatialData. Read Space Ranger outputs, convert formats, and access spatial coordinates. Use when loading Visium, Xenium, MERFISH, or other spatial data.
tool_type: python
primary_tool: squidpy

Spatial Data I/O

Load and work with spatial transcriptomics data from various platforms.

Required Imports

python
import squidpy as sq
import scanpy as sc
import anndata as ad
import spatialdata as sd
import spatialdata_io as sdio

Load 10X Visium Data

python
# Load Space Ranger output (standard method)
adata = sq.read.visium('path/to/spaceranger/output/')
print(f'Loaded {adata.n_obs} spots, {adata.n_vars} genes')

# Spatial coordinates are in adata.obsm['spatial']
print(f"Spatial coords shape: {adata.obsm['spatial'].shape}")

# Image is in adata.uns['spatial']
library_id = list(adata.uns['spatial'].keys())[0]
print(f'Library ID: {library_id}')

Load Visium with Scanpy

python
# Alternative using Scanpy directly
adata = sc.read_visium('path/to/spaceranger/output/')

# Access tissue image
img = adata.uns['spatial'][library_id]['images']['hires']
scale_factor = adata.uns['spatial'][library_id]['scalefactors']['tissue_hires_scalef']

Load 10X Xenium Data

python
# Load Xenium output
adata = sq.read.xenium('path/to/xenium/output/')
print(f'Loaded {adata.n_obs} cells')

# Xenium has single-cell resolution
print(f"Cell coordinates: {adata.obsm['spatial'].shape}")

Load with SpatialData (Recommended for New Projects)

python
import spatialdata_io as sdio

# Load Visium as SpatialData object
sdata = sdio.visium('path/to/spaceranger/output/')
print(sdata)

# Load Xenium
sdata = sdio.xenium('path/to/xenium/output/')

# Access components
table = sdata.tables['table']  # AnnData with expression
shapes = sdata.shapes  # Spatial shapes (spots, cells)
images = sdata.images  # Tissue images

Load MERFISH Data

python
# MERFISH (Vizgen MERSCOPE)
sdata = sdio.merscope('path/to/merscope/output/')

# Or as AnnData
adata = sq.read.vizgen('path/to/vizgen/output/', counts_file='cell_by_gene.csv', meta_file='cell_metadata.csv')

Load Slide-seq Data

python
# Slide-seq / Slide-seqV2
adata = sq.read.slideseq('beads.csv', coordinates_file='coords.csv')

Load Nanostring CosMx

python
# CosMx spatial molecular imaging
sdata = sdio.cosmx('path/to/cosmx/output/')

Load Stereo-seq Data

python
# Stereo-seq (BGI)
sdata = sdio.stereoseq('path/to/stereoseq/output/')

Load from H5AD with Spatial Coordinates

python
# If you have h5ad with spatial already stored
adata = sc.read_h5ad('spatial_data.h5ad')

# Verify spatial data exists
if 'spatial' in adata.obsm:
    print('Has spatial coordinates')
if 'spatial' in adata.uns:
    print('Has image data')

Create Spatial AnnData from Scratch

python
import numpy as np
import pandas as pd

# Expression matrix
X = np.random.poisson(5, size=(1000, 500))

# Spatial coordinates
spatial_coords = np.random.rand(1000, 2) * 1000  # x, y in pixels

# Create AnnData
adata = ad.AnnData(X)
adata.obs_names = [f'spot_{i}' for i in range(1000)]
adata.var_names = [f'gene_{i}' for i in range(500)]
adata.obsm['spatial'] = spatial_coords

# Add minimal spatial metadata for Squidpy
adata.uns['spatial'] = {
    'library_id': {
        'scalefactors': {'tissue_hires_scalef': 1.0, 'spot_diameter_fullres': 50},
    }
}

Access Spatial Coordinates

python
# Get coordinates as numpy array
coords = adata.obsm['spatial']
x_coords = coords[:, 0]
y_coords = coords[:, 1]

# Get coordinates as DataFrame
coord_df = pd.DataFrame(adata.obsm['spatial'], index=adata.obs_names, columns=['x', 'y'])

Access Tissue Images

python
# Get high-resolution image
library_id = list(adata.uns['spatial'].keys())[0]
hires_img = adata.uns['spatial'][library_id]['images']['hires']
lowres_img = adata.uns['spatial'][library_id]['images']['lowres']

# Scale factors
scalef = adata.uns['spatial'][library_id]['scalefactors']
print(f"Hires scale: {scalef['tissue_hires_scalef']}")
print(f"Spot diameter: {scalef['spot_diameter_fullres']}")

Convert Between Formats

python
# SpatialData to AnnData
sdata = sdio.visium('path/to/data/')
adata = sdata.tables['table'].copy()
adata.obsm['spatial'] = np.array(sdata.shapes['spots'][['x', 'y']])

# Save as h5ad
adata.write_h5ad('spatial_converted.h5ad')

# Save SpatialData
sdata.write('spatial_data.zarr')

Load Multiple Samples

python
# Load and concatenate multiple Visium samples
samples = ['sample1', 'sample2', 'sample3']
adatas = []

for sample in samples:
    adata = sq.read.visium(f'data/{sample}/')
    adata.obs['sample'] = sample
    adatas.append(adata)

# Concatenate
adata_combined = ad.concat(adatas, label='sample', keys=samples)
print(f'Combined: {adata_combined.n_obs} spots')

Subset by Spatial Region

python
# Select spots in a rectangular region
x_min, x_max = 1000, 2000
y_min, y_max = 1500, 2500

coords = adata.obsm['spatial']
in_region = (coords[:, 0] >= x_min) & (coords[:, 0] <= x_max) & (coords[:, 1] >= y_min) & (coords[:, 1] <= y_max)

adata_region = adata[in_region].copy()
print(f'Selected {adata_region.n_obs} spots')

Related Skills

  • •spatial-preprocessing - QC and normalization after loading
  • •spatial-visualization - Plot spatial data
  • •single-cell/data-io - Non-spatial scRNA-seq data loading