Spatial Transcriptomics Spatial Data Io

Spatial Transcriptomics Data I/O

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.

What it does

1. Takes your platform-specific spatial data directory (e.g., Space Ranger output) as input.
2. Identifies and parses the correct platform format (Visium, Xenium, MERFISH, Slide-seq, CosMx, Stereo-seq).
3. Reads the raw expression matrix (spot-level or single-cell resolution).
4. Automatically loads and scales spatial coordinates (x, y pixels or physical distances).
5. Ingests accompanying tissue images and spatial scale factors directly into the data object structure.
6. Returns a standardized AnnData or SpatialData object, binding expression data, shapes, and images together for immediate downstream analysis.

Why this exists

• Uses squidpy and spatialdata_io, the gold-standard parsers for spatial data.
• Automatically handles platform-specific quirks (e.g., Xenium single-cell vs. Visium spot resolution).
• Safely nests coordinates in obsm['spatial'] and images/scale factors in uns['spatial'] to ensure compatibility with all major spatial plotting tools.
• Provides future-proof integration by offering SpatialData (Zarr) representations for modern, multi-modal spatial projects.

Usage

"Load my Visium spatial data" → Read spatial transcriptomics outputs (Visium, Xenium, MERFISH, Slide-seq) into AnnData objects with spatial coordinates and tissue images.

• Python: squidpy.read.visium('spaceranger_out/'), spatialdata.read_zarr()

Load and work with spatial transcriptomics data from various platforms.

Required Imports

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

Goal: Load Visium spatial transcriptomics data from Space Ranger output into an AnnData object.

Approach: Use Squidpy's read.visium to parse the output directory, which loads expression, spatial coordinates, and tissue images.

# 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

Goal: Load Visium data using Scanpy's built-in reader as an alternative to Squidpy.

Approach: Use sc.read_visium to parse Space Ranger output, then access images and scale factors from adata.uns['spatial'].

# 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

Goal: Load single-cell resolution Xenium spatial data.

Approach: Use Squidpy's read.xenium to parse Xenium output, yielding per-cell expression and coordinates.

# 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)

Goal: Load spatial data into SpatialData objects for unified multi-modal representation.

Approach: Use spatialdata-io readers per platform, which organize expression, shapes, and images into a single object.

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

Goal: Load MERFISH (Vizgen MERSCOPE) spatial data.

Approach: Use spatialdata-io or Squidpy readers to parse MERSCOPE output with cell-by-gene counts and metadata.

# 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

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

Load Nanostring CosMx

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

Load Stereo-seq Data

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

Load from H5AD with Spatial Coordinates

# 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

Goal: Construct a spatial AnnData object from raw expression and coordinate arrays.

Approach: Build an AnnData with spatial coordinates in obsm['spatial'] and minimal metadata in uns['spatial'] for Squidpy compatibility.

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

# 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

# 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

Goal: Convert spatial data between SpatialData and AnnData representations.

Approach: Extract tables and coordinate arrays from SpatialData, then save as h5ad or zarr.

# 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

Goal: Load and merge spatial data from multiple Visium samples into a single AnnData.

Approach: Iterate over sample directories, tag each with a sample label, then concatenate with ad.concat.

# 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

Goal: Extract spots within a rectangular spatial region of interest.

Approach: Apply coordinate-based boolean masking on obsm['spatial'] to filter spots by x/y bounds.

# 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')

Example Output

Spatial Transcriptomics I/O
===========================
Input: path/to/spaceranger/output/
Platform detected: 10x Visium

Loaded AnnData Object:
  n_obs (spots): 2,987
  n_vars (genes): 31,053

Spatial Data Bindings:
  Coordinates: adata.obsm['spatial'] -> (2987, 2)
  Images: adata.uns['spatial']['library_id']['images'] -> ['hires', 'lowres']
  Scale factors: adata.uns['spatial']['library_id']['scalefactors']
    - tissue_hires_scalef: 0.150015
    - spot_diameter_fullres: 89.43

Subsetting check:
  Successfully filtered to bounding box (X: 1000-2000, Y: 1500-2500)
  Selected 412 spots in region of interest.

Data ready for Squidpy spatial statistics or Scanpy plotting.

Requirements

Inputs

Outputs

Citations

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/blob/main/skills/bio-spatial-transcriptomics-spatial-data-io