Spatial Transcriptomics Spatial Data Io

• 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