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name: spatialdata description: SpatialData framework for handling spatial multi-modal and multi-scale data. Use this skill for questions about spatial omics data analysis, visualization, and the SpatialData Python ecosystem.

SpatialData Skill

Comprehensive assistance with SpatialData development, generated from official documentation.

When to Use This Skill

This skill should be triggered when:

Data Analysis & Visualization:

• Working with spatial omics data (images, labels, points, shapes)
• Analyzing multi-modal spatial datasets (transcriptomics, proteomics)
• Visualizing spatial data with napari or spatialdata-plot
• Performing spatial queries and data aggregation

Framework Implementation:

• Building spatial data pipelines with SpatialData objects
• Implementing coordinate transformations and spatial indexing
• Working with OME-NGFF/Zarr format storage
• Creating annotation tables with AnnData

Technical Operations:

• Loading/saving spatial data in various formats
• Querying spatial elements by regions of interest
• Performing SQL-like joins between spatial elements and tables
• Managing multi-scale image pyramids and chunked storage

Ecosystem Integration:

• Using spatialdata-io for technology-specific readers
• Integrating with napari-spatialdata for interactive visualization
• Working with spatialdata-plot for static plotting
• Processing data from technologies like Visium, Stereo-seq, MERFISH

Key Concepts

Core SpatialData Elements

• Images: Multi-dimensional arrays with channels (c), spatial dimensions (y, x, z), and coordinate transformations
• Labels: Segmentation masks as integer arrays where each value represents a spatial region/instance
• Points: Coordinate data for transcript locations, point clouds, or single-molecule data
• Shapes: Geometric regions (circles, polygons, multipolygons) for areas of interest
• Tables: AnnData objects storing annotations that can be linked to spatial elements

Essential Terminology

• Coordinate Systems: Reference frames for spatial alignment and transformations
• Spatial Elements: Building blocks (images, labels, points, shapes) with metadata
• OME-NGFF: Open Microscopy Environment Next-Generation File Format specification
• Zarr: Chunked storage format for large multi-dimensional arrays
• Regions of Interest (ROIs): Specific spatial subsets for analysis
• Multi-scale: Pyramidal image representations at different resolutions

Data Relationships

• Tables can annotate spatial elements but not other tables
• Images cannot be directly annotated by tables (no index)
• Spatial elements require coordinate systems and transformations
• Labels and Shapes are both types of "Regions"

Quick Reference

Basic Data Operations

1. Load and explore sample dataset

from spatialdata.datasets import blobs
sdata = blobs()
print(sdata)
# Shows all elements: images, labels, points, shapes, tables

2. Access element instances and properties

# Get unique label instances
from spatialdata import get_element_instances
instances = get_element_instances(sdata["blobs_multiscale_labels"])

# Get image channels
from spatialdata.models import get_channels
channels = get_channels(sdata["blobs_multiscale_image"])

3. Work with coordinate transformations

# Set image channels
from spatialdata.models import Image2DModel
sdata["blobs_image"] = Image2DModel.parse(
    sdata["blobs_image"],
    c_coords=["r", "g", "b"]
)

Annotation and Table Operations

4. Retrieve annotation values from elements

from spatialdata import get_values

# Get values from points element
genes = get_values(value_key="genes", element=sdata["blobs_points"])

# Get values from table annotations
channel_values = get_values(
    value_key="channel_0_sum",
    sdata=sdata,
    element_name="blobs_labels",
    table_name="table"
)

5. Create and manage annotation tables

from anndata import AnnData
from spatialdata.models import TableModel

# Create table without spatial annotation
codebook_table = AnnData(obs={
    "Gene": ["Gene1", "Gene2", "Gene3"],
    "barcode": ["03210", "23013", "30121"]
})
sdata["codebook"] = TableModel.parse(codebook_table)

# Create table annotating multiple elements
table = TableModel.parse(
    adata,
    region=["blobs_polygons", "blobs_points"],
    region_key="region",
    instance_key="instance_id"
)

Spatial Queries and Operations

6. Query data by spatial regions

from spatialdata import bounding_box_query, polygon_query

# Bounding box query
result = sdata.query.bounding_box(
    axes=("y", "x"),
    min_coordinate=[100, 100],
    max_coordinate=[200, 200],
    target_coordinate_system="global"
)

# Polygon query
from shapely.geometry import Polygon
polygon = Polygon([(0, 0), (10, 0), (10, 10), (0, 10)])
result = sdata.query.polygon(polygon, target_coordinate_system="global")

7. Join spatial elements with tables

from spatialdata import join_spatialelement_table

# SQL-like join between elements and annotation tables
element_dict, filtered_table = join_spatialelement_table(
    sdata=sdata,
    spatial_element_names="blobs_polygons",
    table_name="annotations",
    how="left",
    match_rows="left"
)

Data Model Validation

8. Parse and validate different data types

from spatialdata.models import (
    Image2DModel, Labels2DModel,
    PointsModel, ShapesModel
)

# Parse and validate 2D image
image = Image2DModel.parse(
    data=numpy_array,
    c_coords=["channel1", "channel2"],
    transformations={"global": Identity()}
)

# Parse points from DataFrame
points = PointsModel.parse(
    data=dataframe,
    coordinates={"x": "x_col", "y": "y_col"},
    feature_key="gene_id"
)

# Parse shapes from GeoDataFrame
shapes = ShapesModel.parse(geodataframe)

Reference Files

getting_started.md

Content: Installation guide and basic setup

• Use for: First-time installation, dependency management
• Key sections: PyPI/conda installation, ecosystem packages (spatialdata-io, spatialdata-plot, napari-spatialdata), Docker setup
• Examples: Installation commands for different environments

api.md (26 pages)

Content: Comprehensive API documentation

• Use for: Detailed function and class references
• Key sections:
  • Models: Image2DModel, Image3DModel, Labels2DModel, Labels3DModel, PointsModel, ShapesModel, TableModel
  • Operations: Spatial queries, bounding box operations, polygon queries
  • Transformations: Coordinate systems and transformations
  • Utils: Helper functions for data manipulation
• Examples: Function signatures and usage patterns

advanced.md (4 pages)

Content: Design document and advanced concepts

• Use for: Understanding framework architecture, design decisions
• Key sections: Goals and non-goals, element types, OME-NGFF compliance, coordinate systems
• Examples: Technical specifications and implementation details

io.md (1 page)

Content: Glossary and terminology

• Use for: Understanding spatial data terminology
• Key sections: Definitions of NGFF, OME, Zarr, ROI, spatial elements, vector/raster data
• Examples: Clear explanations of technical terms

other.md (6 pages)

Content: General information and links

• Use for: Project overview, citation information, ecosystem links
• Key sections: Main project description, related packages, contributing guidelines
• Examples: Links to documentation and resources

Working with This Skill

For Beginners

1. Start with getting_started.md to install SpatialData and dependencies
2. Use the Quick Reference examples to understand basic operations
3. Practice with the blobs dataset - it contains examples of all element types
4. Learn the terminology from io.md glossary

Beginner workflow:

# 1. Install and load
pip install spatialdata
from spatialdata.datasets import blobs

# 2. Explore the data structure
sdata = blobs()
print(sdata)

# 3. Access specific elements
points = sdata["blobs_points"]
labels = sdata["blobs_labels"]

# 4. Basic operations
from spatialdata import get_values
values = get_values("genes", element=points)

For Intermediate Users

1. Study api.md for detailed function references
2. Learn spatial queries - bounding_box_query and polygon_query
3. Work with annotations - understand table-element relationships
4. Explore coordinate transformations and multi-scale data

Intermediate workflow:

# 1. Create your own annotation tables
from spatialdata.models import TableModel
table = TableModel.parse(adata, region="my_labels")

# 2. Perform spatial queries
result = sdata.query.bounding_box(...)

# 3. Join data with annotations
from spatialdata import join_spatialelement_table
elements, table = join_spatialelement_table(...)

# 4. Work with coordinate systems
sdata.transform_to("global", "new_system")

For Advanced Users

1. Read advanced.md for architecture understanding
2. Implement custom data readers using spatialdata-io patterns
3. Optimize performance with chunking and lazy loading
4. Contribute to the ecosystem - understanding OME-NGFF compliance

Advanced workflow:

# 1. Create custom elements with specific transformations
from spatialdata.models import Image2DModel
image = Image2DModel.parse(
    data,
    transformations={"custom": Translation([100, 100])}
)

# 2. Optimize large datasets
# Use chunking, multi-scale representations
# Implement custom spatial indexes

# 3. Extend the framework
# Create custom models, readers, or visualization tools

Navigation Tips

• For installation issues: Check getting_started.md for dependency conflicts
• For API questions: Search api.md for specific function names
• For conceptual understanding: Start with io.md terminology, then read advanced.md
• For practical examples: Use the Quick Reference section with real code patterns
• For ecosystem integration: Look for spatialdata-io, spatialdata-plot, napari-spatialdata references

Resources

Ecosystem Packages

• spatialdata-io: Technology-specific readers and converters
• spatialdata-plot: Static plotting and visualization
• napari-spatialdata: Interactive napari plugin
• spatialdata-sandbox: Example datasets and converters

Storage Formats

• Zarr: Chunked storage for large arrays
• OME-NGFF: Standard for bioimaging data
• Parquet: Columnar storage for tabular data

Community

• scverse project: Governance and contribution guidelines
• GitHub repository: Source code and issue tracking
• Documentation: Comprehensive guides and tutorials

Notes

• SpatialData uses standard scientific Python libraries (xarray, AnnData, geopandas)
• All spatial elements require coordinate systems and transformations
• Tables use AnnData format and can annotate multiple spatial elements
• The framework emphasizes lazy loading and chunked storage for performance
• Compatible with OME-NGFF specification for interoperability

Updating

To refresh this skill with updated documentation:

1. Re-run the scraper with the same configuration
2. The skill will be rebuilt with the latest information from the SpatialData documentation