Comprehensive assistance with OrganAxis computational approach for constructing Common Coordinate Frameworks (CCF) from spatial landmarks.
When to Use This Skill
This skill should be triggered when:
Spatial Analysis & CCF Development
Building Common Coordinate Frameworks for any organ or tissue
Mapping specimens to reference spaces using spatial landmarks
Performing multi-sample spatial integration while preserving continuous information
Developing spatial axes for specific anatomical structures (e.g., Cortico-Medullary Axis)
Tissue Annotation & Sampling
Need consistent tissue annotations across different spatial platforms
Working with spatial sampling resolution harmonization
Creating hexagonal point grids (HPG) for standardized spatial sampling
Annotating tissues at different resolutions (pixel-level vs. spot-level)
Related Skills
Platform-Specific Tasks
Analyzing Visium spatial transcriptomics data with 100µm spot resolution
Working with imaging technologies like IBEX at pixel resolution
Using TissueTag for interactive tissue annotation in Jupyter notebooks
Integrating data from multiple spatial omics platforms
Research Applications
Studying molecular gradients and intra-compartmental tissue differences
Modeling linear and non-linear spatial associations
Analyzing cellular neighborhoods and anatomical structures
Developing hypothesis-driven spatial frameworks
Quick Reference
Core OrganAxis Concepts
# Basic CCF Construction Principles
# OrganAxis enables mapping 2D tissues to Common Coordinate Frameworks
# Derived purely from reference images, universally applicable across platforms
# Reduces double-dipping risk by not using high-dimensional gene space
Spatial Sampling Setup
# Spatial Sampling Frequency Definition
# Define in μm or mm (e.g., spot every 2 μm)
# Independent of imaging resolution (pixel space)
# Must be consistent across all samples and modalities
Tissue Annotation Workflow
# Multi-Platform Annotation Strategy
# Visium: Use 10x Genomics Loupe Browser (100µm resolution)
# Imaging: Use Napari (pixel-level resolution)
# Jupyter: Use TissueTag for interactive annotation
# Cluster: Implement programmatic annotation solutions
Hexagonal Point Grid (HPG) Creation
# HPG for Cross-Platform Harmonization
# Create hexagonal grid with predetermined sampling frequency
# Map pixel-level annotations to grid points
# Balance between structure resolution and noise robustness
# Lower frequency = more robust but less detail
Spatial Resolution Trade-offs
# Resolution Selection Guidelines
# Higher spatial sampling: Discern fine structures, less noise robust
# Lower spatial sampling: Capture broad structures, more noise robust
# Match resolution to research question requirements
# Consider tissue variability and structure complexity
Multi-Sample Integration Strategy
# Diagonal Integration Approach
# Preserve continuous spatial information across samples
# Enable direct inter-sample comparisons
# Use landmark-based orientation with nonlinear transformations
# Account for specimen-to-specimen variability
Hypothesis-Driven Framework Development
# CCF Development Best Practices
# Requires prior knowledge of tissue biology
# Focus on biologically robust and meaningful axes
# Example: Human thymus Cortico-Medullary Axis (CMA)
# Validate framework with domain expertise
Key Concepts
Common Coordinate Framework (CCF)
A set of rules allowing researchers to map specimens to a reference space, enabling direct inter-sample comparisons and integration. Types include:
Anatomical coordinate systems: For stereotypical structures (e.g., embryos)
Landmark-based systems: Using morphological/histological or molecular features
Non-linear transformations: For systems with high variability
Spatial Sampling Frequency
Defined in μm or mm (e.g., spot every 2 μm), independent of imaging resolution. Critical for harmonizing across technologies and maintaining consistency across samples and modalities.
Hexagonal Point Grid (HPG)
A filter-like structure for mapping pixel-level annotations to a standardized spatial sampling frequency, balancing structure resolution with noise robustness.
Double-Dipping Prevention
OrganAxis approach is derived purely from reference images, not from high-dimensional gene space, significantly reducing the risk of circular analysis.
Cortico-Medullary Axis (CMA)
The specific application of OrganAxis to derive the human thymus spatial framework, demonstrating the approach's practical implementation.
Reference Files
getting_started.md
Pages: 3
Introduction: Comprehensive overview of CCF concepts and OrganAxis methodology
Common Coordinate Framework: Detailed explanation of CCF types and applications
Welcome: OrganAxis features and computational approach summary
Key Topics Covered:
Tissue compartment subdivision challenges
Inter-sample variability handling
Allen Mouse Brain Atlas example
Human brain mapping complexities
Multi-dimensional extension capabilities
tutorials.md
Pages: 1
Prerequisites for CFF establishment: Complete guide to tissue annotation and spatial sampling
Key Topics Covered:
Consistent tissue annotation strategies across platforms
Spatial sampling resolution definition and harmonization