SpatioloJI

core foundation for Ji Universe

SpatioloJI is a comprehensive Python library for analyzing spatial transcriptomics data. It provides a robust framework for managing, visualizing, and performing advanced spatial statistics on multi-FOV (Field of View) spatial transcriptomics datasets.

Overview

SpatioloJI offers an extensive suite of tools and functionalities specifically designed to address the challenges in spatial transcriptomics data analysis:

• Data Management: Organize cell polygons, gene expression, metadata, and images across multiple FOVs
• Quality Control: Comprehensive QC pipeline for filtering cells and genes
• Spatial Visualization: Advanced visualization tools for displaying cells, gene expression, and spatial relationships
• Spatial Statistics: Methods for detecting spatial patterns, correlations, and organization of cells and gene expression
• Network Analysis: Tools for building and analyzing cell interaction networks

Main Components

The library consists of three main components:

1. Quality Control (Spatioloji_qc Class): Tools for quality control and data preprocessing
2. Spatioloji Class (Spatial_Object.py): Core data structure for managing filtered spatial transcriptomics data
3. Spatial Analysis Functions (Spatial_function.py): Collection of statistical methods for spatial analysis
4. Spatial Visualization Functions (Plot_Spatial_Image.py): Functions for visualizing spatial relationships

Installation

conda create -n SpatioloJI python=3.12 -y
pip install spatioloji

Tutorials

Please check SpatioloJI Documentation for detailed instructions.

Spatial Stats Analysis Categories

SpatioloJI provides functions for spatial ststs in the following categories:

1. Neighbor Analysis

   • perform_neighbor_analysis: Comprehensive analysis of neighboring cells based on polygon geometries
   • calculate_nearest_neighbor_distances: Calculates distances to nearest neighbors for each cell
   • calculate_cell_density: Measures local cell density within a specified radius

2. Spatial Pattern Analysis

   • calculate_ripleys_k: Analyzes spatial point patterns using Ripley's K function
   • calculate_cross_k_function: Examines spatial relationships between different cell types
   • calculate_j_function: Uses Baddeley's J-function for spatial pattern analysis
   • calculate_g_function: Analyzes nearest neighbor distance distributions
   • calculate_pair_correlation_function: Measures correlations between cells at different distances

3. Cell Type Interaction Analysis

   • calculate_cell_type_correlation: Measures how different cell types correlate in space
   • calculate_colocation_quotient: Quantifies spatial relationships between cell types
   • calculate_proximity_analysis: Measures distances between specific cell types

4. Heterogeneity and Clustering

   • calculate_morisita_index: Measures the spatial distribution pattern (clustered vs. uniform)
   • calculate_quadrat_variance: Analyzes how variance changes with grid size
   • calculate_spatial_entropy: Quantifies randomness in spatial distribution
   • calculate_hotspot_analysis: Identifies statistically significant spatial hot/cold spots
   • calculate_spatial_autocorrelation: Measures Moran's I and related statistics
   • calculate_kernel_density: Creates density maps of cell distributions
   • calculate_spatial_heterogeneity: Quantifies and characterizes spatial variation

5. Network-Based Analysis

   • calculate_network_statistics: Creates and analyzes cell interaction networks
   • calculate_spatial_context: Analyzes cell neighborhoods and their composition

6. Gene Expression Spatial Analysis

   • calculate_gene_spatial_autocorrelation: Examines spatial patterns of gene expression
   • calculate_mark_correlation: Analyzes spatial correlation of cell attributes

Contributing

Contributions to SpatioloJI are welcome! Please feel free to submit a pull request or open an issue to discuss your ideas.

License

SpatioloJI is released under the MIT License.

Citation

If you use SpatioloJI in your research, please cite:

Citation information coming soon

Acknowledgments

SpatioloJI builds upon several established algorithms and methods for spatial analysis, and we thank the community for their contributions to this field.