Xenium Benchmarking Docs Local

Xenium-Benchmarking-Docs-Local Skill

Comprehensive assistance with Xenium spatial transcriptomics benchmarking, generated from official documentation.

When to Use This Skill

This skill should be triggered when:

• Working with Xenium spatial transcriptomics data analysis
• Processing Xenium machine output files
• Performing spatial domain identification using Banksy, NBD, or RBD
• Analyzing neighborhood enrichment and spatially variable genes
• Benchmarking Xenium data against scRNA-seq references
• Implementing quality control and filtering for spatial transcriptomics
• Calculating cell density and spatial metrics
• Performing coexpression analysis in spatial data
• Using Baysor for alternative segmentation

Quick Reference

Common Patterns

Basic Data Processing

# Import required modules
import os
import numpy as np
import pandas as pd
import scanpy as sc
from xb.formatting import *
from xb.plotting import *
from xb.preprocessing import *
from xb.Spage_main import *
from xb.calculating import *

# Format Xenium output to AnnData
files=['./data/output-XETG00047__0011146__1886C__20231102__180733',
       './data/output-XETG00047__0011146__1886P__20231102__180733']
output_path=r'../pipeline_output/'
max_nucleus_distance=10
min_quality=0

adata=format_to_adata(files=files,
                      output_path=output_path,
                      max_nucleus_distance=max_nucleus_distance,
                      min_quality=min_quality,
                      save=True)

Preprocessing and Clustering

# Define clustering parameters
clustering_params={
    'normalization_target_sum':100,
    'min_counts_x_cell':40,
    'min_genes_x_cell':15,
    'scale':False,
    'clustering_alg':'louvain',
    'resolutions':[0.2,0.5,1.1],
    'n_neighbors':15,
    'umap_min_dist':0.1,
    'n_pcs':0
}

# Read and preprocess data
adata=sc.read(output_path+'combined_filtered.h5ad')
adata=preprocess_adata(adata, save=True,
                       clustering_params=clustering_params,
                       output_path=output_path)

Domain Identification with Banksy

# Banksy parameters for spatial domain identification
banksy_params={
    'resolutions':[.9],
    'pca_dims':[20],
    'lambda_list':[.8],
    'k_geom':15,
    'max_m':1,
    'nbr_weight_decay':"scaled_gaussian",
    'cluster_algorithm':'leiden'
}

# Run Banksy domain identification
adata, adata_banksy = domains_by_banksy(adata,
                                       plot_path=plot_path,
                                       banksy_params=banksy_params)

Alternative Segmentation with Baysor

# Prepare Xenium data for Baysor
prep_xenium_data_for_baysor(files[0], output_path,
                           CROP=True,
                           COORDS=[1000, 5000, 1000, 5000])

# Run Baysor using Docker (command line)
!docker pull louisk92/txsim_baysor:v0.6.2bin
!sudo docker run -it --rm -v /path/to/baysor/input:/data \
   -v /path/to/xb/module:/module louisk92/txsim_baysor:v0.6.2bin
!cd /path/to/xb && bash run_baysor.sh "/data"

# Format Baysor output to AnnData
path='/path/to/baysor/output'
adata=format_baysor_output_to_adata(path, output_path)

Spatial Analysis

# Calculate neighborhood enrichment
adata1 = neighborhood_enrichment(adata,
                                sample_key='sample',
                                radius=50,
                                cluster_key='cell_type')

# Identify spatially variable genes
adata1 = spatially_variable_features(adata1,
                                    sample_key='sample',
                                    radius=50)

# Generate spatial plots
spatial_plot(adata, key='cell_type', clusters='all',
           size=10, background='white',
           figuresize=(10, 8), save=True)

Quality Metrics

# Calculate cell density
cell_density_value = cell_density(adata_sp, pipeline_output=True)

# Calculate negative marker purity
purity_score = negative_marker_purity(adata_sp, adata_sc,
                                    key='cell_type',
                                    pipeline_output=True)

# Compute clustering comparisons
fmi = fowlkes_mallows_index(ground_truth, predicted)
nmi = normalized_mutual_info_score(ground_truth, predicted)
vi = variation_of_information(ground_truth, predicted)

Key Concepts

Xenium Spatial Transcriptomics

• Xenium Platform: In situ sequencing technology for spatial gene expression profiling
• Spatial Resolution: Subcellular localization of transcript molecules
• Quality Metrics: Transcript quality scores and distance to nucleus filtering

Analysis Modules

• xb.formatting: Raw data processing and AnnData conversion
• xb.preprocessing: Normalization, clustering, and spatial preprocessing
• xb.domain_identification: Spatial domain identification (Banksy, NBD, RBD)
• xb.plotting: Spatial visualization and plotting functions
• xb.calculating: Metrics, distances, and quality assessments
• xb.neighborhood: Cell-cell neighborhood analysis
• xb.comparing: Benchmarking and comparison metrics

Domain Identification Methods

• Banksy: Incorporates neighboring cell information into clustering (preferred)
• NBD (Neighbors-based domains): Uses neighboring cell type composition
• RBD (Read-based domains): Collapses expression from neighboring cells

Reference Files

This skill includes comprehensive documentation in references/:

• main_documentation.md - Core module API documentation

  • xb package structure (12 modules)
  • Function parameters and returns
  • Usage examples for each function

• pipeline.md - End-to-end workflow documentation

  • Complete 7-step analysis pipeline
  • Parameter configuration examples
  • Docker setup for Baysor integration
  • Best practices and troubleshooting tips

Working with This Skill

For Beginners

1. Start with Data Formatting: Use format_to_adata() to process Xenium outputs
2. Quality Control: Set appropriate max_nucleus_distance and min_quality filters
3. Basic Clustering: Follow the preprocessing pipeline with default parameters
4. Visualization: Use spatial_plot() to explore cell type distributions

For Intermediate Users

1. Domain Identification: Implement Banksy for spatial domain discovery
2. Parameter Tuning: Adjust Banksy lambda_list and geometric parameters
3. Quality Metrics: Calculate cell density and negative marker purity
4. Comparative Analysis: Fowlkes-Mallows index for cluster comparison

For Advanced Users

1. Alternative Segmentation: Integrate Baysor for refined cell boundaries
2. Spatially Variable Genes: Moran's I analysis for spatial gene patterns
3. Neighborhood Enrichment: Cell-type interaction analysis
4. Benchmarking: Compare with scRNA-seq reference datasets
5. Custom Workflows: Combine modules for specialized analyses

Navigation Tips

• Use the pipeline.md as a step-by-step guide for complete analyses
• Refer to main_documentation.md for detailed function parameters
• Start with small datasets to optimize parameters before scaling
• Check computational requirements when processing multiple samples
• Verify Docker is installed for Baysor integration

Resources

references/

• main_documentation.md: Complete API reference for all xb modules
• pipeline.md: Full workflow from raw Xenium data to final results

scripts/

Add helper scripts for:

• Batch processing of multiple samples
• Automated parameter optimization
• Quality control report generation
• Custom spatial analysis workflows

assets/

Add example data for:

• Test datasets to validate workflows
• Configuration templates
• Visualization examples
• Troubleshooting test cases

Performance Tips

Memory Management

• Use parquet files with use_parquet=True for faster loading
• Apply strict quality filters for large datasets
• Consider cropping regions of interest (ROI) for debugging

Computational Efficiency

• Adjust rate_limit parameters based on system resources
• Use clustering parameters appropriate for data size
• Test with subset of cells before full analysis

Scaling to Multiple Samples

• Use batch functions (batch_prep_xenium_data_for_baysor) for multiple samples
• Standardize coordinate systems with modify_coords_for_banksy
• Implement consistent naming conventions across samples

Troubleshooting

Common Issues

• Memory Errors: Reduce dataset size or use subset of genes
• Coordinate Problems: Verify spatial coordinates are in micrometers
• Import Errors: Ensure all dependencies are installed (pip install xb)
• Docker Issues: Check Docker permissions and memory allocation

Quality Control

• Monitor transcript quality distributions
• Validate spatial coordinate ranges
• Check cell-type annotation consistency
• Verify neighbor graph connectivity

Updating

To refresh this skill with updated documentation:

1. Re-run the local documentation scraper
2. Update xenium-benchmarking-docs-local module if API changes occurred
3. Test with new Xenium software versions
4. Update example datasets and configurations