Envi Pkg Local

Envi-Pkg-Local Skill

Comprehensive assistance with ENVI (Environmental Niche Integration) for spatial transcriptomics and single-cell RNA sequencing data integration.

When to Use This Skill

This skill should be triggered when:

Core ENVI Tasks:

• Setting up ENVI models for spatial and scRNA-seq data integration
• Computing COVET (Cellular Niche Covariance) matrices
• Training ENVI variational autoencoders
• Performing gene imputation for spatial data
• Predicting spatial context for dissociated single cells

Data Analysis Workflows:

• Analyzing cellular niches and microenvironments
• Predicting cortical depth or spatial gradients
• Integrating MERFISH, Visium, or other spatial data with scRNA-seq
• Cell type niche composition analysis
• Spatially-aware dimensionality reduction

Technical Implementation:

• Configuring ENVI model parameters (latent dimensions, distributions)
• Handling different data distributions (Poisson, Negative Binomial, ZINB, Normal)
• Optimizing COVET computation with different gene sets
• Troubleshooting ENVI training and convergence

Visualization and Downstream Analysis:

• Creating UMAP embeddings of ENVI latent space
• Running diffusion maps on COVET matrices
• Force-directed layout visualization
• Spatial plotting of niche predictions

Quick Reference

Basic Setup and Installation

Example 1 (python):

# Environment setup for GPU/CPU usage
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # Change to -1 for CPU
import warnings
warnings.filterwarnings('ignore')

Example 2 (python):

# Install and import ENVI
!pip install scenvi
import scenvi

Data Loading and Preparation

Example 3 (python):

# Load spatial and single-cell data
import scanpy as sc
st_data = sc.read_h5ad('st_data.h5ad')  # Spatial transcriptomics
sc_data = sc.read_h5ad('sc_data.h5ad')  # Single-cell RNA-seq

Example 4 (python):

# Prepare single-cell data with highly variable genes
sc_data.layers['log'] = np.log(sc_data.X + 1)
sc.pp.highly_variable_genes(sc_data, layer='log', n_top_genes=2048)

ENVI Model Initialization and Training

Example 5 (python):

# Initialize ENVI model with default parameters
envi_model = scenvi.ENVI(
    spatial_data=st_data,
    sc_data=sc_data,
    spatial_key='spatial',
    covet_batch_size=256
)

Example 6 (python):

# Train the model and run inference
envi_model.train()
envi_model.impute_genes()
envi_model.infer_niche_covet()
envi_model.infer_niche_celltype()

Data Integration and Visualization

Example 7 (python):

# Extract ENVI results and create joint UMAP
st_data.obsm['envi_latent'] = envi_model.spatial_data.obsm['envi_latent']
sc_data.obsm['envi_latent'] = envi_model.sc_data.obsm['envi_latent']

fit = umap.UMAP(n_neighbors=100, min_dist=0.3, n_components=2)
latent_umap = fit.fit_transform(
    np.concatenate([st_data.obsm['envi_latent'], sc_data.obsm['envi_latent']])
)

Example 8 (python):

# Advanced analysis: Diffusion maps on COVET matrices
def run_diffusion_maps(data_df, n_components=10, knn=30, alpha=0):
    """Run diffusion maps using adaptive anisotropic kernel"""
    # Implementation for niche trajectory analysis
    return diffusion_components

DC_COVET = run_diffusion_maps(
    np.concatenate([
        st_data.obsm['COVET_SQRT'].reshape([st_data.shape[0], -1]),
        sc_data.obsm['COVET_SQRT'].reshape([sc_data.shape[0], -1])
    ])
)

Key Concepts

ENVI (Environmental Niche Integration): A deep learning framework that integrates spatial transcriptomics data with dissociated single-cell RNA sequencing data using a conditional variational autoencoder (CVAE).

COVET (Cellular Niche Covariance): A method that quantifies cellular microenvironments by computing gene-gene covariance matrices for each cell based on its spatial neighbors.

Latent Space Integration: ENVI learns a shared latent representation where spatial and single-cell cells co-embed, enabling cross-modal inference.

Niche Cell Type Composition: Predictions of the proportion of different cell types in each cell's local microenvironment.

Gene Imputation: Prediction of missing gene expression values in spatial data using information learned from single-cell data.

Reference Files

This skill includes comprehensive documentation in references/:

code.md - Core Implementation

• ENVI class definition (lines 334-472): Complete constructor with all parameters
• Distribution functions (lines 122-181): KL divergence, log probability calculations
• Gene preparation methods (lines 482-548): Data preprocessing and HVG selection
• Configuration file (lines 193-296): Sphinx documentation setup

docs.md - Tutorial Documentation

• Complete workflow tutorial (lines 564-1008): From installation to analysis
• Visualization examples (lines 854-994): Spatial plots, UMAPs, embeddings
• Analysis functions (lines 666-808): Force-directed layouts, diffusion maps
• Code patterns (lines 621-664): Imports, utility functions, data loading

tutorials.md - Interactive Notebook

• Step-by-step implementation (lines 1016-1497): Complete analysis pipeline
• Advanced niche analysis (lines 1354-1426): Diffusion components, trajectory analysis
• Practical examples (lines 1070-1184): Real data analysis with MERFISH
• Visualization workflows (lines 1304-1327): Publication-ready plots

Working with This Skill

For Beginners

1. Start with data preparation: Ensure your spatial data has coordinates in obsm['spatial'] and both datasets have matching gene names
2. Use default parameters: The ENVI constructor works well with defaults for most datasets
3. Follow the tutorial workflow: Load data → Initialize → Train → Extract results → Visualize

For Intermediate Users

1. Optimize COVET computation: Adjust num_cov_genes and k_nearest for your tissue type
2. Choose appropriate distributions: Use spatial_dist='pois' for count data, sc_dist='nb' for single-cell
3. Customize gene selection: Provide specific genes with cov_genes or sc_genes parameters

For Advanced Users

1. Fine-tune model architecture: Adjust num_layers, num_neurons, and latent_dim for complex datasets
2. Optimize training coefficients: Balance loss terms with spatial_coeff, sc_coeff, cov_coeff, kl_coeff
3. Custom COVET calculation: Use covet_use_obsm or covet_use_layer for alternative data representations

Navigation Tips

• For implementation details: Check code.md for the ENVI class constructor and utility functions
• For complete workflows: Follow docs.md for step-by-step tutorials with real data
• For interactive analysis: Use tutorials.md for notebook-style exploration and advanced visualizations
• Parameter reference: All constructor parameters are documented in the ENVI class (code.md:334-472)

Common Workflows

Spatial Integration: tutorials.md:1188-1215 shows complete ENVI setup and training Niche Analysis: docs.md:666-808 provides COVET analysis and diffusion maps Visualization: tutorials.md:1304-1327 demonstrates publication-ready plotting

Resources

references/

Comprehensive documentation containing:

• Implementation details: Complete source code with annotations
• Step-by-step tutorials: Real data analysis workflows
• Visualization templates: Publication-quality plotting examples
• Parameter guides: Detailed configuration options

scripts/

Add helper scripts for:

• Data preprocessing pipelines
• Batch ENVI analysis
• Custom visualization functions
• Quality control metrics

assets/

Include:

• Example datasets in correct format
• Color palettes for cell types
• Template notebooks
• Configuration files

Notes

• ENVI requires paired spatial and single-cell data from the same tissue
• COVET computation is the most time-consuming step; adjust covet_batch_size based on memory
• GPU acceleration significantly speeds up training; set CUDA_VISIBLE_DEVICES appropriately
• The model automatically handles gene overlap between datasets

Updating

To refresh this skill with updated documentation:

1. Re-run the documentation scraper with the same configuration
2. Update reference files with new API changes
3. Verify code examples work with latest ENVI version