panospace 0.1.1

High-resolution spatial transcriptomics analysis toolkit

PanoSpace

High-resolution single-cell insight from low-resolution spatial transcriptomics

PanoSpace bridges the gap between spot-based spatial transcriptomics (e.g., 10x Visium) and single-cell resolution. It combines histology-guided cell detection, transcriptomic deconvolution, deep-learning-based super-resolution, expression prediction, and microenvironment analysis to generate consistent cell-level maps across entire tissue sections.

📦 Installation

System Requirements

• OS: Linux (strongly recommended)
• GPU: NVIDIA GPU with CUDA support (strongly recommended for performance)
  • CUDA 12.1+ recommended
  • Minimum 8GB GPU memory

Installation

Option 1: Install from PyPI (Recommended for Users)

# Basic installation (lightweight, no PyTorch)
pip install panospace

# For cell detection functionality (includes PyTorch)
pip install panospace[cellvit]

# For cell annotation functionality (includes deep learning libraries)
pip install panospace[annotation]

# For microenvironment analysis (lightweight)
pip install panospace[microenv]

# For all functionality
pip install panospace[all]

Then set up conda environment for dependencies:

# Create a conda environment
conda create -n panospace python=3.11
conda activate panospace

# Install PyTorch manually (if using [cellvit] or [annotation])
# For GPU version:
pip install --extra-index-url https://download.pytorch.org/whl/cu121 torch>=2.1 torchvision>=0.15

# For CPU version:
pip install torch>=2.1 torchvision>=0.15

Option 2: Install from Source (Automatic Setup)

git clone https://github.com/hehuifeng/PanoSpace.git
cd PanoSpace
bash install.sh

The script will automatically:

• Create conda environment with all dependencies (except PyTorch)
• Detect your GPU
• Install PyTorch via pip (with CUDA if GPU detected)
• Install PanoSpace package
• Verify the installation

Option 3: Manual Installation from Source

For GPU version (recommended):

# Step 1: Create conda environment
conda env create -f environment-gpu.yml
conda activate PanoSpace

# Step 2: Install PyTorch with CUDA support
pip install --extra-index-url https://download.pytorch.org/whl/cu121 torch>=2.1 torchvision>=0.15

# Step 3: Install PanoSpace
pip install .

For CPU-only version:

# Step 1: Create conda environment
conda env create -f environment.yml
conda activate PanoSpace

# Step 2: Install PyTorch (CPU-only)
pip install torch>=2.1 torchvision>=0.15

# Step 3: Install PanoSpace
pip install .

Optional: Optimization Solvers (Click to expand)

Optimization Solvers for Cell Annotation

PanoSpace uses Mixed Integer Linear Programming (MILP) solvers for accurate cell-type annotation with spot-level quota constraints. Two solvers are supported:

Supported Solvers:

1. Gurobi (Recommended, Commercial but Free for Academia)

   • Significantly faster (10-100x speedup on large datasets)
   • Best for production use and large-scale analyses
   • Free academic license available at: https://www.gurobi.com/academia/academic-program-and-licenses/

2. SCIP (Open-Source, Default)

   • Automatically installed with PanoSpace
   • Produces mathematically identical results to Gurobi
   • Suitable for small to medium datasets
   • No additional setup required

Solver Selection Logic:

PanoSpace automatically selects the best available solver:

• If Gurobi is installed → Uses Gurobi (fastest)
• If Gurobi is not available → Uses SCIP (open-source fallback)

Both solvers implement the same mathematical model with:

• Global cell-type quotas
• Spot-level quota constraints (ensures consistency within each spot)
• Exact 0/1 assignment (no approximation)

Installation:

SCIP (installed by default):

# Already included in environment.yml
conda activate PanoSpace

Gurobi (optional, recommended for better performance):

# Install Gurobi
conda install -c conda-forge gurobipy

# Request free academic license at: https://www.gurobi.com/academia/academic-program-and-licenses/
# Follow Gurobi's instructions to activate the license

# Verify installation
python -c "import gurobipy; print('Gurobi installed successfully!')"

Note: Based on our experience, Gurobi typically solves problems in under 1 minute, while SCIP may take hundreds of minutes for the same problem.

🚀 Quick Start

Basic Workflow

import panospace as ps
from PIL import Image

# 1. Detect cells from tissue image
tissue = Image.open("path/to/visium_slide.tif")
seg_adata, contours = ps.detect_cells(tissue, model="cellvit", gpu=True)

# 2. Deconvolve Visium spots
#    visium_adata: AnnData with .X (expression) and .obsm['spatial'] (coordinates)
#    sc_reference: AnnData with .X and .obs[celltype_key] (cell type labels)
deconv_adata = ps.deconv_celltype(
    adata_vis=visium_adata,
    sc_adata=sc_reference,
    celltype_key="celltype_major",  # Column name in sc_reference.obs
    methods=['RCTD', 'spatialDWLS', 'cell2location']
)

# 3. Super-resolve to cell level
sr_adata = ps.superres_celltype(
    deconv_adata=deconv_adata,
    img_dir="path/to/visium_slide.tif"
)

# 4. Annotate segmented cells
annotated_adata = ps.celltype_annotator(
    decov_adata=visium_adata,
    sr_deconv_adata=sr_adata,
    seg_adata=seg_adata
)

# 5. Predict gene expression
pred_adata = ps.genexp_predictor(
    sc_adata=sc_reference,
    spot_adata=visium_adata,
    infered_adata=annotated_adata,
    celltype_list=list(sc_reference.obs["celltype_major"].unique())
)

Cell-Cell Interaction Analysis

# Analyze interactions between cell pairs
pairs = [('Cancer_epithelial', 'CAF'), ('T_cell', 'Macrophage')]
results = ps.analyze_interaction(
    adata=annotated_adata,
    cell_type_pairs=pairs,
    cell_type_col='pred_cell_type',  # Column in adata.obs
    radius=100.0  # Neighborhood radius (same units as spatial coordinates)
)

# Extract results and find correlated genes
expr_df, target_abundance, _ = results[('Cancer_epithelial', 'CAF')]
corr_results = ps.correlation_analysis(expr_df, target_abundance)
significant_genes = corr_results.query('p_adjust < 0.05')['gene'].tolist()

# Functional enrichment
if len(significant_genes) > 0:
    go_results = ps.spatial_enrichment(
        gene_list=significant_genes,
        organism='Human',
        gene_sets='GO_Biological_Process_2021'
    )

Data Requirements

Visium Data (visium_adata)

• AnnData object with .X (gene expression) and .obsm['spatial'] (coordinates)

Single-Cell Reference (sc_reference)

• AnnData object with .X and .obs[celltype_key] (cell type labels)
• Minimum 100 cells per type, genes should overlap with Visium data

Histology Image

• TIFF/PNG/JPEG format, 40x+ magnification recommended

📖 Citation

If you use PanoSpace in your research, please cite:

He, HF., Peng, P., Yang, ST. et al. Unlocking single-cell level and continuous whole-slide insights in spatial transcriptomics with PanoSpace. Nat Comput Sci (2026). https://doi.org/10.1038/s43588-025-00938-y

📧 Contact

For questions or collaboration opportunities:

• Hui-Feng He ([e-mail])
• Xiao-Fei Zhang ([e-mail])

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Note: PanoSpace is actively under development. API changes may occur between versions. Please check the changelog when upgrading.