ssc-python 0.1.0

Signaling Single Cell - Visualizations for single-cell RNA sequencing data

ssc: Signaling Single Cell - Visualizations

A Python package for creating publication-ready visualizations of single-cell RNA sequencing data.

Installation

Development Installation (Recommended during active development)

# Clone the repository
git clone https://github.com/yourusername/ssc.git
cd ssc

# Install in development mode
pip install -e .

PyPI Installation (Coming soon)

pip install ssc

Quick Start

import ssc
import scanpy as sc

# Load your AnnData object
adata = sc.read_h5ad('your_data.h5ad')

# Basic violin plot
fig = ssc.vlnplot(adata, 'GNLY', 'cell_type')

# Split violin with custom colors
fig = ssc.vlnplot(adata, 'GNLY', 'condition', 
                  split_by='treatment',
                  split_colors={'pre': 'lightblue', 'dupi': 'darkblue'})

# Faceted plot with custom ordering
fig = ssc.vlnplot(adata, 'GNLY', 'condition', 
                  facet_by='subject', facet_col='cell_type',
                  group_order=['Nonlesional', 'SADBE', 'Metal'])

Features

• Publication-ready plots: High-quality violin plots with comprehensive customization
• Split violin plots: Compare conditions within each group
• Faceted plots: Create subplot grids by multiple variables
• Custom colors: Dictionary-based color mapping for groups and splits
• R-style scaling: Dual y-axis with proper mean expression scaling
• Custom ordering: Control category order for all grouping variables
• Rich statistics: Cell counts, fraction expressing, and mean expression
• Layer support: Plot from any AnnData layer or raw counts

Dependencies

• pandas >= 1.3.0
• numpy >= 1.20.0
• matplotlib >= 3.5.0
• seaborn >= 0.11.0
• scanpy >= 1.8.0

Contributing

Contributions welcome! Please feel free to submit a Pull Request.

License

MIT License