scAnalyzer: A Single-Cell Analysis Toolkit

A Python toolkit for single-cell RNA sequencing (scRNA-seq) analysis.

🚧 Warning this project is under heavy development and not ready for production. ABI changes can happen frequently until reach stable version 🚧

pip install scAnalysis

🚀 Features

• Core Data Structure: SingleCellDataset (AnnData-like) for efficient handling of sparse matrices and metadata.
• Preprocessing: QC metrics, filtering (cells/genes), normalization, log-transformation, and highly variable gene (HVG) selection.
• Dimensionality Reduction: PCA, t-SNE, and UMAP implementations.
• Clustering: Graph-based (Leiden, Louvain), geometric (K-Means, Hierarchical), and density-based (DBSCAN) clustering.
• Differential Expression: Statistical testing (T-test, Wilcoxon) to identify marker genes.
• Visualization: Publication-ready plots (UMAP, t-SNE, Violin, Dotplot, Heatmap).
• I/O: Support for 10x Genomics (.mtx), H5AD (.h5ad), and CSV formats.

📦 Installation

Clone the repository and install the required dependencies:

git clone [https://github.com/demirbasayyuce/scAnalyzer.git](https://github.com/demirbasayyuce/scAnalyzer.git)
cd sc_analysis
pip install -r requirements.txt

## ⚡ Quick Start

Here is a minimal example of how to run a full analysis pipeline:

```python
import sc_io as io
import preprocessing as pp
import dimensionality as dim
import clustering as cl
import visualization as vis

# 1. Load Data
data = io.read_10x_mtx('./data/pbmc3k/')

# 2. Preprocess
pp.filter_cells(data, min_genes=200, max_pct_mito=5.0)
pp.normalize_total(data)
pp.log1p(data)
pp.highly_variable_genes(data, n_top_genes=2000)
pp.scale(data)

# 3. Embed & Cluster
dim.run_pca(data)
dim.neighbors(data)
dim.run_umap(data)
cl.cluster_leiden(data, resolution=0.5)

# 4. Visualize
vis.plot_umap(data, color='leiden', save='umap_clusters.png')

📂 Project Structure

• core.py: Main data structure (SingleCellDataset).
• preprocessing.py: Filtering, normalization, and scaling functions.
• dimensionality.py: PCA, Neighborhood Graph, t-SNE, UMAP.
• clustering.py: Community detection algorithms.
• differential.py: Marker gene identification.
• visualization.py: Plotting functions.
• sc_io.py: Input/Output handlers.
• utils.py: Helpers for merging and subsampling.

🧪 Running Tests

The project includes a comprehensive suite of unit tests. Run them using:

python -m unittest discover test

📄 License

MIT License.