Single-Cell RNA-Seq Analysis Framework

A comprehensive Python framework for single-cell RNA-Seq data analysis, providing streamlined workflows for preprocessing, clustering, differential expression, and trajectory analysis with support for multiple data formats and analysis methods.

Overview

The scRNA-Seq Analysis Framework simplifies complex single-cell analysis workflows by providing a unified interface for common tasks. It integrates popular tools like Scanpy and Seurat into easy-to-use Python functions with consistent APIs.

Key Features

• Multi-Format Support: Read 10x Genomics, Drop-seq, CEL-seq data
• Preprocessing: Quality control, normalization, feature selection
• Dimensionality Reduction: PCA, UMAP, t-SNE
• Clustering: Leiden, Louvain, K-means algorithms
• Differential Expression: Scanpy, Seurat, edgeR integration
• Trajectory Analysis: PAGA, Monocle, Slingshot
• Visualization: Publication-ready plots and interactive dashboards

Core Modules

1. Data Loading and Preprocessing

from scrnaseq import preprocessing

# Load data from various formats
adata = preprocessing.load_data(
    data_dir='data/10x_matrix',
    format='10x'
)

# Quality control
adata = preprocessing.quality_control(
    adata,
    min_genes=200,
    max_genes=5000,
    mt_threshold=10
)

# Normalization and scaling
adata = preprocessing.normalize_data(
    adata,
    method='log1p'
)

2. Dimensionality Reduction and Clustering

from scrnaseq import clustering

# Dimensionality reduction
adata = clustering.dimensionality_reduction(
    adata,
    n_pcs=50,
    n_neighbors=15
)

# Clustering
adata = clustering.cluster_cells(
    adata,
    resolution=0.8,
    algorithm='leiden'
)

# Visualization
clustering.plot_umap(adata, color='leiden')

3. Differential Expression Analysis

from scrnaseq import differential_expression

# Find marker genes
markers = differential_expression.find_markers(
    adata,
    groupby='leiden',
    method='wilcoxon'
)

# Compare conditions
de_results = differential_expression.compare_conditions(
    adata,
    groupby='condition',
    reference='control'
)

4. Trajectory Analysis

from scrnaseq import trajectory

# PAGA trajectory inference
adata = trajectory.paga_trajectory(
    adata,
    groups='leiden'
)

# Plot trajectory
trajectory.plot_paga(adata)

Installation

# Clone the repository
git clone https://github.com/tamoghnadas12/scRNA-seq-framework
cd scRNA-seq-framework

# Create conda environment
conda env create -f environment.yml
conda activate scrnaseq-framework

# Install package
pip install -e .

Usage Examples

Complete Analysis Workflow

import scrnaseq as sc

# Initialize analysis
analysis = sc.ScRNASeqAnalysis(
    data_dir='data/10x_matrix',
    output_dir='results'
)

# Run complete pipeline
analysis.preprocess_data()
analysis.cluster_cells(resolution=0.8)
analysis.find_markers()
analysis.run_trajectory_analysis()

# Generate report
analysis.generate_report()

Custom Analysis

# Load pre-processed data
adata = sc.read_h5ad('results/processed_data.h5ad')

# Custom clustering with different parameters
sc.tl.leiden(adata, resolution=1.2)
sc.pl.umap(adata, color=['leiden', 'cell_type'], ncols=2)

# Differential expression for specific clusters
de_genes = sc.get.rank_genes_groups_df(adata, group='0')

Technologies Integrated

• Core: Scanpy, Anndata
• Clustering: Leiden, Louvain, Scikit-learn
• Visualization: UMAP, t-SNE, Matplotlib, Seaborn
• Trajectory: PAGA, Palantir, Slingshot
• Differential Expression: Scanpy, Seurat, edgeR
• Data Formats: 10x Genomics, Drop-seq, CEL-seq

Interactive Demo

Check out our interactive demo to see the framework in action with real single-cell data.

Documentation

• API Reference
• Tutorials
• Best Practices

Contributing

We welcome contributions from the community! Please read our contributing guidelines for details on how to get involved.

License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.