This repository contains bioinformatics scripts and pipelines for analyzing oncology-related genomic data, with a focus on single-cell RNA sequencing (scRNA-seq) and whole-exome sequencing (WES) analyses.

• ScRNAseq/: Single-cell RNA sequencing analysis scripts

  • cluster_cells.py: Advanced cell clustering with quality assessment using scanpy
  • CNV.py: Copy number variation analysis using infercnvpy
  • crisper.py: CRISPR UMI data processing, normalization and guide RNA enrichment analysis
  • mast.R: Seurat-based scRNA-seq analysis using the MAST differential expression framework
  • ml_models.py: Machine learning models for treatment response prediction using scVI for latent representation
  • scanpy.py: Standard scanpy-based scRNA-seq analysis pipeline with quality control
  • seurat.R: Comprehensive Seurat-based workflow for cancer scRNA-seq with cell cycle analysis
  • spatial_analysis.py: Spatial transcriptomics processing and visualization using Scanpy/Visium

• WES/: Whole-exome sequencing analysis scripts

  • jwes.sh: Shell script for WES data processing

• Cancer genomics: Tools optimized for tumor heterogeneity and copy number analysis
• Single-cell analytics: Implementation of best practices for scRNA-seq quality control and clustering
• Treatment response: ML integration for predictive modeling from transcriptomic signatures
• Spatial analysis: Support for spatial transcriptomics in tumor microenvironment studies
• Reproducible workflows: Command-line interface with standardized parameters

• Languages: Python, R, Shell
• Core libraries:
  • Python: scanpy, scVI, infercnvpy, scikit-learn, PyTorch
  • R: Seurat, MAST, dplyr, ggplot2