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CytoBulk: Results Reproduction & Visualization Tutorials

This repository contains comprehensive installation guides, result reproduction tutorials, and visualization notebooks for the CytoBulk.

Overview

CytoBulk is a method for deconvolving bulk transcriptomics data and mapping H&E histology images using single-cell reference data. This repository provides reproducible workflows and tutorials for:

  • Installation (Conda or Docker)
  • Result Reproduction (multiple datasets and analysis types)
  • Data Visualization (Jupyter notebooks)

📁 Repository Structure

1. Installation (install/)

Start here to set up CytoBulk in your environment:

  • conda_install.md — Installation guide for Conda environment

    • Clone CytoBulk repository
    • Create Conda environment from environment.yml
    • Install Giotto in R (required for marker detection)
    • Verify installation

  • docker_install.md — Installation guide for Docker

    • Check Docker prerequisites
    • Pull CytoBulk Docker image
    • Verify image availability

2. Result Reproduction (run_case/)

Contains detailed tutorials for reproducing CytoBulk results on various datasets. Choose between Conda or Docker environments:

A. Conda Tutorials (conda_run_case/)

Markdown documentation for tutorials (each includes instructions to run accompanying Python scripts):

Bulk RNA-seq deconvolution and cell mapping:

  • bulk_deconv_*.md — Bulk deconvolution tutorials (12_simulation, BRCA, Flu_sdy67, human_bulk, TCGA, HGSOC, etc.)

H&E image → scRNA mapping:

  • he_mapping.md — General H&E mapping tutorial
  • HE_mapping_*.md — H&E mapping tutorials for specific datasets (CID867, TCGA-37-4132, etc.)

Spatial transcriptomics (ST) deconvolution:

  • st_deconv_*.md — ST deconvolution tutorials (10x_BRCA, ER2, merfish, mouse_mob, pdac, seqfishplus, TNBC, etc.)

ST reconstruction:

  • st_10x_mapping.md — ST cell type mapping (maps single cells to spatial locations)

B. Docker Tutorials (docker_run_case/)

Docker-compatible documentation for running the same analyses:

Bulk deconvolution:

  • bulk_deconv_*.md — Docker versions of bulk deconvolution tutorials

H&E mapping:

  • HE_mapping_*.md — H&E mapping tutorials for specific datasets (CID867, TCGA-37-4132, etc.)

ST deconvolution:

  • st_deconv_*.md — ST deconvolution tutorials (10x_BRCA, ER2, merfish, mouse_mob, pdac, seqfishplus, TNBC, etc.)

3. Visualization (visualization/)

Interactive Jupyter notebooks for exploring and visualizing CytoBulk results:

Important: Required packages for visualization

Before running Jupyter notebooks, ensure the following packages are installed in your environment:

pip install pandas numpy scanpy matplotlib seaborn scipy scikit-learn

Key packages:

  • scanpy — Single-cell analysis toolkit
  • pandas — Data manipulation
  • numpy — Numerical computing
  • matplotlib & seaborn — Data visualization
  • scipy — Statistical tests (Mann-Whitney U, Pearson correlation, etc.)
  • scikit-learn — Machine learning metrics (MSE, mean absolute error, etc.)

Jupyter notebooks:

  • bulk_visualization.ipynb — Bulk RNA-seq deconvolution results

    • Visualize deconvolved cell-type fractions
    • Compare with ground truth
    • Statistical analysis and plots

  • he_visualization.ipynb — H&E mapping results

    • Compare CytoBulk vs. expression-only methods
    • Gene-wise correlation and MSE metrics
    • Statistical significance testing

  • st_visualization.ipynb — Spatial transcriptomics deconvolution results

    • Spatial distribution of cell types
    • Cell-type colocalization patterns
    • Comparative analysis across methods