MintFlow Reproducibility

This repository contains the code to reproduce the analyses and benchmarking experiments performed in the MintFlow manuscript. The MintFlow source code can be found here.

This structure is designed for reviewer readability and to enable co-authors to contribute their analyses. See CONTRIBUTING.md for how to add your analysis.

Repository Structure

mintflow-reproducibility/
├── analysis/           # Reproducible analyses organized by figure/application
│   ├── figure2_3_eczema/    # Eczema scRNA-seq and drug2cell analyses
│   ├── figure4_melanoma/    # Melanoma spatial transcriptomics
│   └── kidney_cancer/       # Renal cell carcinoma (RCC) analyses
├── datasets/           # Dataset documentation and download info
├── envs/               # Conda environment specifications
├── utils/              # Shared utility functions
└── README.md

Installation

Standard

1. Clone the mintflow-reproducibility repository and navigate into it:

   git clone https://github.com/Lotfollahi-lab/mintflow-reproducibility.git
   cd mintflow-reproducibility

2. (Optional) Install the Libmamba solver to make the installation faster:

   conda update -n base conda
   conda install -n base conda-libmamba-solver
   conda config --set solver libmamba

3. Create the mintflow-reproducibility conda environment:

   conda env create -f envs/environment.yaml
   conda activate mintflow-reproducibility

4. Install MintFlow (if not already installed):

   pip install mintflow

   Or install from source: pip install git+https://github.com/Lotfollahi-lab/mintflow.git

R Analyses

The kidney cancer survival analysis (Fig6&S14_MintFlow_RCCanalysis_TGCASurvival.R) requires R. Install R and the required packages as indicated in the script.

Data & Models

Preprocessed data used in the manuscript and trained models are downloadable from GDrive (add link when available).

When running analyses, ensure data paths in notebooks match your local setup or the paths documented in each analysis folder.

Contributing Your Analysis

We welcome contributions from co-authors. To add your analysis:

1. Create a new subfolder under analysis/ (e.g., analysis/your_figure_name/)
2. Add a README.md describing the analysis and file purposes
3. Use relative paths or document data requirements
4. See CONTRIBUTING.md for the full guide

Reference

@article{Akbarnejad2025,
  author    = {Akbarnejad, A. et al.},
  title     = {Mapping and reprogramming microenvironment-induced cell states in human disease using generative AI},
  journal   = {bioRxiv},
  year      = {2025},
  doi       = {10.1101/2025.06.24.661094},
  url       = {https://doi.org/10.1101/2025.06.24.661094}
}