Dissecting AXL-mediated signaling and resistance

The project investigates how the AXL receptor tyrosine kinase drives resistance to EGFR-targeted therapies in lung cancer. It employs a systems biology approach, utilizing Dual Data-Motif Clustering (DDMC) to integrate phosphoproteomic abundance and sequence information, and Partial Least Squares Regression (PLSR) to link signaling clusters to phenotypic outcomes (survival, migration, and spatial clustering).

Installation

This project uses uv for dependency management, and git-lfs for large data files.

# 1. Install git-lfs if not already installed (https://git-lfs.com)
git lfs install

# 2. Clone the repository and pull LFS files
git clone https://github.com/meyer-lab/AXLomics.git
cd AXLomics
git lfs pull

# 3. Install dependencies
uv sync

Usage

Reproducing Figures

Run any of the Jupyter notebooks (Figure1.ipynb–FigureS2_motifs.ipynb) directly in VS Code using the .venv kernel created by uv sync, or execute all at once from the repo root:

make notebooks

Generated figure files (SVG, PDF, PNG) are written to output/ and computed results to msresist/results/.

External Datasets

Figure3C-J requires the Maynard et al. single-cell RNA-seq dataset (PMID: 32822576, DOI: 10.1016/j.cell.2020.07.017). Generate an h5ad file using the study's deposited raw data and place it at:

msresist/data/Maynard/8091e3d90a9045a181b2fc11000c0dd9_PMID32822576.h5ad

The accompanying cancer_cell_annotation.csv (inferCNV-derived cancer cell annotations) is included in the repository.

Running Analysis

To run the supplemental analysis regarding AXL receptor dosage bias:

uv run python AXLdosage_bias/scripts/run_analysis.py

Testing and Linting

make test  # Run unit tests
make lint  # Run Ruff linter

Repository Structure

• msresist/: Core Python package containing:
  • clustering.py: Implementation of DDMC (Gaussian Mixture Model variant).
  • pca.py, plsr.py: Dimensionality reduction and regression modeling.
  • distances.py: Implementation of Ripley's K function for cell island analysis.
  • data/: Raw and processed datasets (Mass Spec, phenotypic assays).
• AXLdosage_bias/: Scripts and figures for AXL dosage sensitivity analysis.
• Figure*.ipynb: Notebooks for main and supplemental figure generation.

Authors

• Marc Creixell (creixell.marc@gmail.com)
• Aaron S. Meyer (git@ameyer.me)