Tumor Inference of Causality (TIC) is a computational framework for analyzing cellular micro-environments using graph-based pseudo-time analysis. It integrates tools for graph construction, embedding preparation, pseudo-time trajectory computation, and biomarker trend visualization. The framework aims to facilitate both Pseudotime Ordering (left side of the figure below) and Causal Inference (right side), enabling comprehensive analyses of how cellular states evolve over time and how various factors may influence these trajectories. (Left) An overview of the pseudo-time analysis process: cells are embedded, clustered, and ordered along a trajectory to infer temporal progression. (Right) A conceptual outline for causal inference on the resulting pseudo-time data, allowing exploration of how various factors or biomarkers may causally relate to outcome variables.

To use TIC, follow these steps:

git clone https://github.com/cellethology/tic.git
cd tic

All dependencies can be installed in one command:

pip install -e .

If you prefer to install TIC in an isolated environment, you can use conda or venv:

conda create -n tic_env python=3.10 # min python version is 3.10
conda activate tic_env
pip install -e .

python -m venv tic_env
source tic_env/bin/activate  # On Windows: tic_env\Scripts\activate
pip install -e .

After installation, you can check if TIC is installed correctly:

python -c "import tic; print('TIC installed successfully!')"

see: Readme.md