Welcome to the exciting world of data analysis with PILOT!
PILOT is a Python library for Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport.
🚀 In these three comprehensive tutorials, we’ll guide you through a fascinating journey across diverse datasets. 📊✨ Uncover the intricate details of cellular behavior in Myocardial Infarction single cell data, explore the intricate landscape of Kidney IgAN(Glomeruli) & Kidney IgAN(Tubule) with pathomics data, and unravel the complexities of Patients sub-group detection while ranking cells and genes in Pancreas data. Whether you’re a beginner or an experienced data enthusiast, our step-by-step guides will empower you to harness the power of PILOT to derive insights and make impactful discoveries from these intricate datasets. Let’s dive in and unlock the hidden insights within the data together! 🧬🔍💡
Installation Guide
Follow these steps to install and set up PILOT:
conda create --name PILOT python=3.11.5 r-base
conda activate PILOT
pip install pilotpy
Once you’ve completed these steps, you can proceed to run the tutorials and explore the features of PILOT. When doing so, remember to move to the tutorial folder, as all the work will be performed there:
cd Tutorial
Citation
@article{joodaki2024detection,
title={Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)},
author={Joodaki, Mehdi and Shaigan, Mina and Parra, Victor and B{\"u}low, Roman D and Kuppe, Christoph and H{\"o}lscher, David L and Cheng, Mingbo and Nagai, James S and Goedertier, Micha{\"e}l and Bouteldja, Nassim and others},
journal={Molecular systems biology},
volume={20},
number={2},
pages={57--74},
year={2024},
publisher={Nature Publishing Group UK London}
}
scRNA-seq Analysis
Multimodal Integration
- Trajectory Analysis and Integration of Modalities using Kidney IgAN (Pathomics) Data with PILOT
- Kidney_IgAN Tubuli (first modality)
- Loading the required information and computing the Wasserstein distance:
- Kidney_IgAN Glomeruli (second modality)
- Integration of modalities:
- Fit a principal graph:
- Feature selection for Glomeruli based on Combination:
- Feature selection for Tubuli based on Combination
- Saving morphological features and map them with the obtained order by PILOT (for Tubuli):
Patients sub-group detection
- Patients sub-group detection by PILOT
- Reading Anndata
- Loading the required information and computing the Wasserstein distance:
- Ploting the Cost matrix and the Wasserstein distance:
- Trajectory:
- In this section, we should find the optimal number of clusters.
- Patients sub-group detection by clustering EMD.
- Cell-type selection.
- Differential expression analysis
- Pseudobulk differential expression analysis