OTMODE 0.1.0

OTMODE is a computational framework built on Optimal Transport (OT) theory for improving cell-type annotation accuracy and for identifying differential features across conditions in single-cell multi-omics data.

OTMODE: Optimal Transport-Based Framework for Differential Feature Identification in Single-Cell Multi-Omics

---

🧬 Overview

OTMODE is a computational framework built on Optimal Transport (OT) theory for improving cell-type annotation accuracy and for identifying differential features across conditions in single-cell multi-omics data.

It provides interpretable metrics, including Sinkhorn distances, to compare cell types and clusters across predicted and true annotations. Please see our architecture of the OTMODE framework for more details!

To optimize the performance of OTMODE in differential feature detection, we evaluated its behavior under various parameter settings. Our results showed that parameter values between 0.1 and 1.0 generally yielded the highest performance.

🔬 Built with Python

---

📘 Tutorials

We provide interactive tutorials to help users get started with applying OTMODE to multi-omics single-cell data. These tutorials demonstrate real use cases such as cell type annotation and differential feature detection.

All tutorials are provided as Jupyter notebooks and are located in the tutorials/ folder.

🧪 Available Tutorials

Tutorial	Description	Link
Application 1: Cell Type Annotation	Learn how to use OTMODE to annotate cell types using integrated multi-omics data.	View Notebook ›
Application 2: Differential Feature Detection	Step-by-step guide to detect differentially expressed genes or features across cell groups.	View Notebook ›

---

📝 Notes for Users

Each tutorial is fully commented and includes the following steps:

• 📂 Data Loading and Preparation
  Load example or user-provided multi-omics data and prepare it for analysis.

• ⚙️ Application of the OTMODE Algorithm
  Apply the OTMODE method to integrate modalities and infer latent representations.

• 📊 Visualization of Results
  Generate plots such as UMAP, heatmaps, and feature importance to interpret results.

• 🔍 Interpretation of Outputs
  Understand clustering, annotations, or differential features identified by OTMODE.

---

✅ Recommended Environment

• Python ≥ 3.8
• Libraries: scanpy, anndata, numpy, matplotlib, seaborn, and others listed in requirements.txt

---