# Ocelli: an open-source tool for the analysis and visualization of developmental multimodal single-cell data

## TL;DR

Ocelli is a Python tool that helps analyze and visualize complex single-cell data from developmental biology.

## Contribution

Ocelli introduces a scalable and efficient method for analyzing multimodal single-cell data using diffusion-based modeling.

## Key findings

- Ocelli provides visualization of cell states while preserving developmental progression continuity.
- The tool outperforms existing methods in computational speed and representation quality.
- Ocelli supports integration with trajectory inference and imputation of undetected features.

## Abstract

The recent expansion of single-cell technologies has enabled simultaneous genome-wide measurements of multiple modalities in the same single cell. The potential to jointly profile such modalities as gene expression, chromatin accessibility, protein epitopes, or multiple histone modifications at single-cell resolution represents a compelling opportunity to study developmental processes at multiple layers of gene regulation. Here, we present Ocelli, a lightweight Python package implemented in Ray for scalable visualization and analysis of developmental multimodal single-cell data. The core functionality of Ocelli focuses on diffusion-based modeling of biological processes involving cell state transitions. Ocelli addresses common tasks in single-cell data analysis, such as visualization of cells on a low-dimensional embedding that preserves the continuity of the developmental progression of cells, identification of rare and transient cell states, integration with trajectory inference algorithms, and imputation of undetected feature counts. Extensive benchmarking shows that Ocelli outperforms existing methods regarding computational time and quality of the reconstructed low-dimensional representation of developmental data.

Graphical Abstract