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

Area of Science:

• Genomics
• Computational Biology
• Developmental Biology

Background:

• Single-cell technologies allow simultaneous genome-wide measurements of multiple cellular features.
• Jointly profiling gene expression, chromatin accessibility, and protein epitopes aids in understanding gene regulation during development.
• Analyzing multimodal single-cell data is crucial for deciphering complex biological processes.

Purpose of the Study:

• To introduce Ocelli, a scalable Python package for multimodal single-cell data analysis.
• To provide tools for visualizing and analyzing developmental processes using diffusion-based modeling.
• To enable identification of rare cell states and imputation of feature counts.

Main Methods:

• Ocelli utilizes Ray for scalable computation.
• Employs diffusion-based modeling for cell state transition analysis.
• Implements visualization on low-dimensional embeddings preserving developmental continuity.

Main Results:

• Ocelli facilitates visualization of continuous developmental trajectories.
• Enables identification of rare and transient cell states.
• Demonstrates superior computational time and reconstruction quality compared to existing methods.

Conclusions:

• Ocelli offers an efficient and effective solution for multimodal single-cell data analysis.
• The package aids in understanding developmental trajectories and gene regulation.
• Ocelli represents a significant advancement in single-cell data analysis tools.