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Cellograph: a semi-supervised approach to analyzing multi-condition single-cell RNA-sequencing data using graph

Jamshaid A Shahir^1,2,3, Natalie Stanley^2,3,4, Jeremy E Purvis^5,6,7,8

¹Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

BMC Bioinformatics

|January 14, 2024

Summary

Cellograph, a new deep learning framework, analyzes single-cell data to reveal how cells respond to perturbations. It quantifies cellular changes and identifies key genes, outperforming existing methods.

Keywords:

Graph neural networks Semi-supervised learning Single-cell genomics

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell data analysis is crucial for understanding cellular responses to perturbations.
Existing differential gene expression (DGE) methods often oversimplify data by clustering, losing valuable single-cell variability information.
Current DGE approaches may yield false positives due to incorrect statistical distribution assumptions.

Purpose of the Study:

To introduce Cellograph, a novel semi-supervised framework utilizing graph neural networks for single-cell data analysis.
To quantify perturbation effects at single-cell resolution, preserving cellular variability.
To develop an interpretable latent space for data visualization and clustering, identifying key genes driving condition-specific differences.

Main Methods:

Cellograph employs graph neural networks (GNNs) within a semi-supervised learning framework.
The method quantifies cell prototypicality across experimental conditions.
It learns a latent gene representation and a gene weight matrix for interpretability.

Main Results:

Cellograph effectively quantifies perturbation effects at single-cell granularity.
The framework generates an interpretable latent space suitable for visualization and clustering.
Analysis of cancer drug therapy, stem cell reprogramming, and organoid differentiation datasets demonstrated Cellograph's utility.
Cellograph outperformed existing methods in quantifying experimental perturbation effects.

Conclusions:

Cellograph provides a powerful new framework for analyzing single-cell data using deep learning.
The method leverages single-cell variability for deeper biological insights.
Cellograph offers superior performance in quantifying perturbation effects compared to traditional approaches.