SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication

Understanding how cells interact with each other is key to unlocking the mysteries of development, disease, and beyond. Recent advancements in single-cell RNA sequencing (scRNA-seq) technology have provided unprecedented insights into the signaling networks that govern these interactions. However, deciphering the complex web of cell-to-cell communication from scRNA-seq data has remained a daunting challenge—until now.

A groundbreaking study by researchers at the University of Windsor introduces a novel method called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), poised to revolutionize our understanding of cell communication. Built upon the foundation of attributed graph convolutional neural networks, SEGCECO leverages the power of machine learning to predict cell–cell communication from scRNA-seq data with unprecedented accuracy.

Unlike previous approaches that made assumptions about the likelihood of cell interaction, SEGCECO overcomes this limitation by extracting local subgraphs from the cellular network. By capturing both the latent and explicit attributes of undirected, attributed graphs constructed from gene expression profiles, SEGCECO offers a comprehensive view of cell communication dynamics.

Pipeline of the proposed framework

One of the main challenges in analyzing scRNA-seq data is its high-dimensional and sparse nature, making it difficult to convert into a graphical format. However, SEGCECO tackles this obstacle head-on by utilizing SoptSC, a similarity-based optimization method. By learning a cell–cell similarity matrix from gene expression data, SoptSC constructs a robust communication network that serves as the foundation for SEGCECO’s predictive capabilities.

To put SEGCECO to the test, the researchers conducted experiments on six datasets extracted from human and mouse pancreas tissue. The results were nothing short of impressive—SEGCECO outperformed latent feature-based approaches and the state-of-the-art method for link prediction, WLNM, with a remarkable ROC of 0.99 and 99% prediction accuracy.

The implications of this groundbreaking research are vast. By shedding light on the intricate web of cell communication, SEGCECO opens doors to new insights into disease mechanisms, therapeutic targets, and beyond. From understanding developmental processes to unraveling the complexities of cancer progression, the potential applications of SEGCECO are limitless.

Availability – The code is publicly available at Github https://github.com/sheenahora/SEGCECO and Code Ocean https://codeocean.com/capsule/8244724/tree.

Vasighizaker A, Hora S, Zeng R, Rueda L. (2024) SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication. Brief Bioinform 25(3):bbae160. [article]

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