PhD. Seminar: Subgraph Embedding of Gene Expression Matrix for Prediction of Cell-Cell Communication by Akram Vasighizaker

Wednesday, December 13, 2023 - 10:00

The School of Computer Science at the University of Windsor is pleased to present …

Title: Subgraph Embedding of Gene Expression Matrix for Prediction of Cell-Cell Communication


PhD. Seminar by: Akram Vasighizaker


Date: Wednesday December 13th, 2023

Time: 10:00 am – 11:30 am

Location: Essex Hall, Room 122 



Recent advances in single-cell RNA sequencing technology have eased analyses of intercellular signalling networks. Recently, cell-cell communication is studied based on various link prediction approaches on graph-structured data.

These approaches have assumptions about the likelihood of node interaction, thus, showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network.

In this work, we present a novel method, called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), which uses an attributed graph convolutional neural network to predict cell-cell communication from single-cell RNA-seq data. SEGCECO captures the latent as well as explicit attributes of undirected, attributed graphs constructed from the gene expression profile of individual cells. High-dimensional and sparse single-cell RNA-seq data make the process of converting the data to a graphical format a daunting task. We successfully overcome this limitation by applying SoptSC, a similarity-based optimization method in which the cell-cell communication network is built using a cell-cell similarity matrix which is learned from gene expression data.

We performed experiments on six datasets extracted from the human and mouse pancreas tissue. Our comparative analysis shows that SEGCECO outperforms latent feature-based approaches, as well as the state-of-the-art method for link prediction, WLNM, with 0.99 ROC and 99% prediction accuracy.


PhD Doctoral Committee:

Internal Reader: Dima Alhadidi

Advisor (s): Luis Rueda


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