Single-cell Curriculum Learning-based Deep Graph Embedding Clustering

• URL: http://arxiv.org/abs/2408.10511v1
• Date: Tue, 20 Aug 2024 03:20:13 GMT
• Title: Single-cell Curriculum Learning-based Deep Graph Embedding Clustering
• Authors: Huifa Li, Jie Fu, Xinpeng Ling, Zhiyu Sun, Kuncan Wang, Zhili Chen,
• Abstract summary: We propose a single-cell curriculum learning-based deep graph embedding clustering (scCLG) We first propose a Chebyshev graph convolutional autoencoder with multi-decoder (ChebAE) that combines three optimization objectives corresponding to three decoders. We employ a selective training strategy to train GNN based on the features and entropy of nodes and prune the difficult nodes based on the difficulty scores to keep the high-quality graph.
• Score: 21.328135630638343
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The swift advancement of single-cell RNA sequencing (scRNA-seq) technologies enables the investigation of cellular-level tissue heterogeneity. Cell annotation significantly contributes to the extensive downstream analysis of scRNA-seq data. However, The analysis of scRNA-seq for biological inference presents challenges owing to its intricate and indeterminate data distribution, characterized by a substantial volume and a high frequency of dropout events. Furthermore, the quality of training samples varies greatly, and the performance of the popular scRNA-seq data clustering solution GNN could be harmed by two types of low-quality training nodes: 1) nodes on the boundary; 2) nodes that contribute little additional information to the graph. To address these problems, we propose a single-cell curriculum learning-based deep graph embedding clustering (scCLG). We first propose a Chebyshev graph convolutional autoencoder with multi-decoder (ChebAE) that combines three optimization objectives corresponding to three decoders, including topology reconstruction loss of cell graphs, zero-inflated negative binomial (ZINB) loss, and clustering loss, to learn cell-cell topology representation. Meanwhile, we employ a selective training strategy to train GNN based on the features and entropy of nodes and prune the difficult nodes based on the difficulty scores to keep the high-quality graph. Empirical results on a variety of gene expression datasets show that our model outperforms state-of-the-art methods.

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