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GAEKLRR: A novel clustering method of the low-rank representation based on graph auto-encoder and relaxed k-means for single-cell type identification

Lin-Ping Wang,Junliang Shang,Lingyun Dai,Juan Wang

Published 2025 in IEEE International Conference on Bioinformatics and Biomedicine

ABSTRACT

Clustering is critical for scRNA-seq because it reveals the similarity of single-cell expression patterns. However, single-cell data contains numerous noises and outliers. Traditional clustering algorithms may fail to capture accurate clustering information. In this study, we propose the GAEKLRR method for single-cell type identification, which is a low-rank representation (LRR) method based on a graph autoencoder (GAE) and relaxed k-means. GAEKLRR consists of gedLRR and relaxed k-means. Among them, gedLRR is a GAEbased LRR algorithm that captures structural information and node features of samples using GAE. Relaxed k-means is a soft clustering method that can better preserve complex relationships between samples through soft partitioning. Specifically, to reduce the impact of noise and outliers on the mapping benchmark, GAEKLRR generates a robust graph embedding dictionary using gedLRR. Due to the reconstruction using inner product distance, the graph embedding dictionary has interpretability. Meanwhile, to capture accurate clustering information, GAEKLRR utilizes gedLRR to seek the LRR matrix of the graph embedding dictionary while using relaxed k -means to update the clustering centroid. It is worth noting that the continuous clustering indication matrix captured by relaxed k-means contains clustering labels, which can be used directly for clustering tasks. Finally, experiments on real singlecell datasets demonstrate that GAEKLRR has significant advantages for clustering.

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