An optimized graph-based structure for single-cell RNA-seq cell-type classification based on non-linear dimension reduction

Abadi, Saeedeh Akbari Rokn; Laghaee, Seyed Pouria; Koohi, Somayyeh

doi:10.1186/s12864-023-09344-y

Cited by 3 publications

(2 citation statements)

References 30 publications

(36 reference statements)

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“…We selected several state-of-art scRNA-seq clustering methods as baseline methods, including DESC [18], Leiden (scanpy) [4,15], SC3 [13], scGNN [10], scGAC [11], graph-sc [21], scDeepCluster [9], scvi-tools [20], and SCEA [24]. Among these methods, scGAC, scDeepCluster and SCEA need to specify the number of clusters when using K-means algorithm to initialize the cluster centers.…”

Section: Evaluation Metric and Baseline Methodsmentioning

confidence: 99%

“…scGAC [11] also constructed the cell-cell graph and adopted a self-optimizing method to simultaneously learn representation and optimize clustering. The topology embeddings of cells were extracted by Graph Attention Network-based GAE, and the optimized clustering results were obtained by DEC. Additionally, SCEA utilized an Encoder based on Multilayer Perceptron (MLP) for data dimension reduction prior to employing a similar topology representation learning strategy and achieved superior performance [24].…”

Section: Introductionmentioning

confidence: 99%

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Attention-based deep clustering method for scRNA-seq cell type identification

Li,

Guo,

Zhang

et al. 2023

PLoS Comput Biol

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Single-cell sequencing (scRNA-seq) technology provides higher resolution of cellular differences than bulk RNA sequencing and reveals the heterogeneity in biological research. The analysis of scRNA-seq datasets is premised on the subpopulation assignment. When an appropriate reference is not available, such as specific marker genes and single-cell reference atlas, unsupervised clustering approaches become the predominant option. However, the inherent sparsity and high-dimensionality of scRNA-seq datasets pose specific analytical challenges to traditional clustering methods. Therefore, a various deep learning-based methods have been proposed to address these challenges. As each method improves partially, a comprehensive method needs to be proposed. In this article, we propose a novel scRNA-seq data clustering method named AttentionAE-sc (Attention fusion AutoEncoder for single-cell). Two different scRNA-seq clustering strategies are combined through an attention mechanism, that include zero-inflated negative binomial (ZINB)-based methods dealing with the impact of dropout events and graph autoencoder (GAE)-based methods relying on information from neighbors to guide the dimension reduction. Based on an iterative fusion between denoising and topological embeddings, AttentionAE-sc can easily acquire clustering-friendly cell representations that similar cells are closer in the hidden embedding. Compared with several state-of-art baseline methods, AttentionAE-sc demonstrated excellent clustering performance on 16 real scRNA-seq datasets without the need to specify the number of groups. Additionally, AttentionAE-sc learned improved cell representations and exhibited enhanced stability and robustness. Furthermore, AttentionAE-sc achieved remarkable identification in a breast cancer single-cell atlas dataset and provided valuable insights into the heterogeneity among different cell subtypes.

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Section: Evaluation Metric and Baseline Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attention-based deep clustering method for scRNA-seq cell type identification

Li,

Guo,

Zhang

et al. 2023

PLoS Comput Biol

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Graph neural network approaches for single-cell data: a recent overview

Lazaros,

Koumadorakis,

Vlamos

et al. 2024

Neural Comput & Applic

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scLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data

Liu,

Liang,

Wang

et al. 2024

Briefings in Bioinformatics

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Single-cell RNA sequencing (scRNA-seq) enables the exploration of biological heterogeneity among different cell types within tissues at a resolution. Inferring cell types within tissues is foundational for downstream research. Most existing methods for cell type inference based on scRNA-seq data primarily utilize highly variable genes (HVGs) with higher expression levels as clustering features, overlooking the contribution of HVGs with lower expression levels. To address this, we have designed a novel cell type inference method for scRNA-seq data, termed scLEGA. scLEGA employs a novel zero-inflated negative binomial (ZINB) loss function that fully considers the contribution of genes with lower expression levels and combines two distinct scRNA-seq clustering strategies through a multi-head attention mechanism. It utilizes a low-expression optimized denoising autoencoder, based on the novel ZINB model, to extract low-dimensional features and handle dropout events, and a GCN-based graph autoencoder (GAE) that leverages neighbor information to guide dimensionality reduction. The iterative fusion of denoising and topological embedding in scLEGA facilitates the acquisition of cluster-friendly cell representations in the hidden embedding, where similar cells are brought closer together. Compared to 12 state-of-the-art cell type inference methods on 15 scRNA-seq datasets, scLEGA demonstrates superior performance in clustering accuracy, scalability, and stability. Our scLEGA model codes are freely available at https://github.com/Masonze/scLEGA-main.

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An optimized graph-based structure for single-cell RNA-seq cell-type classification based on non-linear dimension reduction

Cited by 3 publications

References 30 publications

Attention-based deep clustering method for scRNA-seq cell type identification

Attention-based deep clustering method for scRNA-seq cell type identification

Graph neural network approaches for single-cell data: a recent overview

scLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data

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