# scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding

arXiv: https://arxiv.org/abs/2404.06167 · HTML: https://mlsanity.com/paper/2404.06167/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [Graph Neural Networks with Heterophily](https://mlsanity.com/paper/2009.13566/) (2020)
- [Structural Deep Clustering Network](https://mlsanity.com/paper/2002.01633/) (2020)
- [Graph Soft-Contrastive Learning via Neighborhood Ranking](https://mlsanity.com/paper/2209.13964/) (2022)

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [Deep Cut-informed Graph Embedding and Clustering](https://mlsanity.com/paper/2503.06635/) (2025)
- [scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data](https://mlsanity.com/paper/2505.12626/) (2025)
- [Rethinking Graph Contrastive Learning through Relative Similarity Preservation](https://mlsanity.com/paper/2505.05533/) (2025)
- [Soft Graph Clustering for single-cell RNA Sequencing Data](https://mlsanity.com/paper/2507.09890/) (2025)
- [scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis](https://mlsanity.com/paper/2509.25884/) (2025)
- [Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linking](https://mlsanity.com/paper/2505.19722/) (2025)
- [scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing](https://mlsanity.com/paper/2606.13007/) (2026)
- [scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering](https://mlsanity.com/paper/2606.18672/) (2026)
- [scKDGM: KAN-guided Dynamic Graph Masked Learning for Single-Cell RNA-seq Clustering](https://mlsanity.com/paper/2606.28459/) (2026)

## Abstract (arXiv)

Single-cell RNA sequencing (scRNA-seq) is essential for unraveling cellular heterogeneity and diversity, offering invaluable insights for bioinformatics advancements. Despite its potential, traditional clustering methods in scRNA-seq data analysis often neglect the structural information embedded in gene expression profiles, crucial for understanding cellular correlations and dependencies. Existing strategies, including graph neural networks, face challenges in handling the inefficiency due to scRNA-seq data's intrinsic high-dimension and high-sparsity. Addressing these limitations, we introduce scCDCG (single-cell RNA-seq Clustering via Deep Cut-informed Graph), a novel framework designed for efficient and accurate clustering of scRNA-seq data that simultaneously utilizes intercellular high-order structural information. scCDCG comprises three main components: (i) A graph embedding module utilizing deep cut-informed techniques, which effectively captures intercellular high-order structural information, overcoming the over-smoothing and inefficiency issues prevalent in prior graph neural network methods. (ii) A self-supervised learning module guided by optimal transport, tailored to accommodate the unique complexities of scRNA-seq data, specifically its high-dimension and high-sparsity. (iii) An autoencoder-based feature learning module that simplifies model complexity through effective dimension reduction and feature extraction. Our extensive experiments on 6 datasets demonstrate scCDCG's superior performance and efficiency compared to 7 established models, underscoring scCDCG's potential as a transformative tool in scRNA-seq data analysis. Our code is available at: https://github.com/XPgogogo/scCDCG.