scAGC: Learning Adaptive Cell Graphs with Contrastive Guidance for Single-Cell Clustering

TL;DR

scAGC introduces an adaptive graph learning framework with contrastive guidance for single-cell RNA sequencing clustering, effectively handling high-dimensional, zero-inflated data and long-tailed distributions to improve cell type annotation accuracy.

Contribution

It proposes a novel end-to-end method that dynamically refines cell graphs using a differentiable Gumbel-Softmax and ZINB loss, outperforming existing approaches.

Findings

Outperforms state-of-the-art methods on 9 scRNA-seq datasets.

Achieves highest NMI and ARI scores on most datasets.

Effectively handles zero-inflation and long-tailed distributions.

Abstract

Accurate cell type annotation is a crucial step in analyzing single-cell RNA sequencing (scRNA-seq) data, which provides valuable insights into cellular heterogeneity. However, due to the high dimensionality and prevalence of zero elements in scRNA-seq data, traditional clustering methods face significant statistical and computational challenges. While some advanced methods use graph neural networks to model cell-cell relationships, they often depend on static graph structures that are sensitive to noise and fail to capture the long-tailed distribution inherent in single-cell populations.To address these limitations, we propose scAGC, a single-cell clustering method that learns adaptive cell graphs with contrastive guidance. Our approach optimizes feature representations and cell graphs simultaneously in an end-to-end manner. Specifically, we introduce a topology-adaptive graph autoencoder that leverages a differentiable Gumbel-Softmax sampling strategy to dynamically refine the graph structure during training. This adaptive mechanism mitigates the problem of a long-tailed degree distribution by promoting a more balanced neighborhood structure. To model the discrete, over-dispersed, and zero-inflated nature of scRNA-seq data, we integrate a Zero-Inflated Negative Binomial (ZINB) loss for robust feature reconstruction. Furthermore, a contrastive learning objective is incorporated to regularize the graph learning process and prevent abrupt changes in the graph topology, ensuring stability and enhancing convergence. Comprehensive experiments on 9 real scRNA-seq datasets demonstrate that scAGC consistently outperforms other state-of-the-art methods, yielding the best NMI and ARI scores on 9 and 7 datasets, respectively.Our code is available at Anonymous Github.