Differentiable Graph Clustering with Structural Grouping for Single-cell RNA-seq Data.

Abstract

Motivation: Clustering cells into subpopulations is one of the most crucial tasks in single-cell RNA sequencing (scRNA-seq) data analysis, which provides support for biological research at cellular level. With the development of graph neural networks, deep graph clustering approaches have achieved excellent performance by modelling the topological relationships between cells. However, existing approaches rely on cell node and its neighbors to obtain the cell feature representation, which ignore the graph cluster structure hidden in scRNA-seq data. Besides, how to bridge the heterogeneous gap between cell node feature and its structural information remains a highly challenging problem.

Results: Here, we propose a novel differentiable graph clustering with structural grouping (DGCSG) for scRNA-seq data, which incorporates graph cluster information into deep graph clustering model by designing a differentiable clustering mechanism to learn clustering-friendly representation. Firstly, an interactive module is devised to dynamically transfer node representations learned by autoencoder (AE) to graph attention autoencoder (GATE) in layer-by-layer manner. Then, to characterize graph cluster information, a differentiable clustering mechanism is proposed to transform K-way normalized cuts from a discrete optimization problem into differentiable learning objective through spectral relaxation, which jointly optimizes the graph attention autoencoder by allocating more attention scores to nodes in the same graph cluster. Finally, a decoupled self-supervised optimization is proposed, which guides the representation learning of AE and GATE in the interactive module. Extensive evaluations on 14 scRNA-seq benchmarks verify the superiority of DGCSG compared with state-of-the-art baselines.

Availability: https://github.com/Xiaoqiang-Yan/DGCSG.

Supplementary information: Supplementary data are available at Bioinformatics online.