Robust scRNA-seq Cell Types Identification by Self-Guided Deep Clustering Network

Abstract

The emergence of single-cell RNA sequencing (scRNA-seq) has brought to light the critical need for scrutinizing transcriptomes at the individual cellular level with unparalleled precision. A pivotal aspect of scRNA-seq data analysis involves cell identification, commonly accomplished through diverse clustering methodologies. However, scRNA-seq datasets frequently encounter missing values due to technical limitations, posing a significant challenge that can compromise the accuracy of clustering outcomes. In response, we present a novel approach that seamlessly integrates missing value estimation with scRNA-seq clustering. Our method harnesses the power of an imputation autoencoder network to predict missing values, coupled with the deployment of a deep clustering network for efficient cell categorization. To mitigate the risk of deep clustering networks converging towards suboptimal local minima, we have devised a self-guided learning strategy. This approach exploits shared parameters between the imputation and clustering networks, fostering a symbiotic relationship that enhances overall performance. Through rigorous empirical evaluations, we substantiate the effectiveness of our methodology, demonstrating its comparability to, or surpassing, several established single-cell clustering techniques. Furthermore, our analysis of cellular trajectories underscores the proficiency of the proposed method in accurately deducing cellular trajectories by leveraging the clustering results to discern biologically meaningful cell types.

Keywords

• Single cell clustering
• data imputation
• subspace clustering