Abstract
Single-cell RNA sequencing (scRNA-seq) enables the analysis of the genome, transcriptome, and epigenome at the single-cell level, providing a critical tool for understanding cellular heterogeneity and diversity. Cell clustering, a key step in scRNA-seq data analysis, reveals population structure by grouping cells with similar expression patterns. However, due to the high dimensionality and sparsity of scRNA-seq data, the performance of existing clustering algorithms remains suboptimal. In this study, we propose a novel clustering algorithm, scCCTR, which performs semi-supervised classification by guiding a deep learning model through iterative selection of high-confidence cells and labels. The algorithm consists of two main components: an iterative selection module and a semi-supervised classification module. In the iterative selection module, scCCTR progressively selects high-confidence cells that exhibit core group features and iteratively optimizes feature representations, constructing a consensus clustering result throughout the iterations. In the semi-supervised classification module, scCCTR uses the selected core data to train a Transformer neural network, which leverages a multi-head attention mechanism to focus on critical information, thereby achieving higher clustering precision. We compared scCCTR with several established cell clustering methods on real datasets, and the results demonstrate that scCCTR outperforms existing methods in terms of accuracy and effectiveness for both cell clustering and visualization. (The code of scCCTR is free available for academic https://github.com/chenjiejie387/scCCTR).