Single-cell RNA sequencing (scRNA-seq) is widely used to study cellular heterogeneity in different samples. However, due to technical deficiencies, dropout events often result in zero gene expression values in the gene expression matrix. In this paper, we propose a new imputation method called scCAN, based on adaptive neighborhood clustering, to estimate the zero value of dropouts. Our method continuously updates cell-cell similarity information by simultaneously learning similarity relationships, clustering structures, and imposing new rank constraints on the Laplacian matrix of the similarity matrix, improving the imputation of dropout zero values. To evaluate the performance of this method, we used four simulated and eight real scRNA-seq data for downstream analyses, including cell clustering, recovered gene expression, and reconstructed cell trajectories. Our method improves the performance of the downstream analysis and is better than other imputation methods.
IEEE/ACM transactions on computational biology and bioinformatics. 2024 Jan-Feb;21(1):95-105. doi: 10.1109/TCBB.2023.3337231 Q13.42025
scCAN: Clustering With Adaptive Neighbor-Based Imputation Method for Single-Cell RNA-Seq Data
scCAN：具有自适应邻居插值方法的单细胞RNA测序数据聚类分析 翻译改进
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DOI: 10.1109/TCBB.2023.3337231 PMID: 38285569
摘要 Ai翻译
Keywords：Single-Cell RNA-Seq Data; clustering
关键词：单细胞RNA测序数据; 聚类
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