Single-cell RNA sequencing (scRNA-seq) enables the possibility of many in-depth transcriptomic analyses at a single-cell resolution. It’s already widely used for exploring the dynamic development process of life, studying the gene regulation mechanism, and discovering new cell types. However, the low RNA capture rate, which cause highly sparse expression with dropout, makes it difficult to do downstream analyses. We propose a new method SCC to impute the dropouts of scRNA-seq data. Experiment results show that SCC gives competitive results compared to two existing methods while showing superiority in reducing the intra-class distance of cells and improving the clustering accuracy in both simulation and real data. SCC is an effective tool to resolve the dropout noise in scRNA-seq data. The code is freely accessible at https://github.com/nwpuzhengyan/SCC .

中文翻译：

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SCC：基于混合模型的scRNA-seq缺失的准确归因方法

单细胞RNA测序（scRNA-seq）使在单细胞分辨率下进行许多深度转录组分析成为可能。它已经广泛用于探索生命的动态发展过程，研究基因调控机制以及发现新的细胞类型。但是，由于RNA捕获率低，会导致表达稀疏而导致缺失，因此很难进行下游分析。我们提出了一种新的SCC方法来估算scRNA-seq数据的缺失。实验结果表明，与两种现有方法相比，SCC具有竞争优势，同时在减少单元内类距离和提高模拟和真实数据的聚类精度方面均具有优势。SCC是解决scRNA-seq数据中缺失噪声的有效工具。该代码可从https：// github免费访问。