SCC：一种基于混合模型的 scRNA-seq 数据缺失的精确推断方法。
SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model.
机构信息
School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xi'an, 710072, China.
出版信息
BMC Bioinformatics. 2021 Jan 6;22(1):5. doi: 10.1186/s12859-020-03878-8.
BACKGROUND
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.
RESULTS
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.
CONCLUSIONS
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 .
背景
单细胞 RNA 测序（scRNA-seq）使在单细胞分辨率下进行许多深入的转录组分析成为可能。它已经被广泛用于探索生命的动态发展过程、研究基因调控机制和发现新的细胞类型。然而，低 RNA 捕获率导致高度稀疏的表达，出现数据缺失，这使得下游分析变得困难。
结果
我们提出了一种新的方法 SCC 来推断 scRNA-seq 数据中的缺失值。实验结果表明，SCC 与两种现有方法相比，具有竞争力，同时在减少细胞内类间距离和提高模拟和真实数据的聚类准确性方面具有优势。
结论
SCC 是解决 scRNA-seq 数据中缺失值噪声的有效工具。代码可在 https://github.com/nwpuzhengyan/SCC 上免费获取。
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