Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise.

中文翻译：

---

PRIME：一种概率插补方法，可减少单细胞RNA测序中的脱落效应。

单细胞RNA测序技术提供了一种新颖的方法来分析单个细胞的转录组谱。但是，该技术容易受到一种称为“落差效应”的噪声的影响，这种噪声会导致转录组图谱中的分布零膨胀，并降低结果的可靠性。因此，在进行深入分析之前，需要仔细处理单细胞RNA测序数据。在这里，我们描述了一种新颖的插补方法，可以减少单细胞测序中的缺失效应。我们构建一个细胞对应网络，并根据转录组概况调整相同类型细胞的局部子网的基因表达估计。我们对这种方法进行了全面评估，这种方法称为PRIME（可降低概率的输入，以减少单细胞测序表达谱中的脱落效应），