论文标题
单细胞RNA-seq数据分析原始计数的数学框架
A mathematical framework for raw counts of single-cell RNA-seq data analysis
论文作者
论文摘要
由于读数的稀少度，许多相关基因的微小表达以及不同细胞的RNA提取效率的可变性，因此单细胞RNA-seq数据是具有挑战性的。我们考虑了一个简单的概率模型，用于读取计数，基于每个基因的负二项式分布，该模型由被解释为提取效率的细胞依赖系数修饰。我们提供了两种替代快速方法来估计模型参数，以及一个细胞导致基因读数为零的读数的概率。这允许以新颖的方式测量基因共表达和差异表达。
Single-cell RNA-seq data are challenging because of the sparseness of the read counts, the tiny expression of many relevant genes, and the variability in the efficiency of RNA extraction for different cells. We consider a simple probabilistic model for read counts, based on a negative binomial distribution for each gene, modified by a cell-dependent coefficient interpreted as an extraction efficiency. We provide two alternative fast methods to estimate the model parameters, together with the probability that a cell results in zero read counts for a gene. This allows to measure genes co-expression and differential expression in a novel way.
