论文标题
零充气计数的结构学习，并应用于单细胞RNA测序数据
Structure learning for zero-inflated counts, with an application to single-cell RNA sequencing data
论文作者
论文摘要
从观察到的数据中估算图的结构的问题在高通量基因组数据的背景下，尤其是单细胞RNA测序越来越兴趣。但是，这些数据是充满挑战的应用程序，因为数据包括具有较高差异和零差异的高维计数。在这里，我们提出了一个一般框架，用于根据零泄漏的负二项式分布从单细胞RNA-seq数据中学习图的结构。我们通过模拟证明我们的方法能够在各种设置中检索图的结构，并且我们在真实数据上显示了该方法的实用性。
The problem of estimating the structure of a graph from observed data is of growing interest in the context of high-throughput genomic data, and single-cell RNA sequencing in particular. These, however, are challenging applications, since the data consist of high-dimensional counts with high variance and over-abundance of zeros. Here, we present a general framework for learning the structure of a graph from single-cell RNA-seq data, based on the zero-inflated negative binomial distribution. We demonstrate with simulations that our approach is able to retrieve the structure of a graph in a variety of settings and we show the utility of the approach on real data.
