论文标题
单细胞熵来量化单细胞RNA-seq数据的细胞转录
Single-cell entropy to quantify the cellular transcription from single-cell RNA-seq data
论文作者
论文摘要
我们介绍了单细胞熵（Scentropy）的使用来测量单细胞RNA-seq数据的细胞转录组轮廓谱的顺序，该数据导致通过香气进行无监督的细胞类型分类的方法，然后是高斯混合模型（SCEGMM）。 Scentropy在定义细胞的固有转录状态时很简单。 SCEGMM是一种连贯的细胞类型分类方法，其中不包含参数且没有聚类。但是，它可与基准研究中的现有基于机器学习的方法相媲美，并促进生物学解释。
We present the use of single-cell entropy (scEntropy) to measure the order of the cellular transcriptome profile from single-cell RNA-seq data, which leads to a method of unsupervised cell type classification through scEntropy followed by the Gaussian mixture model (scEGMM). scEntropy is straightforward in defining an intrinsic transcriptional state of a cell. scEGMM is a coherent method of cell type classification that includes no parameters and no clustering; however, it is comparable to existing machine learning-based methods in benchmarking studies and facilitates biological interpretation.
