SimCD：单细胞转录组数据的同时聚类和差异表达分析
SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic data
摘要：
单细胞RNA测序（scRNA-seq）测量促进了个体细胞的基因组规模的转录组学分析，希望解卷积相应细胞亚群中的细胞动态变化，以更好地理解不同发育过程的分子机制。已经提出了几种scRNA-seq分析方法，以首先通过聚类鉴定细胞亚群，然后分别进行差异表达分析以了解基因表达变化。它们相应的统计模型和推理算法通常是分开设计的。我们开发了一种新的方法- SimCD -在一个统一的分层γ-负二项式（hGNB）模型中明确建模细胞异质性和动态差异变化，允许同时进行scRNA-seq数据的细胞聚类和差异表达分析。我们的方法通过动态表达变化自然地定义了细胞异质性，与单独执行这两项任务的现有方法相比，这有望有助于在这两项任务上实现更好的性能。此外，SimCD通过细胞和基因水平因子更好地模拟scRNA-seq数据中的脱落（零膨胀），并避免了复杂的预处理步骤，如归一化，这要归功于通过严格的hGNB模型和高效的Gibbs采样推断算法直接建模scRNA-seq计数数据。与模拟和真实scRNA-seq计数数据的最新方法进行的广泛比较证明了SimCD发现细胞簇和捕获动态表达变化的能力。此外，SimCD有助于识别下丘脑神经元细胞亚型中受食物剥夺影响的几个已知基因以及一些新的潜在标记物，表明SimCD用于生物标记物发现的能力。
英文摘要：
Single-Cell RNA sequencing (scRNA-seq) measurements have facilitated genome-scale transcriptomic profiling of individual cells, with the hope of deconvolving cellular dynamic changes in corresponding cell sub-populations to better understand molecular mechanisms of different development processes. Several scRNA-seq analysis methods have been proposed to first identify cell sub-populations by clustering and then separately perform differential expression analysis to understand gene expression changes. Their corresponding statistical models and inference algorithms are often designed disjointly. We develop a new method -- SimCD -- that explicitly models cell heterogeneity and dynamic differential changes in one unified hierarchical gamma-negative binomial (hGNB) model, allowing simultaneous cell clustering and differential expression analysis for scRNA-seq data. Our method naturally defines cell heterogeneity by dynamic expression changes, which is expected to help achieve better performances on the two tasks compared to the existing methods that perform them separately. In addition, SimCD better models dropout (zero inflation) in scRNA-seq data by both cell- and gene-level factors and obviates the need for sophisticated pre-processing steps such as normalization, thanks to the direct modeling of scRNA-seq count data by the rigorous hGNB model with an efficient Gibbs sampling inference algorithm. Extensive comparisons with the state-of-the-art methods on both simulated and real-world scRNA-seq count data demonstrate the capability of SimCD to discover cell clusters and capture dynamic expression changes. Furthermore, SimCD helps identify several known genes affected by food deprivation in hypothalamic neuron cell subtypes as well as some new potential markers, suggesting the capability of SimCD for bio-marker discovery.
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