Recent single-cell transcriptomic studies revealed new insights into cell-type heterogeneities in cellular microenvironments unavailable from bulk studies. A significant drawback of currently available algorithms is the need to use empirical parameters or rely on indirect quality measures to estimate the degree of complexity, i.e., the number of subgroups present in the sample. We fill this gap with a single-cell data analysis procedure allowing for unambiguous assessments of the depth of heterogeneity in subclonal compositions supported by data. Our approach combines nonnegative matrix factorization, which takes advantage of the sparse and nonnegative nature of single-cell RNA count data, with Bayesian model comparison enabling de novo prediction of the depth of heterogeneity. We show that the method predicts the correct number of subgroups using simulated data, primary blood mononuclear cell, and pancreatic cell data. We applied our approach to a collection of single-cell tumor samples and found two qualitatively distinct classes of cell-type heterogeneity in cancer microenvironments.

中文翻译：

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从头预测单细胞RNA-seq和肿瘤微环境中细胞类型的复杂性。

最近的单细胞转录组学研究揭示了对细胞微环境中细胞类型异质性的新见解，而批量研究无法获得这种微异质性。当前可用算法的显着缺点是需要使用经验参数或依靠间接质量度量来估计复杂程度，即样本中存在的子组的数量。我们用单细胞数据分析程序填补了这一空白，可以对数据支持的亚克隆组合物中的异质性深度进行明确评估。我们的方法结合了非负矩阵分解（利用单细胞RNA计数数据的稀疏和非负性质）和贝叶斯模型比较，从而可以从头预测异质性深度。我们显示该方法使用模拟数据，原代血液单核细胞和胰腺细胞数据预测正确的亚组数目。我们将我们的方法应用于单细胞肿瘤样本的收集，并在癌症微环境中发现了两种在质量上截然不同的类别的细胞类型异质性。