Karagiannis Tanya T, Monti Stefano, Sebastiani Paola
Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
bioRxiv. 2023 Jan 19:2023.01.17.524410. doi: 10.1101/2023.01.17.524410.
The analysis of cell type proportions in a biological sample should account for the compositional nature of the data but most analyses ignore this characteristic with the risk of producing misleading conclusions. The recent method scCODA appropriately incorporates these constraints by using a Bayesian Multinomial-Dirichlet model that requires a reference cell type to normalize the distribution of all cell types. However, a reference cell type that is stable across biological conditions may not always be available. Here, we present an approach that uses a Bayesian multinomial regression for the analysis of single cell distribution data without the need for a reference cell type. We show an implementation example using the rjags package within the R software.
对生物样本中细胞类型比例的分析应考虑数据的组成性质,但大多数分析忽略了这一特征,存在得出误导性结论的风险。最近的方法scCODA通过使用贝叶斯多项狄利克雷模型适当地纳入了这些约束条件,该模型需要一个参考细胞类型来对所有细胞类型的分布进行归一化。然而,在不同生物学条件下都稳定的参考细胞类型并非总是可用。在此,我们提出一种方法,该方法使用贝叶斯多项回归来分析单细胞分布数据,而无需参考细胞类型。我们展示了一个在R软件中使用rjags包的实现示例。