Compositional Data Analysis using Kernels in mass cytometry data.
Compositional Data Analysis using Kernels in mass cytometry data.
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DOI：
10.1093/bioadv/vbac003
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Ghosh D
中科院分区：
文献类型：
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作者：
Rudra P;Baxter R;Hsieh EWY;Ghosh D
由质谱细胞计数实验产生的细胞类型丰度数据本质上是组成性的。经典的关联检验不适用于成分数据，由于其非欧几里德性质。用于分析细胞类型丰度数据的现有方法对于高维质量细胞计数数据具有若干限制，特别是当样本量小时。我们提出了一种新的多元统计学习方法，使用核的组成数据分析（CODAK），基于核距离协方差（KDC）框架来测试细胞类型组成与重要预测因子（分类或连续）（如疾病状态）的关联。CODAK对于高维数据具有良好的扩展性，并且对于小样本量（n < 25）具有令人满意的性能。我们进行了模拟研究，以比较该方法的性能与现有的方法分析细胞类型丰度数据的质谱细胞术研究。该方法也适用于一个高维数据集，包含不同的人群亚组，包括系统性红斑狼疮（SLE）患者和健康对照受试者。CODAK使用R实现。本手稿中使用的代码和数据可在http://github.com/GhoshLab/CODAK/网站上获得。 prudra@okstate.edu 补充数据可在Bioinformatics Advances在线获得。
Cell-type abundance data arising from mass cytometry experiments are compositional in nature. Classical association tests do not apply to the compositional data due to their non-Euclidean nature. Existing methods for analysis of cell type abundance data suffer from several limitations for high-dimensional mass cytometry data, especially when the sample size is small. We proposed a new multivariate statistical learning methodology, Compositional Data Analysis using Kernels (CODAK), based on the kernel distance covariance (KDC) framework to test the association of the cell type compositions with important predictors (categorical or continuous) such as disease status. CODAK scales well for high-dimensional data and provides satisfactory performance for small sample sizes (n < 25). We conducted simulation studies to compare the performance of the method with existing methods of analyzing cell type abundance data from mass cytometry studies. The method is also applied to a high-dimensional dataset containing different subgroups of populations including Systemic Lupus Erythematosus (SLE) patients and healthy control subjects. CODAK is implemented using R. The codes and the data used in this manuscript are available on the web at http://github.com/GhoshLab/CODAK/. prudra@okstate.edu Supplementary data are available at Bioinformatics Advances online.