Model-based clustering for flow and mass cytometry data with clinical information
Model-based clustering for flow and mass cytometry data with clinical information
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DOI：
10.1186/s12859-020-03671-7
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发表时间：
2020-09-17
影响因子：
3
通讯作者：
Shimamura, Teppei
中科院分区：
文献类型：
--
作者：
Abe, Ko;Minoura, Kodai;Shimamura, Teppei
背景高维流式细胞术和质量细胞术可以在单细胞分辨率下对10多个蛋白质图谱进行系统水平的表征，并在许多生物学应用中提供了更广阔的前景，如疾病诊断和临床结果预测。在将临床信息与细胞学数据相关联时，传统方法需要两个不同的步骤来识别细胞群体，并进行统计检验以确定两个群体比例之间的差异是否显著。结果我们提出了一种新的统计框架，称为LAMBDA(基于贝叶斯数据分析的潜在分配模型)，用于同时识别未知细胞群体并发现这些群体与临床信息之间的关联。Lambda使用特定的概率模型，分别为流式细胞术或质量细胞术数据建模不同的分布信息。针对质量仪数据的特点，我们采用了零膨胀分布。通过对估计参数的精度进行评估，仿真研究证实了该模型的有效性。我们还证明，Lambda可以通过分析真实数据来确定细胞群体和他们的临床结果之间的关联。Lambda在R中实现，可从GitHub(https://github.com/abikoushi/lambda).)获得
BackgroundHigh-dimensional flow cytometry and mass cytometry allow systemic-level characterization of more than 10 protein profiles at single-cell resolution and provide a much broader landscape in many biological applications, such as disease diagnosis and prediction of clinical outcome. When associating clinical information with cytometry data, traditional approaches require two distinct steps for identification of cell populations and statistical test to determine whether the difference between two population proportions is significant. These two-step approaches can lead to information loss and analysis bias.ResultsWe propose a novel statistical framework, called LAMBDA (Latent Allocation Model with Bayesian Data Analysis), for simultaneous identification of unknown cell populations and discovery of associations between these populations and clinical information. LAMBDA uses specified probabilistic models designed for modeling the different distribution information for flow or mass cytometry data, respectively. We use a zero-inflated distribution for the mass cytometry data based the characteristics of the data. A simulation study confirms the usefulness of this model by evaluating the accuracy of the estimated parameters. We also demonstrate that LAMBDA can identify associations between cell populations and their clinical outcomes by analyzing real data. LAMBDA is implemented in R and is available from GitHub (https://github.com/abikoushi/lambda).