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Identification of significant B cell associations with undetected observations using a Tobit model

机译:使用Tobit模型识别未检测到的重要B细胞关联

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To study the relationship of serum antibody neutralization activity (determined by IC50) and the B cell immune response, we face two challenges: (i) IC50 values can not be observed when they are below the detected limitation, and (ii) the number of factors is larger than the number of observations. To address these two challenges, we propose a Tobit model for the analysis of the study, and an adaptive LASSO penalized variable selection procedure to identify important factors. Furthermore, we suggest extended Bayesian information criterion for selecting the tuning parameter. Our analysis indicates that three measured B cells, specifically the frequency of CD19+CD20+, CD19-CD20+, and IgD-B220-CD27- peripheral blood B cell subsets have significant effects on IC50. We have also run simulation studies to evaluate the numerical performance of the proposed method.
机译:为了研究血清抗体中和活性(由IC50决定)与B细胞免疫反应之间的关系,我们面临两个挑战:(i)当IC50值低于检测到的限制时无法观察到,以及(ii)因素大于观察的数量。为了解决这两个挑战,我们提出了一个用于研究分析的Tobit模型,以及一个自适应的LASSO罚变量选择程序以识别重要因素。此外,我们建议使用扩展的贝叶斯信息准则来选择调整参数。我们的分析表明,三个测量的B细胞,特别是CD19 + CD20 +,CD19-CD20 +和IgD-B220-CD27-外周血B细胞亚群的频率对IC50有重要影响。我们还进行了仿真研究,以评估该方法的数值性能。

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