背景:单细胞技术的创新导致了一系列数据集和计算工具来处理和解释它们,包括细胞组成变化和细胞状态转变的分析。细胞计数数据差异发现的diffcyt工作流程由几个步骤组成,包括预处理,与二元或连续协变量关联的细胞群体识别和差异测试。然而,临床研究中通常测量的生存时间数量通常会导致经典差异测试不适用的删失协变量。
结果:为了克服这一限制,使用模拟研究和案例研究,研究了在差异丰度分析中直接包括删失协变量的多种方法。结果表明,基于多重插补的方法在灵敏度和误差控制方面提供了与Cox比例风险模型相同的性能。同时提供考虑协变量的灵活性。所测试的方法作为diffcyt的扩展在R包censcyt中实现,可在https://biocorductor.org/packages/censcyt上获得。
结论:在GLMM中直接包含删失变量作为预测因子的方法是经典生存分析方法的有效替代方法,比如Cox比例风险模型,同时允许差异分析更多的灵活性。
BACKGROUND: Innovations in single cell technologies have lead to a flurry of datasets and computational tools to process and interpret them, including analyses of cell composition changes and transition in cell states. The diffcyt workflow for differential discovery in cytometry data consist of several steps, including preprocessing, cell population identification and differential testing for an association with a binary or continuous covariate. However, the commonly measured quantity of survival time in clinical studies often results in a censored covariate where classical differential testing is inapplicable.
RESULTS: To overcome this limitation, multiple methods to directly include censored covariates in differential abundance analysis were examined with the use of simulation studies and a case study. Results show that multiple imputation based methods offer on-par performance with the Cox proportional hazards model in terms of sensitivity and error control, while offering flexibility to account for covariates. The tested methods are implemented in the R package censcyt as an extension of diffcyt and are available at https://bioconductor.org/packages/censcyt .
CONCLUSIONS: Methods for the direct inclusion of a censored variable as a predictor in GLMMs are a valid alternative to classical survival analysis methods, such as the Cox proportional hazard model, while allowing for more flexibility in the differential analysis.