High-throughput metabolomics data are fraught with both nonignorable missing observations and unobserved factors that influence a metabolite’s measured concentration, and it is well known that ignoring either of these complications can compromise estimators. However, current methods to analyze these data can only account for the missing data or unobserved factors, but not both. We therefore developed MetabMiss, a statistically rigorous method to account for both nonrandom missing data and latent factors in high-throughput metabolomics data. Our methodology does not require the practitioner specify a likelihood for the missing data, and makes investigating the relationship between the metabolome and tens, or even hundreds, of phenotypes computationally tractable. We demonstrate the fidelity of MetabMiss’s estimates using both simulated and real metabolomics data and prove their asymptotic correctness when the sample size and number of metabolites grows to infinity.

中文翻译：

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代谢组学中非随机缺失数据和潜在因素的估计和推断


高通量代谢组学数据充满了不可忽略的缺失观察结果和影响代谢物测量浓度的未观察因素，众所周知，忽略这些并发症中的任何一个都会影响估计值。然而，当前分析这些数据的方法只能解释缺失的数据或未观察到的因素，而不能同时解释两者。因此，我们开发了 MetabMiss，这是一种统计上严格的方法，用于解释高通量代谢组学数据中的非随机缺失数据和潜在因素。我们的方法不需要从业者指定丢失数据的可能性，并且可以通过计算来研究代谢组与数十甚至数百个表型之间的关系。我们使用模拟和真实代谢组学数据证明了 MetabMiss 估计的保真度，并证明当样本量和代谢物数量增长到无穷大时其渐近正确性。