Abstract：
Lipidomic data often exhibit missing data points, which can be categorized as missing completely at random (MCAR), missing at random, or missing not at random (MNAR). In order to utilize statistical methods that require complete datasets or to improve the identification of potential effects in statistical comparisons, imputation techniques can be employed. In this study, we investigate commonly used methods such as zero, half-minimum, mean, and median imputation, as well as more advanced techniques such as k-nearest neighbor and random forest imputation. We employ a combination of simulation-based approaches and application to real datasets to assess the performance and effectiveness of these methods. Shotgun lipidomics datasets exhibit high correlations and missing values, often due to low analyte abundance, characterized as MNAR. In this context, k-nearest neighbor approaches based on correlation and truncated normal distributions demonstrate best performance. Importantly, both methods can effectively impute missing values independent of the type of missingness, the determination of which is nearly impossible in practice. The imputation methods still control the type I error rate.
摘要：
脂肪组学数据通常表现出缺失的数据点，可以归类为完全随机缺失(MCAR)，随机失踪，或非随机丢失(MNAR)。为了利用需要完整数据集的统计方法或改进统计比较中潜在影响的识别，可以采用插补技术。在这项研究中,我们研究常用的方法，如零，半最小值，意思是，和中位数填补，以及更先进的技术，如k最近邻和随机森林插补。我们采用基于仿真的方法和实际数据集的应用相结合来评估这些方法的性能和有效性。猎枪脂质组学数据集表现出高度的相关性和缺失值，通常由于分析物丰度低，表征为MNAR。在这种情况下，基于相关性和截断正态分布的k最近邻方法表现出最佳性能。重要的是,这两种方法都可以有效地估算缺失值，而与缺失类型无关，在实践中几乎不可能确定。插补方法仍然控制I型错误率。
