多组学数据的两步惩罚逻辑斯谛回归及其在心脏代谢综合征中的应用
Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome
摘要：
基于高维多组学数据集构建预测二进制类别标签的分类模型提出了几个挑战，这是由于数据层在预测器数量、数据类型和噪声水平方面通常具有广泛不同的特性。先前的研究已经表明，将具有弹性网络惩罚的经典逻辑斯谛回归应用于这些数据集可能导致较差的结果（Liu等人，2018年）。我们实现了一个两步的方法，多组学逻辑斯谛回归，其中变量的选择是在每一层上分别进行，然后使用在第一步中选择的变量建立预测模型。在这里，我们的方法与为相同目的开发的其他方法进行了比较，我们将现有的多组学线性回归软件（Zhao和Zucknick，2020）调整为逻辑斯谛回归设置。广泛的模拟研究表明，我们的方法应该是首选，如果目标是选择尽可能多的相关预测，以及实现预测性能媲美的最佳竞争对手。我们的激励示例是心脏代谢综合征数据集，其包括针对2个极端表型组（10个肥胖和10个脂肪代谢障碍个体）和185个献血者的8个组学数据类型。我们提出的方法使我们能够在分子水平上识别出抑制心脏代谢综合征的特征。R代码可在https://github.com/acabassi/logistic-regression-for-multi-omic-data上获得。
英文摘要：
Building classification models that predict a binary class label on the basis of high dimensional multi-omics datasets poses several challenges, due to the typically widely differing characteristics of the data layers in terms of number of predictors, type of data, and levels of noise. Previous research has shown that applying classical logistic regression with elastic-net penalty to these datasets can lead to poor results (Liu et al., 2018). We implement a two-step approach to multi-omic logistic regression in which variable selection is performed on each layer separately and a predictive model is then built using the variables selected in the first step. Here, our approach is compared to other methods that have been developed for the same purpose, and we adapt existing software for multi-omic linear regression (Zhao and Zucknick, 2020) to the logistic regression setting. Extensive simulation studies show that our approach should be preferred if the goal is to select as many relevant predictors as possible, as well as achieving prediction performances comparable to those of the best competitors. Our motivating example is a cardiometabolic syndrome dataset comprising eight 'omic data types for 2 extreme phenotype groups (10 obese and 10 lipodystrophy individuals) and 185 blood donors. Our proposed approach allows us to identify features that characterise cardiometabolic syndrome at the molecular level. R code is available at https://github.com/acabassi/logistic-regression-for-multi-omic-data.
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