Discrimination of missing data types in metabolomics data based on particle swarm optimization algorithm and XGBoost model

Abstract

In the field of data analysis, it is often faced with a large number of missing values, especially in metabolomics data, this problem is more prominent. Data imputation is a common method to deal with missing metabolomics data, while traditional data imputation methods usually ignore the differences in missing types, and thus the results of data imputation are not satisfactory. In order to discriminate the missing types of metabolomics data, a missing data classification model (PX-MDC) based on particle swarm algorithm and XGBoost is proposed in this paper. First, the missing values in a given missing data set are obtained by panning the missing values to obtain the largest subset of complete data, and then the particle swarm algorithm is used to search for the concentration threshold of missing data and the proportion of low concentration deletions as a percentage of overall deletions. Next, the missing data are simulated based on the search results. Finally, the training data are trained using the XGBoost model using the feature set proposed in this paper in order to build a classifier for the missing data. The experimental results show that the particle swarm algorithm is able to match the traditional enumeration method in terms of accuracy and significantly reduce the search time in concentration threshold search. Compared with the current mainstream methods, the PX-MDC model designed in this paper exhibits higher accuracy and is able to distinguish different deletion types for the same metabolite. This study is expected to make an important breakthrough in metabolomics data imputation and provide strong support for research in related fields.