Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study

Abstract

Background LC-MS technology makes it possible to measure the relative abundance of numerous molecular features of a sample in single analysis. However, especially non-targeted metabolite profiling approaches generate vast arrays of data that are prone to aberrations such as missing values. No matter the reason for the missing values in the data, coherent and complete data matrix is always a pre-requisite for accurate and reliable statistical analysis. Therefore, there is a need for proper imputation strategies that account for the missingness and reduce the bias in the statistical analysis. Results Here we present our results after evaluating nine imputation methods in four different percentages of missing values of different origin. The performance of each imputation method was analyzed by Normalized Root Mean Squared Error (NRMSE). We demonstrated that random forest (RF) had the lowest NRMSE in the estimation of missing values for Missing at Random (MAR) and Missing Completely at Random (MCAR). In case of absent values due to Missing Not at Random (MNAR), the left truncated data was best imputed with minimum value imputation. We also tested the different imputation methods for datasets containing missing data of various origin, and RF was the most accurate method in all cases. The results were obtained by repeating the evaluation process 100 times with the use of metabolomics datasets where the missing values were introduced to represent absent data of different origin. Conclusion Type and rate of missingness affects the performance and suitability of imputation methods. RF-based imputation method performs best in most of the tested scenarios, including combinations of different types and rates of missingness. Therefore, we recommend using random forest-based imputation for imputing missing metabolomics data, and especially in situations where the types of missingness are not known in advance.

The K nearest neighbor algorithm for imputation of missing longitudinal prenatal alcohol data

“…However, some studies reported that simpler methods such as mean or median replacement were as adequate as methods like kNN when imputation was followed by clustering of genetic data [26]. On the other hand, some have reported slightly better performance of random forest over kNN to impute metabolomics data [27].…”

“Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling

“…Missing data occur in metabolomics datasets for various reasons and managing this missingness is highly challenging [33]. Imputation is the procedure of replacing missing data with reasonable values using a priori knowledge or information available from the existing data.…”

“…Imputation is the procedure of replacing missing data with reasonable values using a priori knowledge or information available from the existing data. In this workflow, we perform random forest (RF)-based imputation using the missForest package [33,34], although several other procedures are available [35,36]. Data distributions can affect statistical analysis, especially for variance-based models [37].…”

“NoTaMe”: Workflow for Non-Targeted LC-MS Metabolic Profiling

“…Missing data occur in metabolomics datasets for various reasons and it is one of the most challenging computational processes in the metabolomics data preprocessing (28). Imputation is the procedure replacing the missing data with reasonable values using a priori knowledge or information available from the existing data.…”

“…Imputation is the procedure replacing the missing data with reasonable values using a priori knowledge or information available from the existing data. In this workflow, random forest (RF) imputation is performed in order to replace the missing values with the most appropriate estimate with the missForest package (28,29), although several other procedures for imputation are available (30,31).…”