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

SUBMITTER: Kokla M 

PROVIDER: S-EPMC6788053 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature