NMF-Based Approach for Missing Values Imputation of Mass Spectrometry Metabolomics Data.

ABSTRACT: In mass spectrometry (MS)-based metabolomics, missing values (NAs) may be due to different causes, including sample heterogeneity, ion suppression, spectral overlap, inappropriate data processing, and instrumental errors. Although a number of methodologies have been applied to handle NAs, NA imputation remains a challenging problem. Here, we propose a non-negative matrix factorization (NMF)-based method for NA imputation in MS-based metabolomics data, which makes use of both global and local information of the data. The proposed method was compared with three commonly used methods: k-nearest neighbors (kNN), random forest (RF), and outlier-robust (ORI) missing values imputation. These methods were evaluated from the perspectives of accuracy of imputation, retrieval of data structures, and

SUBMITTER: Xu J 

PROVIDER: S-EPMC8510447 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature