NMF-Based Approach for Missing Values Imputation of Mass Spectrometry Metabolomics Data
| Autor: | Daniel Raftery, Jingjing Xu, Wang Yuanshan, Kian Kai Cheng, Jiyang Dong, Xiangnan Xu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Pharmaceutical Science Organic chemistry missing pattern Article Mass Spectrometry Analytical Chemistry Matrix decomposition Non-negative matrix factorization non-negative matrix factorization QD241-441 Drug Discovery Cluster Analysis Metabolomics Imputation (statistics) Physical and Theoretical Chemistry metabolomics data Data processing business.industry outliers Computational Biology Pattern recognition Data structure Missing data Random forest missing values imputation Chemistry (miscellaneous) Outlier Molecular Medicine Artificial intelligence business Algorithms |
| Zdroj: | Molecules Molecules, Vol 26, Iss 5787, p 5787 (2021) Volume 26 Issue 19 |
| ISSN: | 1420-3049 |
| Popis: | 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 rank of imputation superiority. The experimental results showed that the NMF-based method is well-adapted to various cases of data missingness and the presence of outliers in MS-based metabolic profiles. It outperformed kNN and ORI and showed results comparable with the RF method. Furthermore, the NMF method is more robust and less susceptible to outliers as compared with the RF method. The proposed NMF-based scheme may serve as an alternative NA imputation method which may facilitate biological interpretations of metabolomics data. |
| Databáze: | OpenAIRE |
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