Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics
Abstract When analyzing large datasets from high-throughput technologies, researchers often encounter missing quantitative measurements, which are particularly frequent in metabolomics datasets. Metabolomics, the comprehensive profiling of metabolite abundances, are typically measured using mass spe...
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| Format: | Article |
| Language: | English |
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BMC
2022-05-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-022-04659-1 |
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| author | Jonathan P. Dekermanjian Elin Shaddox Debmalya Nandy Debashis Ghosh Katerina Kechris |
| author_facet | Jonathan P. Dekermanjian Elin Shaddox Debmalya Nandy Debashis Ghosh Katerina Kechris |
| author_sort | Jonathan P. Dekermanjian |
| collection | DOAJ |
| description | Abstract When analyzing large datasets from high-throughput technologies, researchers often encounter missing quantitative measurements, which are particularly frequent in metabolomics datasets. Metabolomics, the comprehensive profiling of metabolite abundances, are typically measured using mass spectrometry technologies that often introduce missingness via multiple mechanisms: (1) the metabolite signal may be smaller than the instrument limit of detection; (2) the conditions under which the data are collected and processed may lead to missing values; (3) missing values can be introduced randomly. Missingness resulting from mechanism (1) would be classified as Missing Not At Random (MNAR), that from mechanism (2) would be Missing At Random (MAR), and that from mechanism (3) would be classified as Missing Completely At Random (MCAR). Two common approaches for handling missing data are the following: (1) omit missing data from the analysis; (2) impute the missing values. Both approaches may introduce bias and reduce statistical power in downstream analyses such as testing metabolite associations with clinical variables. Further, standard imputation methods in metabolomics often ignore the mechanisms causing missingness and inaccurately estimate missing values within a data set. We propose a mechanism-aware imputation algorithm that leverages a two-step approach in imputing missing values. First, we use a random forest classifier to classify the missing mechanism for each missing value in the data set. Second, we impute each missing value using imputation algorithms that are specific to the predicted missingness mechanism (i.e., MAR/MCAR or MNAR). Using complete data, we conducted simulations, where we imposed different missingness patterns within the data and tested the performance of combinations of imputation algorithms. Our proposed algorithm provided imputations closer to the original data than those using only one imputation algorithm for all the missing values. Consequently, our two-step approach was able to reduce bias for improved downstream analyses. |
| first_indexed | 2024-04-12T15:36:05Z |
| format | Article |
| id | doaj.art-ce408c908a7843cfaa11e6d594a04f11 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T15:36:05Z |
| publishDate | 2022-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-ce408c908a7843cfaa11e6d594a04f112022-12-22T03:26:58ZengBMCBMC Bioinformatics1471-21052022-05-0123111710.1186/s12859-022-04659-1Mechanism-aware imputation: a two-step approach in handling missing values in metabolomicsJonathan P. Dekermanjian0Elin Shaddox1Debmalya Nandy2Debashis Ghosh3Katerina Kechris4Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical CampusDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical CampusDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical CampusDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical CampusDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical CampusAbstract When analyzing large datasets from high-throughput technologies, researchers often encounter missing quantitative measurements, which are particularly frequent in metabolomics datasets. Metabolomics, the comprehensive profiling of metabolite abundances, are typically measured using mass spectrometry technologies that often introduce missingness via multiple mechanisms: (1) the metabolite signal may be smaller than the instrument limit of detection; (2) the conditions under which the data are collected and processed may lead to missing values; (3) missing values can be introduced randomly. Missingness resulting from mechanism (1) would be classified as Missing Not At Random (MNAR), that from mechanism (2) would be Missing At Random (MAR), and that from mechanism (3) would be classified as Missing Completely At Random (MCAR). Two common approaches for handling missing data are the following: (1) omit missing data from the analysis; (2) impute the missing values. Both approaches may introduce bias and reduce statistical power in downstream analyses such as testing metabolite associations with clinical variables. Further, standard imputation methods in metabolomics often ignore the mechanisms causing missingness and inaccurately estimate missing values within a data set. We propose a mechanism-aware imputation algorithm that leverages a two-step approach in imputing missing values. First, we use a random forest classifier to classify the missing mechanism for each missing value in the data set. Second, we impute each missing value using imputation algorithms that are specific to the predicted missingness mechanism (i.e., MAR/MCAR or MNAR). Using complete data, we conducted simulations, where we imposed different missingness patterns within the data and tested the performance of combinations of imputation algorithms. Our proposed algorithm provided imputations closer to the original data than those using only one imputation algorithm for all the missing values. Consequently, our two-step approach was able to reduce bias for improved downstream analyses.https://doi.org/10.1186/s12859-022-04659-1Missing dataImputationMachine learningMetabolomics |
| spellingShingle | Jonathan P. Dekermanjian Elin Shaddox Debmalya Nandy Debashis Ghosh Katerina Kechris Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics BMC Bioinformatics Missing data Imputation Machine learning Metabolomics |
| title | Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics |
| title_full | Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics |
| title_fullStr | Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics |
| title_full_unstemmed | Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics |
| title_short | Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics |
| title_sort | mechanism aware imputation a two step approach in handling missing values in metabolomics |
| topic | Missing data Imputation Machine learning Metabolomics |
| url | https://doi.org/10.1186/s12859-022-04659-1 |
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