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

Xu, Jingjing; Wang, Yuan-Shan; Xu, Xiangnan; Cheng, Kun; Raftery, Daniel; Dong, Jiyang

doi:10.3390/molecules26195787

Cited by 10 publications

(8 citation statements)

References 30 publications

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“…It is particularly suitable for analyses using large-scale multimodal data since it has been demonstrated to perform well regardless of the underlying pattern of missingness. (77-79) Supplementary Table 2 shows the number and percentage of observations which were trimmed and filled with NNMF for the training and test sets, respectively. After imputation with NNMF, any variables originating from phenotypic assessments lacking summary scores were reduced to single continuous, summary metrics using feature agglomeration.…”

Section: Methodsmentioning

confidence: 99%

Predicting new onset thought disorder in early adolescence with optimized deep learning implicates environmental-putamen interactions

de Lacy,

Ramshaw

2023

Preprint

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Background: Thought disorder (TD) is a sensitive and specific marker of risk for schizophrenia onset. Specifying factors that predict TD onset in adolescence is important to early identification of youth at risk. However, there is a paucity of studies prospectively predicting TD onset in unstratified youth populations. Study Design: We used deep learning optimized with artificial intelligence (AI) to analyze 5,777 multimodal features obtained at 9-10 years from youth and their parents in the ABCD study, including 5,014 neural metrics, to prospectively predict new onset TD cases at 11-12 years. The design was replicated for all prevailing TD cases at 11-12 years. Study Results: Optimizing performance with AI, we were able to achieve 92% accuracy and F1 and 0.96 AUROC in prospectively predicting the onset of TD in early adolescence. Structural differences in the left putamen, sleep disturbances and the level of parental externalizing behaviors were specific predictors of new onset TD at 11-12 yrs, interacting with low youth prosociality, the total parental behavioral problems and parentchild conflict and whether the youth had already come to clinical attention. More important predictors showed greater inter-individual variability. Conclusions: This study provides robust person-level, multivariable signatures of early adolescent TD which suggest that structural differences in the left putamen in late childhood are a candidate biomarker that interacts with psychosocial stressors to increase risk for TD onset. Our work also suggests that interventions to promote improved sleep and lessen parent-child psychosocial stressors are worthy of further exploration to modulate risk for TD onset.

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Section: Methodsmentioning

confidence: 99%

Predicting new onset thought disorder in early adolescence with optimized deep learning implicates environmental-putamen interactions

de Lacy,

Ramshaw

2023

Preprint

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“…noise analysis for protein liquid chromatography-mass spectrometry of human serum. Bioinformatics, 20 (18)…”

Section: Associated Contentmentioning

confidence: 99%

“…On the basis of the resulting citation counts (Figure 1), we selected four of the most popular imputation methods: k-nearest neighbor (kNN) [3], MissForest [11], Gaussian sampling [9], and low value replacement (Figure 1). We also include a non-negative matrix factorization (NMF) imputation method, which has recently been proposed for proteomics [18][19][20]. By focusing only on the most commonly used imputation methods, our aim is to provide a practical comparison that will be beneficial to experimental proteomicists.…”

Section: Introductionmentioning

confidence: 99%

Evaluating proteomics imputation methods with improved criteria

Harris

Fondrie²,

et al. 2023

Preprint

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Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. This missingness hinders reproducibility, reduces statistical power, and makes it difficult to compare across samples or experiments. Although many methods exist for imputing missing values in proteomics data, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error, such as the mean-squared error between the imputed and the held-out observed values. Here we evaluate the performance of a set of commonly used imputation methods using three practical, "downstream-centric" criteria, which measure the ability of imputation methods to reconstruct differentially expressed peptides, identify new quantitative peptides, and improve peptide lower limit of quantification. Our evaluation spans several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides, but that it can identify new quantitative peptides and improve peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that exisiting imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.

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“…On the basis of the resulting citation counts (Figure ), we selected four of the most popular imputation methods: k -nearest neighbor (kNN), MissForest, Gaussian sampling, and low value replacement. We also include a non-negative matrix factorization (NMF) imputation method, which has recently been proposed for proteomics. − By focusing on only the most commonly used imputation methods, our aim is to provide a practical comparison that will be beneficial to experimental proteomicists. For this reason, seldom used R packages (e.g., imp4p, impSeqRob, and QRLIC) have been omitted from our analysis.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Proteomics Imputation Methods with Improved Criteria

Harris,

Fondrie,

et al. 2023

J. Proteome Res.

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Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. Missing values hinder reproducibility, reduce statistical power, and make it difficult to compare across samples or experiments. Although many methods exist for imputing missing values, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error such as the mean-squared error between imputed and held-out values. Here we evaluate the performance of commonly used imputation methods using three practical, “downstream-centric” criteria. These criteria measure the ability to identify differentially expressed peptides, generate new quantitative peptides, and improve the peptide lower limit of quantification. Our evaluation comprises several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides but that it can identify new quantitative peptides and improve the peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that existing imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.

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

Cited by 10 publications

References 30 publications

Predicting new onset thought disorder in early adolescence with optimized deep learning implicates environmental-putamen interactions

Predicting new onset thought disorder in early adolescence with optimized deep learning implicates environmental-putamen interactions

Evaluating proteomics imputation methods with improved criteria

Evaluating Proteomics Imputation Methods with Improved Criteria

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