Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics.
Imputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one. However, the imputation itself may not be optimally considered downstream of the imputation process, as imputed datasets are often considered as if...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-08-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010420 |
| _version_ | 1828110515273990144 |
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| author | Marie Chion Christine Carapito Frédéric Bertrand |
| author_facet | Marie Chion Christine Carapito Frédéric Bertrand |
| author_sort | Marie Chion |
| collection | DOAJ |
| description | Imputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one. However, the imputation itself may not be optimally considered downstream of the imputation process, as imputed datasets are often considered as if they had always been complete. Hence, the uncertainty due to the imputation is not adequately taken into account. We provide a rigorous multiple imputation strategy, leading to a less biased estimation of the parameters' variability thanks to Rubin's rules. The imputation-based peptide's intensities' variance estimator is then moderated using Bayesian hierarchical models. This estimator is finally included in moderated t-test statistics to provide differential analyses results. This workflow can be used both at peptide and protein-level in quantification datasets. Indeed, an aggregation step is included for protein-level results based on peptide-level quantification data. Our methodology, named mi4p, was compared to the state-of-the-art limma workflow implemented in the DAPAR R package, both on simulated and real datasets. We observed a trade-off between sensitivity and specificity, while the overall performance of mi4p outperforms DAPAR in terms of F-Score. |
| first_indexed | 2024-04-11T11:20:18Z |
| format | Article |
| id | doaj.art-291ee1a22e624efab92f8e83695ef083 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T11:20:18Z |
| publishDate | 2022-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-291ee1a22e624efab92f8e83695ef0832022-12-22T04:27:06ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-08-01188e101042010.1371/journal.pcbi.1010420Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics.Marie ChionChristine CarapitoFrédéric BertrandImputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one. However, the imputation itself may not be optimally considered downstream of the imputation process, as imputed datasets are often considered as if they had always been complete. Hence, the uncertainty due to the imputation is not adequately taken into account. We provide a rigorous multiple imputation strategy, leading to a less biased estimation of the parameters' variability thanks to Rubin's rules. The imputation-based peptide's intensities' variance estimator is then moderated using Bayesian hierarchical models. This estimator is finally included in moderated t-test statistics to provide differential analyses results. This workflow can be used both at peptide and protein-level in quantification datasets. Indeed, an aggregation step is included for protein-level results based on peptide-level quantification data. Our methodology, named mi4p, was compared to the state-of-the-art limma workflow implemented in the DAPAR R package, both on simulated and real datasets. We observed a trade-off between sensitivity and specificity, while the overall performance of mi4p outperforms DAPAR in terms of F-Score.https://doi.org/10.1371/journal.pcbi.1010420 |
| spellingShingle | Marie Chion Christine Carapito Frédéric Bertrand Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics. PLoS Computational Biology |
| title | Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics. |
| title_full | Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics. |
| title_fullStr | Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics. |
| title_full_unstemmed | Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics. |
| title_short | Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics. |
| title_sort | accounting for multiple imputation induced variability for differential analysis in mass spectrometry based label free quantitative proteomics |
| url | https://doi.org/10.1371/journal.pcbi.1010420 |
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