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A combined test for feature selection on sparse metaproteomics data—an alternative to missing value imputation
One of the difficulties encountered in the statistical analysis of metaproteomics data is the high proportion of missing values, which are usually treated by imputation. Nevertheless, imputation methods are based on restrictive assumptions regarding missingness mechanisms, namely “at random” or “not...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235818/ https://www.ncbi.nlm.nih.gov/pubmed/35769140 http://dx.doi.org/10.7717/peerj.13525 |
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author | Plancade, Sandra Berland, Magali Blein-Nicolas, Mélisande Langella, Olivier Bassignani, Ariane Juste, Catherine |
author_facet | Plancade, Sandra Berland, Magali Blein-Nicolas, Mélisande Langella, Olivier Bassignani, Ariane Juste, Catherine |
author_sort | Plancade, Sandra |
collection | PubMed |
description | One of the difficulties encountered in the statistical analysis of metaproteomics data is the high proportion of missing values, which are usually treated by imputation. Nevertheless, imputation methods are based on restrictive assumptions regarding missingness mechanisms, namely “at random” or “not at random”. To circumvent these limitations in the context of feature selection in a multi-class comparison, we propose a univariate selection method that combines a test of association between missingness and classes, and a test for difference of observed intensities between classes. This approach implicitly handles both missingness mechanisms. We performed a quantitative and qualitative comparison of our procedure with imputation-based feature selection methods on two experimental data sets, as well as simulated data with various scenarios regarding the missingness mechanisms and the nature of the difference of expression (differential intensity or differential presence). Whereas we observed similar performances in terms of prediction on the experimental data set, the feature ranking and selection from various imputation-based methods were strongly divergent. We showed that the combined test reaches a compromise by correlating reasonably with other methods, and remains efficient in all simulated scenarios unlike imputation-based feature selection methods. |
format | Online Article Text |
id | pubmed-9235818 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92358182022-06-28 A combined test for feature selection on sparse metaproteomics data—an alternative to missing value imputation Plancade, Sandra Berland, Magali Blein-Nicolas, Mélisande Langella, Olivier Bassignani, Ariane Juste, Catherine PeerJ Bioinformatics One of the difficulties encountered in the statistical analysis of metaproteomics data is the high proportion of missing values, which are usually treated by imputation. Nevertheless, imputation methods are based on restrictive assumptions regarding missingness mechanisms, namely “at random” or “not at random”. To circumvent these limitations in the context of feature selection in a multi-class comparison, we propose a univariate selection method that combines a test of association between missingness and classes, and a test for difference of observed intensities between classes. This approach implicitly handles both missingness mechanisms. We performed a quantitative and qualitative comparison of our procedure with imputation-based feature selection methods on two experimental data sets, as well as simulated data with various scenarios regarding the missingness mechanisms and the nature of the difference of expression (differential intensity or differential presence). Whereas we observed similar performances in terms of prediction on the experimental data set, the feature ranking and selection from various imputation-based methods were strongly divergent. We showed that the combined test reaches a compromise by correlating reasonably with other methods, and remains efficient in all simulated scenarios unlike imputation-based feature selection methods. PeerJ Inc. 2022-06-24 /pmc/articles/PMC9235818/ /pubmed/35769140 http://dx.doi.org/10.7717/peerj.13525 Text en © 2022 Plancade et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Plancade, Sandra Berland, Magali Blein-Nicolas, Mélisande Langella, Olivier Bassignani, Ariane Juste, Catherine A combined test for feature selection on sparse metaproteomics data—an alternative to missing value imputation |
title | A combined test for feature selection on sparse metaproteomics data—an alternative to missing value imputation |
title_full | A combined test for feature selection on sparse metaproteomics data—an alternative to missing value imputation |
title_fullStr | A combined test for feature selection on sparse metaproteomics data—an alternative to missing value imputation |
title_full_unstemmed | A combined test for feature selection on sparse metaproteomics data—an alternative to missing value imputation |
title_short | A combined test for feature selection on sparse metaproteomics data—an alternative to missing value imputation |
title_sort | combined test for feature selection on sparse metaproteomics data—an alternative to missing value imputation |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235818/ https://www.ncbi.nlm.nih.gov/pubmed/35769140 http://dx.doi.org/10.7717/peerj.13525 |
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