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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...

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Autores principales: Plancade, Sandra, Berland, Magali, Blein-Nicolas, Mélisande, Langella, Olivier, Bassignani, Ariane, Juste, Catherine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
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.
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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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