Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurements
Abstract Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes, commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition n...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-09-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-04918-1 |
| _version_ | 1811191579554086912 |
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| author | Mathias Gotsmy Julia Brunmair Christoph Büschl Christopher Gerner Jürgen Zanghellini |
| author_facet | Mathias Gotsmy Julia Brunmair Christoph Büschl Christopher Gerner Jürgen Zanghellini |
| author_sort | Mathias Gotsmy |
| collection | DOAJ |
| description | Abstract Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes, commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii), contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data. |
| first_indexed | 2024-04-11T21:11:34Z |
| format | Article |
| id | doaj.art-fe834cc27d9443e4a5f1d5f4b3349b03 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-11T21:11:34Z |
| publishDate | 2022-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-fe834cc27d9443e4a5f1d5f4b3349b032022-12-22T04:03:00ZengBMCBMC Bioinformatics1471-21052022-09-0123113010.1186/s12859-022-04918-1Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurementsMathias Gotsmy0Julia Brunmair1Christoph Büschl2Christopher Gerner3Jürgen Zanghellini4Department of Analytical Chemistry, Faculty of Chemistry, University of ViennaDepartment of Analytical Chemistry, Faculty of Chemistry, University of ViennaDepartment of Analytical Chemistry, Faculty of Chemistry, University of ViennaDepartment of Analytical Chemistry, Faculty of Chemistry, University of ViennaDepartment of Analytical Chemistry, Faculty of Chemistry, University of ViennaAbstract Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes, commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii), contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data.https://doi.org/10.1186/s12859-022-04918-1MetabolomicsFinger SweatBlood PlasmaPKMPQN |
| spellingShingle | Mathias Gotsmy Julia Brunmair Christoph Büschl Christopher Gerner Jürgen Zanghellini Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurements BMC Bioinformatics Metabolomics Finger Sweat Blood Plasma PKM PQN |
| title | Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurements |
| title_full | Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurements |
| title_fullStr | Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurements |
| title_full_unstemmed | Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurements |
| title_short | Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurements |
| title_sort | probabilistic quotient s work and pharmacokinetics contribution countering size effect in metabolic time series measurements |
| topic | Metabolomics Finger Sweat Blood Plasma PKM PQN |
| url | https://doi.org/10.1186/s12859-022-04918-1 |
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