Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)

Abstract The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because cu...

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Autores principales:	Sebastian Malkusch, Lisa Hahnefeld, Robert Gurke, Jörn Lötsch
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/850b9423372b45f3bd26413a3af60024
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spelling	oai:doaj.org-article:850b9423372b45f3bd26413a3af600242021-11-15T18:41:53ZVisually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)2163-830610.1002/psp4.12704https://doaj.org/article/850b9423372b45f3bd26413a3af600242021-11-01T00:00:00Zhttps://doi.org/10.1002/psp4.12704https://doaj.org/toc/2163-8306Abstract The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because current tools available for this purpose often require programming skills, preprocessing tools with graphical user interfaces that can be used interactively are needed. In collaboration between data scientists and experts in bioanalytical diagnostics, a graphical software package for data preprocessing called pguIMP is proposed, which contains a fixed sequence of preprocessing steps to enable reproducible interactive data preprocessing. As an R‐based package, it also allows direct integration into this data science environment without requiring any programming knowledge. The implementation of contemporary data processing methods, including machine‐learning‐based imputation techniques, ensures the generation of corrected and cleaned bioanalytical data sets that preserve data structures such as clusters better than is possible with classical methods. This was evaluated on bioanalytical data sets from lipidomics and drug research using k‐nearest‐neighbors‐based imputation followed by k‐means clustering and density‐based spatial clustering of applications with noise. The R package provides a Shiny‐based web interface designed to be easy to use for non–data analysis experts. It is demonstrated that the spectrum of methods provided is suitable as a standard pipeline for preprocessing bioanalytical data in biomedical research domains. The R package pguIMP is freely available at the comprehensive R archive network (https://cran.r‐project.org/web/packages/pguIMP/index.html).Sebastian MalkuschLisa HahnefeldRobert GurkeJörn LötschWileyarticleTherapeutics. PharmacologyRM1-950ENCPT: Pharmacometrics & Systems Pharmacology, Vol 10, Iss 11, Pp 1371-1381 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Therapeutics. Pharmacology RM1-950
spellingShingle	Therapeutics. Pharmacology RM1-950 Sebastian Malkusch Lisa Hahnefeld Robert Gurke Jörn Lötsch Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)
description	Abstract The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because current tools available for this purpose often require programming skills, preprocessing tools with graphical user interfaces that can be used interactively are needed. In collaboration between data scientists and experts in bioanalytical diagnostics, a graphical software package for data preprocessing called pguIMP is proposed, which contains a fixed sequence of preprocessing steps to enable reproducible interactive data preprocessing. As an R‐based package, it also allows direct integration into this data science environment without requiring any programming knowledge. The implementation of contemporary data processing methods, including machine‐learning‐based imputation techniques, ensures the generation of corrected and cleaned bioanalytical data sets that preserve data structures such as clusters better than is possible with classical methods. This was evaluated on bioanalytical data sets from lipidomics and drug research using k‐nearest‐neighbors‐based imputation followed by k‐means clustering and density‐based spatial clustering of applications with noise. The R package provides a Shiny‐based web interface designed to be easy to use for non–data analysis experts. It is demonstrated that the spectrum of methods provided is suitable as a standard pipeline for preprocessing bioanalytical data in biomedical research domains. The R package pguIMP is freely available at the comprehensive R archive network (https://cran.r‐project.org/web/packages/pguIMP/index.html).
format	article
author	Sebastian Malkusch Lisa Hahnefeld Robert Gurke Jörn Lötsch
author_facet	Sebastian Malkusch Lisa Hahnefeld Robert Gurke Jörn Lötsch
author_sort	Sebastian Malkusch
title	Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)
title_short	Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)
title_full	Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)
title_fullStr	Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)
title_full_unstemmed	Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)
title_sort	visually guided preprocessing of bioanalytical laboratory data using an interactive r notebook (pguimp)
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/850b9423372b45f3bd26413a3af60024
work_keys_str_mv	AT sebastianmalkusch visuallyguidedpreprocessingofbioanalyticallaboratorydatausinganinteractivernotebookpguimp AT lisahahnefeld visuallyguidedpreprocessingofbioanalyticallaboratorydatausinganinteractivernotebookpguimp AT robertgurke visuallyguidedpreprocessingofbioanalyticallaboratorydatausinganinteractivernotebookpguimp AT jornlotsch visuallyguidedpreprocessingofbioanalyticallaboratorydatausinganinteractivernotebookpguimp
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Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)

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