Evidence map›Paper›PMID 39789378›Full record

ArticleAnalytical and bioanalytical chemistry2025

Exploratory multivariate analysis using R Language for method development in liquid chromatography.

Miloš Hroch

Abstract read
In one paragraph

Article in Analytical and bioanalytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Miloš HrochDepartment of Medical Biochemistry, Faculty of Medicine in Hradec Králové, Charles University, Šimkova 870, 500 03, Hradec Králové, Czech Republic. hrochm@lfhk.cuni.cz.ORCID http://orcid.org/0000-0002-5583-2942

Funding

ERDF-Project New Technologies for Translational Research in Pharmaceutical Sciences - NETPHARM CZ.02.01.01/00/22_008/0004607
6 · The paper itself

Abstract

The visual evaluation of data derived from screening and optimization experiments in the development of new analytical methods poses a considerable time investment and introduces the risk of subjectivity. This study presents a novel approach to processing such data, based on factor analysis of mixed data and hierarchical clustering - multivariate techniques implemented in the R programming language. The methodology is demonstrated in the early-stage screening and optimization of the chromatographic separation of 15 structurally diverse drugs that affect the central nervous system, using a custom R Language script. The presented explorative approach enabled the identification of key parameters affecting the separation and significantly reduced the time required to evaluate the comprehensive dataset from the screening experiments. Based on the data analysis results, the optimal combination of stationary phase and mobile phase composition was selected, considering retention, overall resolution, and peak shape of compounds. Additionally, compounds vulnerable to changes in selected chromatographic conditions were identified. As a complement to the presented R Language script, a web-based application ChromaFAMDeX has been developed to offer an intuitive interface that enhances the accessibility of the used statistical methods. Accompanying the publication, the R script and the link to the standalone application are provided, enabling replication and adaptation of the methodology.

Indexed as

Programming LanguagesSoftwareChromatography, High Pressure LiquidChromatography, LiquidMultivariate AnalysisFactor analysis of mixed dataHierarchical clusteringLiquid chromatographyOptimizationR Language

Identifiers

PMID39789378
PMCPMC11802592

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.