Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data

Mass spectrometry-based lipidomics and metabolomics generate extensive data sets that, along with metadata such as clinical parameters, require specific data exploration skills to identify and visualize statistically significant trends and biologically relevant differences. Besides tailored methods...

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Veröffentlicht in:	Nature communications Jg. 16; H. 1; S. 8714 - 19
Hauptverfasser:	Idkowiak, Jakub, Dehairs, Jonas, Schwarzerová, Jana, Olešová, Dominika, Truong, Jacob X. M., Kvasnička, Aleš, Eftychiou, Marios, Cools, Ruben, Spotbeen, Xander, Jirásko, Robert, Veseli, Vullnet, Giampà, Marco, de Laat, Vincent, Butler, Lisa M., Weckwerth, Wolfram, Friedecký, David, Demeulemeester, Jonas, Hron, Karel, Swinnen, Johannes V., Holčapek, Michal
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 30.09.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	101/58 631/114/2415 631/45/320 631/45/608 82/16 Accessibility Best practice Biological variation Chromatography Complexity Data analysis Datasets Humanities and Social Sciences Humans Lipidomics - methods Lipids Mass Spectrometry Mass spectroscopy Metabolites Metabolomics Metabolomics - methods multidisciplinary Parameter identification Programming Languages Python Quality standards Review Article Science Science (multidisciplinary) Scientific imaging Skills Software Statistical analysis Statistical methods Statistics Variables Visualization
ISSN:	2041-1723, 2041-1723
Online-Zugang:	Volltext
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