Getting streamlined elaboration of feature tables with separated quality controls, advanced statistics such as linear model with mixed effects, and more: presenting the “GetFeatistics” R-package Gianfranco Frigerio1,2,3 1 Center for Omics Sciences (COSR), IRCCS San Raffaele Scientific Institute, Milan, Italy 2 Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg 3 Department of Clinical Sciences and Community Health, University of Milan, and Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy 20th Annual Conference of the Metabolomics Society – METABOLOMICS 2024 – Osaka, Japan, 16th -20th June 2024 The package is on GitHub: https://github.com/FrigerioGianfranco/GetFeatistics I acknowledge Albina Rastoder for helping with the testing of some functions, during her 2 months internship at the Luxembourg Centre for Systems Biomedicine (LCSB) of the University of Luxembourg. I also acknowledge Prof. Dr. Emma Schymanski for the support and suggestions and the entire Environmental Cheminformatics group of the Luxembourg Centre for Systems Biomedicine (LCSB) of the University of Luxembourg for the help and feedback. I acknowledge funding support from the Luxembourg National Research Fund (FNR) for project A18/BM/12341006. frigerio.gianfranco@hsr.it Any questions? Write me!!! ☺ Gianfranco Frigerio @gianfrancofrig1 Gianfranco Frigerio Within the metabolomics community, tools such as MS-DIAL and R-based algorithms as XCMS are widely implemented to convert raw data to feature tables. As demonstrated in a previous work, while conducting non-targeted metabolomics studies with defined groups of subjects assessing variations of molecules present at low concentrations (such as metabolites of exogenous compounds) the use of separated pooled quality control (QC) samples can be a promising strategy to both improve the quality of the dataset and preserving potential features deriving from low concentrated molecules. Moreover, in observational epidemiologic studies, controlling for confounding factors is crucial to assess the association of metabolite variations with the biological question of interest; also, for longitudinal studies, linear models with mixed effects are a great statistical approach. Targeted analyses Non-targeted analyses featTable featINFO get_feat_info_from_MSDial • featTable: table with feature intensities • featINFO: table with rt, m/z and potential annotations get_targeted_elaboration Given a tables containing data from targeted analyses, including a calibration curve and internal standards, it calculates the concentrations in unknown samples. test_normality_density_plot test_normality_q_q_plot test_normality_saphiro_table transf_data To replace missing values, log-transform, and/or scale the data. gentab_P.1wayANOVA_posthocTurkeyHSD It performs one-way ANOVA, with also TurkeyHSD posthoc tests for between-groups comparison, to each desired variable and creates a new table with the p-values gentab_P.2wayANOVA_posthocTurkeyHSD It performs 2-way ANOVA, with also TurkeyHSD posthoc tests for between groups comparison, to each desired variable and creates a new table with the p-values addINFO_to_table Given a table containing data related to features (for example the output of a statistical analyses) and the related featINFO table, it combines everything in a single table. It removes special characters from a character vector Given a dataframe and a set of dependent and independent variables from that dataframe, it generates a linear regression model for each single dependent variable and creates a new table with all slopes and p-values. plot_calibration_curves It creates plots of the calibration curves from a targeted elaboration. Statistical analyses Functions to test normality, by producing density plot, q-q-plot and performing Shapiro test get_feat_info_from_patRoon get_feat_table_from_MSDial get_feat_table_from_patRoon From MS-DIAL output or from patRoon output, it