Function reference • margheRita

All functions
`CV_ratio()`	Coefficient of variation filter
`Plot2DPCA()`	Plot score and loading plot
`RLA()`	Relative Log Abudance
`annotate_univariate_results()`	Get significant features
`as.PomaSummarizedExperiment()`	Transforms mRList into PomaSummarizedExperiment
`as.metaboset()`	Transforms mRList into MetaboSet object
`calc_RI()`	Convert sample intensity to relative intensity and filter
`calc_ppm_err()`	Calculate PPM error
`calc_reference()`	Calculate a reference profile
`check_RT()`	Check the retention time
`check_RT_mass()`	Merge candidates with appropriate retention time and PPM error
`check_mass()`	Calculate the PPM error of precursors
`collapse_tech_rep()`	Collapse technical replicates
`filter_NA()`	filter NA
`filter_metabolite_associations()`	Filter metabolite-feature pairs
`filtering()`	Filtering of features
`get_spectra_list_from_vector()`	Convert spectra from character vector to a list of data frames
`h_map()`	Draw an heatmap
`h_map_MSMS_comparison()`	h_map_MSMS_comparison
`heatscatter_chromatography()`	Heatscatter chromatography plot Draw the "heatscatter chromatography" and save it to a png file
`imputation()`	Missing values imputation
`keep_strongest_representative()`	Keep the feature with the highest average across samples
`mL_demo`	mL_demo
`mR_pca()`	PCA
`m_z_filtering()`	Filter m/z of metabolites
`mean_median_stdev_samples()`	Calculate mean, median and standard deviation for each biological group.
`metab_boxplot()`	Boxplot of metabolites
`metabolite_identification()`	Metabolite Identification Run the metabolite identification pipeline
`normalize_profiles()`	Normalize profiles
`pathway_analysis()`	Pathway analysis Run clusterProfiler on pathways provided by NCBI Biosytems to perform ORA or MSEA
`peak_matching()`	MS/MS peaks matching
`pqn()`	PQN normalization
`read_input_file()`	Create an mRList from feature data and sample information
`remove_samples()`	Remove samples from mRList
`select_library()`	Library selection
`select_sign_features()`	select significant features
`size_effect()`	Size effect for pqn
`splitQC()`	Split dataframe and metadata in two: one for samples and one for QC
`univariate()`	Univariate analysis
`visualize_1spectra()`	Visualize the spectra of good candidates
`visualize_associated_spectra()`	MS/MS Spectra Visualization Visualize the MS/MS spectra of the features associated with a metabolite.

Reference

All functions