Introduction

The PollyMetaboR can be used to process metabolomics data generated using untargeted approach. This package works on the peak detailed format of El-MAVEN output.

Steps to use this package

library(PollyMetaboR)

Load Data

data(demo_peak_detailed_elmaven, package = "PollyMetaboR")
data(KEGG_mzMass, package = "PollyMetaboR")

Make XCMS Object

xcms_obj <- create_xcms_object_from_elmaven(demo_peak_detailed_elmaven)

Perform Annotation using CAMERA

camera_output <- perform_annotation_by_camera(xcms_obj, polarity = "positive", ppm = 10, mzabs = 0.1)

Restructure CAMERA output

restructure_camera <- restructure_camera_annotations(camera_output, polarity = "positive")

Get feature representative for each feature group

representative_df <- get_feature_group_representative(restructure_camera$combined, polarity = "positive")

Perform Metabolite Identification

identified_df <- perform_metabolite_identification(mz_data = representative_df, comp_data = KEGG_mzMass, mz_colname = 'basemass', mz_tolerence_unit= 'ppm', mz_tolerence = 20, numcores = 4)

Merge identified data with restrutured camera output

metab_ident <- merge_identified_with_restructured_camera(identified_df, restructure_camera$combined)

Summary of Annotation

p <- plot_hist_elements_frequency(restructure_camera$combined$pcgroup, frequency_type = "by_occurrence", 
                                  plot_title = "Number of features vs counts of pcgroup",
                                  xaxis_title = "Number of features",
                                  yaxis_title = "Counts of pcgroup")

Make group summary format from identification step output format

metscape <- make_group_summary_from_metab_ident_format(identified_df)