metabolite_identification.RdMetabolite Identification Run the metabolite identification pipeline
metabolite_identification(
mRList = NULL,
features = NULL,
library_list = NULL,
rt_err = 1,
rt_best_thr = 0.5,
unaccept_flag = 20,
accept_flag = 5,
suffer_flag = 10,
min_RI = 10,
ppm_err = 20,
RI_err = 20,
RI_err_type = "rel",
filter_ann = TRUE,
dirout = NULL
)mRList object
the feaatures to be annotated. If NULL all features in the mRList will be annotated
library data obtained from margherita_library() function
threshold over the retention time error
Features with RT error < rt_best_thr will be labelled as "super", those with rt_best_thr < RT error <= rt_err_thr as "acceptable", the remaining as "unacceptable".
A number with default value of 15. The maximum PPM error must be less than this value. and those above this number will be eliminated.
A number with default value of 5. PPM errors < accept_flag will be tagged as "super", while those > accept_flag and < suffer_flag will be tagged as "acceptable"
A number with default value of 10. PM errors above this value and < unaccept_flag will be tagged as "suffer"
numeric parameter. the default value is 10. it is a maximum relative intensity that is kept in sample. since low intense peaks could be noise, it is filtering sample dataset by deleting the relative intensity lower then accept_RI.
A number with default value of 20. The maximum PPM error must be less than this value. and those above this number will be eliminated.
maximum absolute RI difference between MS/MS peaks of sample and library
type of RI error calculation.
whether to filter metabolite-feature associations or not.
output directory
data.frames mRList$metabolite_identification$associations and mRList$metabolite_identification$associations_summary; it also adds information to mRList$metab_ann and mRList$data_ann. Collectively, such data.frames contain the following colums:
Name: metabolite name;
Level: annotation level;
Level_note: annotation level note;
RT_err: retention time error;
ppm_error: ppm error;
peaks_found_ppm_RI: number of MS/MS peaks that match;
matched_peaks_ratio: ratio between the number of MS/MS peaks that match and the number of peaks in the reference MS/MS spectra