Metabolite Identification Run the metabolite identification pipeline

Metabolite 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 = FALSE,
  lib_ann_fields = c("ID", "Name", "PubChemCID"),
  dirout = NULL
)

Arguments

mRList

mRList object

features

the feaatures to be annotated. If NULL all features in the mRList will be annotated

library_list

library data obtained from margherita_library() function

rt_err

threshold over the retention time error

rt_best_thr

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".

unaccept_flag

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.

accept_flag

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"

suffer_flag

A number with default value of 10. PM errors above this value and < unaccept_flag will be tagged as "suffer"

min_RI

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.

ppm_err

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.

RI_err

maximum absolute RI difference between MS/MS peaks of sample and library

RI_err_type

type of RI error calculation.

filter_ann

whether to filter metabolite-feature associations or not.

lib_ann_fields

columns of library_list$lib_precursor that will be added to metabolite annotations

dirout

output directory

Value

data.frame with matches between features and library metabolites