Calculate the PPM error of precursors

it is making a list of all library metabolites and assigning proper sample ID to each based on desired PPM error.

check_mass(
  reference = NULL,
  feature_data = NULL,
  unaccept_flag = 20,
  accept_flag = 5,
  suffer_flag = 10,
  filter = TRUE
)

Arguments

reference

A list of library contain mz(mass-to-charge ratio) with specific ID.

feature_data

A list of sample data contain mz(mass-to-charge ratio) with specific ID.

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"

filter

whether to remove unacceptable associations

Value

A list of library ID each contain a data frame of sample ID with a range of PPM error less than unacceptable flag

Details

Calculate the PPM error of precursors and assign a flag according to its value.