Annotates vector of m/z values

This function assigns each valid m/z peak with one/multiple metabolite names based on the mass difference between the observed value and the theoretical value documented in the reference database. This function is to be used when dealing with large datasets as a preprocessing step. Users can annotate m/z values first and then subset their data accordinly before loading it into a SpaMTP Seurat Object.

Usage

AnnotateBigData(
  mzs,
  db = NULL,
  ppm_error = NULL,
  adducts = NULL,
  polarity = NULL,
  tof_resolution = 30000,
  verbose = TRUE,
  maldi_matrix = NULL,
  ...
)

Arguments

mzs

Vector containing m/z values for annotation.

db

Reference metabolite dataset in the form of a data.frame. SpaMTP provides four pre-cleaned databases (HMDB_db, Lipidmaps_db, Chebi_db, GNPS_db). May be NULL when a pre-built index is supplied through ....

ppm_error

Mass tolerance in ppm. If NULL, a strict 5 ppm maximum is used (or a smaller value inferred from tof_resolution). Set to zero for exact numerical matches.

adducts

Optional adduct names/notations; see AdductRules(). If NULL, use the complete rule space selected from maldi_matrix, or all validated general rules for the selected polarity when no matrix is given.

polarity

Character string defining the ion mode. When NULL, infer it from a supplied index or MALDI matrix profile, otherwise use positive.

tof_resolution

Instrument resolving power retained for compatibility; it can only tighten, not widen, the default 5 ppm mass-accuracy threshold.

verbose

Boolean indicating whether to show the message. If TRUE the message will be show, else the message will be suppressed (default = TRUE).

maldi_matrix

Optional MALDI matrix/reagent profile used for automatic rule selection. The adducts argument remains an optional restriction.

...

Additional indexed annotation/scoring arguments passed to annotateTable(), such as index, rules, or ms1_spectrum.

Value

A data.frame containing all successfully annotated m/z values, with their corresponding annotation.

Examples

#cardinal <- readImzML("./Test_Data/Spotted/test_data1")
#mzs <- data.frame(Cardinal::featureData(cardinal))$mz
#results <- AnnotateBigData(mzs, db = HMDB_db, ppm_error = 3, adducts = c("M-H", "M+Cl"), polarity = "negative")
#cardinal_subset <- Cardinal::subset(cardinal, mz %in% results$observed_mz)
#SpaMTP_data <- CardinalToSeurat(cardinal_subset)