R/12_annotate_metabolites_mass_dataset.R
annotate_metabolites_mass_dataset.RdThis function performs metabolite annotation for a `mass_dataset` object based on MS1 and MS2 data. It matches the mass-to-charge ratio (m/z), retention time (RT), and MS2 spectra with a reference database to identify potential metabolites.
annotate_metabolites_mass_dataset(
object,
ms1.match.ppm = 25,
ms2.match.ppm = 30,
mz.ppm.thr = 400,
ms2.match.tol = 0.5,
fraction.weight = 0.3,
dp.forward.weight = 0.6,
dp.reverse.weight = 0.1,
remove_fragment_intensity_cutoff = 0,
rt.match.tol = 30,
polarity = c("positive", "negative"),
ce = "all",
column = c("rp", "hilic"),
ms1.match.weight = 0.25,
rt.match.weight = 0.25,
ms2.match.weight = 0.5,
total.score.tol = 0.5,
candidate.num = 3,
database,
threads = 3
)A `mass_dataset` object that contains MS1 and MS2 data.
A numeric value specifying the mass accuracy threshold for MS1 matching in parts per million (ppm). Defaults to `25`.
A numeric value specifying the mass accuracy threshold for MS2 (Fragment ion) matching in ppm. Defaults to `30`.
A numeric value specifying the m/z threshold in ppm for matching MS1 and MS2. Defaults to `400`.
A numeric value specifying the tolerance for MS2 fragment ion matching. Defaults to `0.5`.
A numeric value specifying the weight for the MS2 fragmentation score. Defaults to `0.3`.
A numeric value specifying the weight for the forward dot product in MS2 matching. Defaults to `0.6`.
A numeric value specifying the weight for the reverse dot product in MS2 matching. Defaults to `0.1`.
A numeric value specifying the intensity cutoff for removing fragments in MS2 matching. Defaults to `0`.
A numeric value specifying the retention time matching tolerance in seconds. Defaults to `30`.
A character string specifying the ionization mode. It can be either `"positive"` or `"negative"`. Defaults to `"positive"`.
A character string specifying the collision energy for MS2 matching. Defaults to `"all"`.
A character string specifying the chromatographic column type, either `"rp"` (reverse phase) or `"hilic"`. Defaults to `"rp"`.
A numeric value specifying the weight of MS1 matching in the total score calculation. Defaults to `0.25`.
A numeric value specifying the weight of RT matching in the total score calculation. Defaults to `0.25`.
A numeric value specifying the weight of MS2 matching in the total score calculation. Defaults to `0.5`.
A numeric value specifying the threshold for the total score. Defaults to `0.5`.
A numeric value specifying the number of top candidates to retain per feature. Defaults to `3`.
A `databaseClass` object containing the reference spectral database for annotation.
An integer specifying the number of threads to use for parallel processing. Defaults to `3`.
A `mass_dataset` object with an updated annotation table containing the metabolite identification results.
This function uses both MS1 and MS2 data (if available) to identify metabolites by matching experimental features with a reference spectral database. If no MS2 data is available, only m/z and RT are used for matching. The matching process is controlled by parameters like `ms1.match.ppm`, `ms2.match.ppm`, `rt.match.tol`, and various weighting factors.
The function supports both positive and negative ionization modes and allows for fine-tuning of the matching process with customizable thresholds and weights. The number of top candidates to retain per feature can be controlled with `candidate.num`.
if (FALSE) { # \dontrun{
library(massdataset)
library(magrittr)
library(dplyr)
ms1_data =
readr::read_csv(file.path(
system.file("ms1_peak", package = "metid"),
"ms1.peak.table.csv"
))
ms1_data = data.frame(ms1_data, sample1 = 1, sample2 = 2)
expression_data = ms1_data %>%
dplyr::select(-c(name:rt))
variable_info =
ms1_data %>%
dplyr::select(name:rt) %>%
dplyr::rename(variable_id = name)
sample_info =
data.frame(
sample_id = colnames(expression_data),
injection.order = c(1, 2),
class = c("Subject", "Subject"),
group = c("Subject", "Subject")
)
rownames(expression_data) = variable_info$variable_id
object = create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info
)
object
data("snyder_database_rplc0.0.3", package = "metid")
database = snyder_database_rplc0.0.3
object1 =
annotate_metabolites_mass_dataset(object = object,
database = snyder_database_rplc0.0.3)
head(extract_annotation_table(object1))
} # }