annotate_metabolites_mass_dataset: Identify metabolites based on MS1 or MS/MS database

In tidymass/metid: Metabolite identification based on MS1 and MS2 spectra

Identify metabolites based on MS1 or MS/MS database

Description

Identify metabolites based on MS1 or MS/MS database.

Usage

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
)

Arguments

object	A mass_dataset class obejct.
ms1.match.ppm	Precursor match ppm tolerance.
ms2.match.ppm	Fragment ion match ppm tolerance.
mz.ppm.thr	Accurate mass tolerance for m/z error calculation.
ms2.match.tol	MS2 match (MS2 similarity) tolerance.
fraction.weight	The weight for matched fragments.
dp.forward.weight	Forward dot product weight.
dp.reverse.weight	Reverse dot product weight.
remove_fragment_intensity_cutoff	remove_fragment_intensity_cutoff
rt.match.tol	RT match tolerance.
polarity	The polarity of data, "positive"or "negative".
ce	Collision energy. Please confirm the CE values in your database. Default is "all".
column	"hilic" (HILIC column) or "rp" (reverse phase).
ms1.match.weight	The weight of MS1 match for total score calculation.
rt.match.weight	The weight of RT match for total score calculation.
ms2.match.weight	The weight of MS2 match for total score calculation.
total.score.tol	Total score tolerance. The total score are referring to MS-DIAL.
candidate.num	The number of candidate.
database	MS2 database name or MS database.
threads	Number of threads

Value

A metIdentifyClass object.

Author(s)

Xiaotao Shen [e-mail]

See Also

The example and demo data of this function can be found https://tidymass.github.io/metid/articles/metid.html

Examples

## Not run: 
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))

## End(Not run)

tidymass/metid documentation built on Nov. 22, 2022, 6:31 p.m.