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

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)

---

Data preparation

library(massdataset)
library(tidyverse)
library(metid)

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

Add MS2 to mass_dataset object

path = "./example"
dir.create(path)

ms2_data <- system.file("ms2_data", package = "metid")
file.copy(
  from = file.path(ms2_data, "QC1_MSMS_NCE25.mgf"),
  to = path,
  overwrite = TRUE,
  recursive = TRUE
)

object =
  massdataset::mutate_ms2(
    object = object,
    column = "rp",
    polarity = "positive",
    ms1.ms2.match.mz.tol = 10,
    ms1.ms2.match.rt.tol = 30
  )

object

object@ms2_data

Identify metabolites according to MS2

data("snyder_database_rplc0.0.3", package = "metid")

object1 =
  annotate_metabolites_mass_dataset(object = object,
                                    database = snyder_database_rplc0.0.3)
head(object1@annotation_table)
head(extract_variable_info(object = object1))

MS2 plot

ms2_plot_mass_dataset(object = object1, variable_index = 1, database = snyder_database_rplc0.0.3)

ms2_plot_mass_dataset(object = object1, variable_index = 3, database = snyder_database_rplc0.0.3)

Session information

sessionInfo()

tidymass/metid documentation built on Oct. 8, 2024, 10:32 p.m.