View source: R/annotate_metabolites_mass_dataset.R
annotate_metabolites_mass_dataset | R Documentation |
Identify metabolites based on MS1 or MS/MS database.
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 )
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 |
A metIdentifyClass object.
Xiaotao Shen shenxt1990@outlook.com
The example and demo data of this function can be found https://tidymass.github.io/metid/articles/metid.html
## 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)
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