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

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MS1 data preparation

The peak table must contain "name" (peak name), "mz" (mass to charge ratio) and "rt" (retention time, unit is second). It can be from any data processing software (XCMS, MS-DIAL and so on).


Database

The database must be generated using constructDatabase() function. You can also use the public databases we provoded here.

Data organization

Place the MS1 peak table and databases which you want to use in one folder like below figure shows:


Run identify_metabolites() function

We use the demo data in metid package to show how to use metid to identify metabolites without MS2 spectra.

Load demo data

First we load the MS1 peak and database from metid package and then put them in a example folder.

library(metid)
library(tidyverse)

##create a folder named as example
path <- file.path(".", "example")
dir.create(path = path, showWarnings = FALSE)

##get MS1 peak table from metid
ms1_peak <- system.file("ms1_peak", package = "metid")
file.copy(from = file.path(ms1_peak, "ms1.peak.table.csv"), 
          to = path, overwrite = TRUE, recursive = TRUE)

##get database from metid
data("snyder_database_rplc0.0.3", package = "metid")
save(snyder_database_rplc0.0.3, file = file.path(path, "snyder_database_rplc0.0.3"))

Now in your ./example, there are two files, namely ms1.peak.table.csv and msDatabase_rplc_0.0.2, respectively.

Only use m/z for metabolite identification

First, we only use m/z for metabolite identification.

annotate_result1 <- 
  identify_metabolites(ms1.data = "ms1.peak.table.csv", 
                       ms1.match.ppm = 15, 
                       rt.match.tol = 1000000, 
                       polarity = "positive", 
                       column = "rp", 
                       path = path, 
                       candidate.num = 3,
                       database = "snyder_database_rplc0.0.3", 
                       threads = 5)

Note: because here we only want to use m/z for metabolite identification, so please set rt.match.tol (second) > 10,000, for example '1000000' here, so the RT will not be used for filtering.

Other parameters:

• ms1.data: The ms1 peak table name.

• ms1.match.ppm: MS1 match tolerance (ppm).

• polarity: positive or negative.

• column: hilic or rp.

• path: Where are your data placaed?

• candidate.num: The candidate number for each peak.

• database: The database name or database.

• threads: How many threads you want to use.

The return result annotate_result1 is a metIdentifyClass object, you can directory get the brief information by print it in console:

annotate_result1

Note:

now we can also provide "databaseClass" object for "database" argument. For example: we load the database first.

snyder_database_rplc0.0.3

Then we can directory provide this database to identify_metabolites():

annotate_result2 <- 
  identify_metabolites(ms1.data = "ms1.peak.table.csv", 
                       ms1.match.ppm = 15, 
                       rt.match.tol = 1000000, 
                       polarity = "positive", 
                       column = "rp", 
                       path = path, 
                       candidate.num = 3,
                       database = snyder_database_rplc0.0.3, 
                       threads = 5)

But what should be noticed is that it have different name for database in the final result:

annotate_result1@database

annotate_result2@database

It is because that if you give the databaseClass, so identify_metabolites can know the name of database, if just use the source and version as the name for database.

paste([e-mail]$Source,
      [e-mail]$Version, 
      sep = "_")

Only use m/z and RT for metabolite identification

Here we set RT tolerance (rt.match.tol) as 30 s.

annotate_result2 <- 
  identify_metabolites(ms1.data = "ms1.peak.table.csv", 
                       ms1.match.ppm = 15, 
                       rt.match.tol = 30, 
                       polarity = "positive", 
                       column = "rp", 
                       path = path, 
                       candidate.num = 3,
                       database = "snyder_database_rplc0.0.3", 
                       threads = 5)

Get detailed annotation information

After get the annotation_result, we can get the detailed information from it.

Get the processing parameters

We can use get_parameters() function to get the detailed parameters. This is very useful for reproductive analysis for data analysis.

metid::get_parameters_metid(annotate_result1)

metid::get_parameters_metid(annotate_result2)

Check what peaks with annotations

Use which_has_identification() function to get what peaks have annotions.

which_has_identification(annotate_result1) %>% 
  head()

Because there are no ms2 data, so the peaks have no MS2 spectra.

Get the identification table

We can use get_identification_table() to get the identification table.

table1 <- 
  get_identification_table(annotate_result1, 
                           candidate.num = 3,
                           type = "old")
table1

The type is set as old. It means the identifications for each peak is shown as one character and seperated by {}. And the order is sorted by Total score.

You can also set type as new to get another style.

table2 <- 
  get_identification_table(annotate_result1, candidate.num = 3,
                           type = "new")
table2

If you only want to keep one cancidate for each peak. Please set candiate.num as 1.

table2 <- 
  get_identification_table(annotate_result1, candidate.num = 2,
                           type = "new")
table2

Get the identification information for single peak

We can use get_iden_info() function to get the detailed information for a sinlge peak. Because it gets the information from the database, so this function need provide the database.

First, we need to know what peaks have annotations.

which_has_identification(annotate_result1) %>% 
  head()

Then we can get the annotation for peak pRPLC_376 use get_iden_info() function.

get_iden_info(object = annotate_result1, 
              which.peak = "pRPLC_376", 
              database = snyder_database_rplc0.0.3)

We can get the detailed information for metabolites in database.

Filter identifications

After we get the annotation result use identify_metabolites() function. We can also use filter_identification() function to filter annotations based on m/z, rt and MS2 match tolerance.

annotate_result2_2 <- 
  filter_identification(object = annotate_result2, 
                        rt.match.tol = 5)

annotate_result2_2

annotate_result2

Session information

sessionInfo()

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