Identify peak tables with multiple databases

Some time we have multiple databases, so we can use identify_metabolite_all() functions to identify metabolites using multiple database.

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).

MS2 data preparation

The raw MS2 data from DDA or DIA should be transfered to msp, mgf or mzXML format files using ProteoWizard software.

Database

The database must be generated using construct_database() function. Here we use the databases in metid packages.

snyder_database_rplc0.0.3: A in-house database, it contains m/z, RT and MS2 spectra information.
orbitrap_database0.0.3: A public database from MassBank. It contains m/z and MS2 spectra information.
hmdb_ms1_database0.0.3: A public database from HMDB, only contains m/z information.

Data organization

Place the MS1 peak table, MS2 data and databases which you want to use in one folder like below figure shows:

Run `identify_metabolite_all()` function

We use the demo data in metid package.

Load demo data

First we load the MS1 peak, MS2 data and databases from metid package and then put them in a example folder.

library(metid)
library(tidyverse)

##creat a folder nameed 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 MS2 data from metid
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
)

##get databases from metid
data("snyder_database_rplc0.0.3")
data("orbitrap_database0.0.3")

save(snyder_database_rplc0.0.3, file = file.path(path, "snyder_database_rplc0.0.3"))
save(orbitrap_database0.0.3, file = file.path(path, "orbitrap_database0.0.3"))

Now in your ./example, there are files files, namely ms1.peak.table.csv, QC1_MSMS_NCE25.mgf and three databases.

Set parameter list

We need to use identify_metabolites_params() functions to set parameter list for each database.

param1 <-
  identify_metabolites_params(
    ms1.match.ppm = 15,
    rt.match.tol = 15,
    polarity = "positive",
    ce = "all",
    column = "rp",
    total.score.tol = 0.5,
    candidate.num = 3,
    threads = 3,
    database = "snyder_database_rplc0.0.3"
  )

param2 <- identify_metabolites_params(
  ms1.match.ppm = 15,
  rt.match.tol = 15,
  polarity = "positive",
  ce = "all",
  column = "rp",
  total.score.tol = 0.5,
  candidate.num = 3,
  threads = 3,
  database = "orbitrap_database0.0.3"
)

param3 <- identify_metabolites_params(
  ms1.match.ppm = 15,
  rt.match.tol = 15,
  polarity = "positive",
  ce = "all",
  column = "rp",
  total.score.tol = 0.5,
  candidate.num = 3,
  threads = 3,
  database = "hmdb_ms1_database0.0.3"
)

Note: You can set different parametes for each database.

Metabolite identification

All the parameters for three databases should be provided to parameter.list.

result <- identify_metabolite_all(
  ms1.data = "ms1.peak.table.csv",
  ms2.data = "QC1_MSMS_NCE25.mgf",
  parameter.list = c(param1, param2, param3),
  path = path
)

Note: result is a list, and each element is a metIdentifyClass object. So you can use the functions for metIdentifylass object to process it.

Integrate annotation results from different databases

After we get the annotation result list, we then can integrate the annotation results from different databases.

For snyder_database_rplc0.0.3, the annotaiton results are Level 1 according to MSI.

result[[1]]

Then get the annotation table.

annotation_table1 <-
  get_identification_table(result[[1]], type = "new", candidate.num = 1)
annotation_table1 %>%
  head()

For orbitrap_database0.0.3, the annotaiton results are Level 2 according to MSI.

result[[2]]

Then get the annotation table.

annotation_table2 <-
  get_identification_table(result[[2]], type = "new", candidate.num = 1)
annotation_table2 %>%
  head()

For hmdb_ms1_database0.0.3, the annotaiton results are Level 3 according to MSI.

result[[3]]

Then get the annotation table.

annotation_table3 <-
  get_identification_table(result[[3]], type = "new", candidate.num = 1)

annotation_table3 %>%
  head()

Then we should combine them together:

annotation_table1 <-
  annotation_table1 %>%
  dplyr::filter(!is.na(Compound.name))

dim(annotation_table1)

annotation_table1 <-
  data.frame(annotation_table1,
             Level = 1,
             stringsAsFactors = FALSE)

annotation_table2 <-
  annotation_table2 %>%
  dplyr::filter(!is.na(Compound.name))

dim(annotation_table2)

annotation_table2 <-
  data.frame(annotation_table2,
             Level = 2,
             stringsAsFactors = FALSE)

annotation_table3 <- 
  annotation_table3 %>% 
  dplyr::filter(!is.na(Compound.name))

dim(annotation_table3)

annotation_table3 <- 
  data.frame(annotation_table3, 
             Level = 3, 
             stringsAsFactors = FALSE)

If one peak have annotation from three different database, we only contains the annotations with higher confidence.

annotation_table2 <- 
  annotation_table2 %>% 
  dplyr::filter(!(name %in% annotation_table1$name))

annotation_table <- 
  rbind(annotation_table1,
        annotation_table2)

annotation_table3 <- 
  annotation_table3 %>% 
  dplyr::filter(!(name %in% annotation_table$name))

annotation_table <- 
  rbind(annotation_table,
        annotation_table3)

The annotation_table is the final annotation table.

Then we can output it as csv file.

readr::write_csv(annotation_table,file = file.path(path, "annotation_table.csv"))

Session information

sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur ... 10.16
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> loaded via a namespace (and not attached):
#>  [1] rprojroot_2.0.3   digest_0.6.29     R6_2.5.1          jsonlite_1.8.0   
#>  [5] magrittr_2.0.3    evaluate_0.15     stringi_1.7.8     rlang_1.0.5      
#>  [9] cachem_1.0.6      cli_3.3.0         rstudioapi_0.14   fs_1.5.2         
#> [13] jquerylib_0.1.4   bslib_0.3.1       ragg_1.2.2        rmarkdown_2.14   
#> [17] pkgdown_2.0.6     textshaping_0.3.6 desc_1.4.1        tools_4.2.1      
#> [21] stringr_1.4.1     purrr_0.3.4       yaml_2.3.5        xfun_0.31        
#> [25] fastmap_1.1.0     compiler_4.2.1    systemfonts_1.0.4 memoise_2.0.1    
#> [29] htmltools_0.5.2   knitr_1.39        sass_0.4.1

Xiaotao Shen PhD (https://www.shenxt.info/)

Created on 2020-03-28 and updated on 2022-09-19

MS1 data preparation

MS2 data preparation

Database

Data organization

Run `identify_metabolite_all()` function

Load demo data

Set parameter list

Metabolite identification

Integrate annotation results from different databases

Session information

Identify peak tables with multiple databases

Xiaotao Shen PhD (https://www.shenxt.info/)

Created on 2020-03-28 and updated on 2022-09-19

MS1 data preparation

MS2 data preparation

Database

Data organization

Run identify_metabolite_all() function

Load demo data

Set parameter list

Metabolite identification

Integrate annotation results from different databases

Session information

Run `identify_metabolite_all()` function