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Peaklist Annotator and Browser

The purpose of this package is to help identification in metabolomics by:

  1. annotating an XCMS (or other) peaklist with the compounds from the compound databases
  2. displaying an interactive browseable peaktable with the annotated compounds in nested tables

This package is in an early alpha stage. It will need to integrate better with CompoundDb and require less "manual" data wrangling. Will also be better when the database files can be made avaialable directly.



Get compound tables from databases

We use the new package CompoundDb

library(dplyr)
library(PeakABro)
library(CompoundDb)
# Read the HMDB SDF file.
# You need to download this from the HMDB website.
hmdb_tab <- compound_tbl_sdf("inst/extdata/HMDB.sdf")


hmdb_meta <- make_metadata(source = "HMDB", 
                           url = "http://www.hmdb.ca",
                           source_version = "4.0", 
                           source_date = "2017-09-10",
                           organism = "Hsapiens"
                           )


hmdb_db <- createCompDb(hmdb_tab, metadata = hmdb_meta, path = tempdir())

Now we can craete our own package with this data and install it. You only need to do this once.

createCompDbPackage(
    hmdb_db, 
    version = "0.0.1", 
    author = "Jan Stanstrup", 
    path = tempdir(),
    maintainer = "Jan Stanstrup <stanstrup@gmail.com>"
)
## Creating package in C:\Users\tmh331\AppData\Local\Temp\RtmpuglabL/CompDb.Hsapiens.HMDB.4.0


library(devtools)
install_local(paste0(tempdir(),"/CompDb.Hsapiens.HMDB.4.0"))

Now we can load this package and get the data.

library(CompDb.Hsapiens.HMDB.4.0)

HMDB_tbl <- compounds(CompDb.Hsapiens.HMDB.4.0, return.type = "tibble")

HMDB_tbl %>% 
    slice(1:3) %>% 
    kable
compound_id compound_name inchi formula mass
HMDB0000001 1-Methylhistidine InChI=1S/C7H11N3O2/c1-10-3-5(9-4-10)2-6(8)7(11)12/h3-4,6H,2,8H2,1H3,(H,11,12)/t6-/m0/s1 C7H11N3O2 169.0851
HMDB0000002 1,3-Diaminopropane InChI=1S/C3H10N2/c4-2-1-3-5/h1-5H2 C3H10N2 74.0844
HMDB0000005 2-Ketobutyric acid InChI=1S/C4H6O3/c1-2-3(5)4(6)7/h2H2,1H3,(H,6,7) C4H6O3 102.0317

This takes rather long because the databases are quite large. Therefore I try to supply pre-parsed data. So far only LipidBlast is available. This will be moved to a seperate package shortly (also there seems to be a bug in LipidBlast ATM that causes wrong info).

lipidblast_tbl <- readRDS(system.file("extdata", "lipidblast_tbl.rds", package="PeakABro"))
lipidblast_tbl %>% slice(1:3) %>% kable
id name inchi formula mass
LipidBlast000001 CerP 24:0; CerP(d14:0/10:0) InChI=1S/C24H50NO6P/c1-3-5-7-9-11-12-14-15-17-19-23(26)22(21-31-32(28,29)30)25-24(27)20-18-16-13-10-8-6-4-2/h22-23,26H,3-21H2,1-2H3,(H,25,27)(H2,28,29,30)/t22-,23+/m0/s1 C24H50NO6P 479.3376
LipidBlast000002 CerP 26:0; CerP(d14:0/12:0) InChI=1S/C26H54NO6P/c1-3-5-7-9-11-13-15-17-19-21-25(28)24(23-33-34(30,31)32)27-26(29)22-20-18-16-14-12-10-8-6-4-2/h24-25,28H,3-23H2,1-2H3,(H,27,29)(H2,30,31,32)/t24-,25+/m0/s1 C26H54NO6P 507.3689
LipidBlast000003 CerP 28:0; CerP(d14:0/14:0) InChI=1S/C28H58NO6P/c1-3-5-7-9-11-13-14-16-18-20-22-24-28(31)29-26(25-35-36(32,33)34)27(30)23-21-19-17-15-12-10-8-6-4-2/h26-27,30H,3-25H2,1-2H3,(H,29,31)(H2,32,33,34)/t26-,27+/m0/s1 C28H58NO6P 535.4002



Expand adducts

Lets use only HMDB for now.

cmp_tbl_exp_pos <- expand_adducts(HMDB_tbl, mode = "pos", adducts = c("[M+H]+", "[M+Na]+", "[2M+H]+", "[M+K]+", "[M+H-H2O]+"))

cmp_tbl_exp_pos %>% slice(1:3) %>% kable
compound_id compound_name inchi formula mass adduct charge nmol mz mode
HMDB0000001 1-Methylhistidine InChI=1S/C7H11N3O2/c1-10-3-5(9-4-10)2-6(8)7(11)12/h3-4,6H,2,8H2,1H3,(H,11,12)/t6-/m0/s1 C7H11N3O2 169.0851 [M+H]+ 1 1 170.0924 pos
HMDB0000001 1-Methylhistidine InChI=1S/C7H11N3O2/c1-10-3-5(9-4-10)2-6(8)7(11)12/h3-4,6H,2,8H2,1H3,(H,11,12)/t6-/m0/s1 C7H11N3O2 169.0851 [2M+H]+ 1 2 339.1775 pos
HMDB0000001 1-Methylhistidine InChI=1S/C7H11N3O2/c1-10-3-5(9-4-10)2-6(8)7(11)12/h3-4,6H,2,8H2,1H3,(H,11,12)/t6-/m0/s1 C7H11N3O2 169.0851 [M+Na]+ 1 1 192.0743 pos



Annotate peaklist

We ultimately want to use the compound table in an interactive browser so lets remove some redundant info and take only one mode.

cmp_tbl_exp_pos <- cmp_tbl_exp_pos %>% 
                   filter(mode=="pos") %>% # if you generated also neg above
                   select(-charge, -nmol, -mode)
                   

Next we load a sample peaklist. I have removed the data columns in this sample.

library(readr)
peaklist <- read_tsv(system.file("extdata", "peaklist_pos.tsv", package="PeakABro"))
## Parsed with column specification:
## cols(
##   mz = col_double(),
##   mzmin = col_double(),
##   mzmax = col_double(),
##   rt = col_double(),
##   rtmin = col_double(),
##   rtmax = col_double(),
##   npeaks = col_integer(),
##   isotopes = col_character(),
##   adduct = col_character(),
##   pcgroup = col_integer()
## )

peaklist %>% slice(1:3) %>% kable
mz mzmin mzmax rt rtmin rtmax npeaks isotopes adduct pcgroup
119.0861 119.0856 119.0873 480.2640 479.7749 480.6898 83 NA NA 48
129.0548 129.0544 129.0552 682.1079 681.5382 682.5982 94 NA [M+H-H2O]+ 146.058 10
147.0654 147.0649 147.0657 682.1068 681.5382 682.5982 84 NA [M+H]+ 146.058 10

Now we can annotate the table. The idea here is that each row will have a nested table with annotations from the compound table.

library(purrr)
peaklist_anno <- peaklist %>% mutate(anno = map(mz, cmp_mz_filter, cmp_tbl_exp_pos, ppm=30))

How the peaktable looks like this:

peaklist_anno %>% select(-mzmin, -mzmax, -rtmin, -rtmax, -npeaks)
## # A tibble: 49 x 6
##          mz       rt   isotopes
##       <dbl>    <dbl>      <chr>
##  1 119.0861 480.2640       <NA>
##  2 129.0548 682.1079       <NA>
##  3 147.0654 682.1068       <NA>
##  4 247.2419 796.0284       <NA>
##  5 259.1905 682.0464       <NA>
##  6 265.2527 795.9556   [31][M]+
##  7 266.2557 795.9540 [31][M+1]+
##  8 308.2944 796.7262       <NA>
##  9 321.2777 796.0501       <NA>
## 10 339.3451 795.8738       <NA>
## # ... with 39 more rows, and 3 more variables: adduct <chr>,
## #   pcgroup <int>, anno <list>

And one of the nested tables look like this:

peaklist_anno$anno[[1]] %>% slice(1:3) %>% kable
compound_id compound_name inchi formula mass adduct mz ppm
HMDB0029579 Diisopropyl sulfide InChI=1S/C6H14S/c1-5(2)7-6(3)4/h5-6H,1-4H3 C6H14S 118.0816 [M+H]+ 119.0889 23.323984
HMDB0029667 2,3,6-Trimethylphenol InChI=1S/C9H12O/c1-6-4-5-7(2)9(10)8(6)3/h4-5,10H,1-3H3 C9H12O 136.0888 [M+H-H2O]+ 119.0855 -4.981809
HMDB0031312 Benzyl ethyl ether InChI=1S/C9H12O/c1-2-10-8-9-6-4-3-5-7-9/h3-7H,2,8H2,1H3 C9H12O 136.0888 [M+H-H2O]+ 119.0855 -4.981809



Interactive Browser

Prepare the table for interactive browser

Before we are ready to explore the peaklist interactively there are a few things we need to do and some optional things to fix:

library(tidyr)
## Warning: package 'tidyr' was built under R version 3.4.2

peaklist_anno_nest <- peaklist_anno %>%
                        mutate(rt=round(rt/60,2), mz = round(mz,4)) %>% # peaklist rt in minutes and round 
                        select(mz, rt, isotopes, adduct, anno, pcgroup) %>% # get only relevant info
                        mutate(anno = map(anno,~ mutate(..1, mz = round(mz, 4), mass = round(mass, 4), ppm = round(ppm,0)))) %>% # round values in annotation tables
                        nest(-pcgroup, .key = "features") %>% # this is required! We nest the tables by the pcgroup
                        mutate(avg_rt = map_dbl(features,~round(mean(..1$rt),2))) %>% # extract average mass for each pcgroup
                        select(pcgroup, avg_rt, features) # putting the nested table last. ATM needed for the browser to work
    

We are almost there but first we want to add some magic to the table for the interaction (adds + button and View button).

peaklist_anno_nest_ready <- peaklist_browser_prep(peaklist_anno_nest, collapse_col = "features", modal_col = "anno")



Interactively browse peaklist

Now we can start the browser!

peaklist_browser(peaklist_anno_nest_ready, collapse_col = "features", modal_col = "anno")

Peaklist Browser



Sources and licenses



Journal References

  • Kind T, Liu K-H, Lee DY, DeFelice B, Meissen JK, Fiehn O. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat. Methods. 2013 Aug;10(8):755–8. http://dx.doi.org/10.1038/nmeth.2551
  • Dennis EA, Brown HA, Deems RA, Glass CK, Merrill AH, Murphy RC, et al. The LIPID MAPS Approach to Lipidomics. Funct. Lipidomics. CRC Press; 2005. p. 1–15. http://dx.doi.org/10.1201/9781420027655.ch1

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