- Get compound tables from databases
- Expand adducts
- Annotate peaklist
- Interactive Browser
- Sources and licenses
- Journal References
The purpose of this package is to help identification in metabolomics by:
- annotating an XCMS (or other) peaklist with the compounds from the compound databases
- 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.
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 <[email protected]>"
)
## 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 |
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 |
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$anno[[1]] %>% slice(1:3) %>% kable| compound_id | compound_name | inchi | formula | mass | adduct | mz | ppm |
|---|---|---|---|---|---|---|---|
| HMDB0029579 | Diisopropyl sulfide | InChI=1S/C6H14S/c1-5(2)7-6(3)4/h5-6H,1-4H3 | C6H14S | 118.0816 | [M+H]+ | 119.0889 | 23.323984 |
| HMDB0029667 | 2,3,6-Trimethylphenol | InChI=1S/C9H12O/c1-6-4-5-7(2)9(10)8(6)3/h4-5,10H,1-3H3 | C9H12O | 136.0888 | [M+H-H2O]+ | 119.0855 | -4.981809 |
| HMDB0031312 | Benzyl ethyl ether | InChI=1S/C9H12O/c1-2-10-8-9-6-4-3-5-7-9/h3-7H,2,8H2,1H3 | C9H12O | 136.0888 | [M+H-H2O]+ | 119.0855 | -4.981809 |
Before we are ready to explore the peaklist interactively there are a few things we need to do and some optional things to fix:
library(tidyr)
## Warning: package 'tidyr' was built under R version 3.4.2
peaklist_anno_nest <- peaklist_anno %>%
mutate(rt=round(rt/60,2), mz = round(mz,4)) %>% # peaklist rt in minutes and round
select(mz, rt, isotopes, adduct, anno, pcgroup) %>% # get only relevant info
mutate(anno = map(anno,~ mutate(..1, mz = round(mz, 4), mass = round(mass, 4), ppm = round(ppm,0)))) %>% # round values in annotation tables
nest(-pcgroup, .key = "features") %>% # this is required! We nest the tables by the pcgroup
mutate(avg_rt = map_dbl(features,~round(mean(..1$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")
Now we can start the browser!
peaklist_browser(peaklist_anno_nest_ready, collapse_col = "features", modal_col = "anno")
- LipidBlast: Downloaded from MassBank of North America (MoNA) http://mona.fiehnlab.ucdavis.edu/downloads under the CC BY 4 license.
- LipidMaps: Downloaded from http://www.lipidmaps.org/resources/downloads No data included in this package due to licensing issues.
- 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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