CompoundDb 1.6.0
Authors: Johannes Rainer
Modified: 2023-10-24 14:43:06.131727
Compiled: Tue Oct 24 16:46:51 2023
Chemical compound annotation and information can be retrieved from a variety of
sources including HMDB,
LipidMaps or
ChEBI. The CompoundDb package provides
functionality to extract data relevant for (chromatographic) peak annotations in
metabolomics/lipidomics experiments from these sources and to store it into a
common format (i.e. an CompDb object/database). This vignette describes how
such CompDb objects can be created exemplified with package-internal test
files that represent data subsets from some annotation resources.
The R object to represent the compound annotation is the CompDb object. Each
object (respectively its database) is supposed to contain and provide
annotations from a single source (e.g. HMDB or LipidMaps) but it is also
possible to create cross-source databases too.
CompDb databasesCompDb databases can be created from existing data resources such as the Human
Metabolome Database (HMDB) by importing all of their data or can alternatively
be build by sequentially adding data and information to the database. This
section explains first how data can be loaded from existing resources to create
a CompDb database and in the last subsection how an empty CompDb can be
sequentially and manually filled with annotation data 2.4.
The CompoundDb package provides the compound_tbl_sdf and the
compound_tbl_lipidblast functions to extract relevant compound annotation from
files in SDF (structure-data file) format or in the json files from LipidBlast
(http://mona.fiehnlab.ucdavis.edu/downloads). CompoundDb allows to process SDF
files from:
Note however that it is also possible to define such a table manually and use
that to create the database. As simple example for this is provided in the
section CompDb from custom input 2.2 below or the help page of
createCompDb for more details on that.
CompDb from HMDB dataBelow we use the compound_tbl_sdf to extract compound annotations from a SDF
file representing a very small subset of the HMDB database. To generate a
database for the full HMDB we would have to download the structures.sdf file
containing all metabolites and load that file instead.
library(CompoundDb)
## Locate the file
hmdb_file <- system.file("sdf/HMDB_sub.sdf.gz", package = "CompoundDb")
## Extract the data
cmps <- compound_tbl_sdf(hmdb_file)The function returns by default a (data.frame-equivalent) tibble (from the
tidyverse’s tibble package).
cmps## # A tibble: 9 × 8
## compound_id name inchi inchikey formula exactmass synonyms smiles
## <chr> <chr> <chr> <chr> <chr> <dbl> <named > <chr>
## 1 HMDB0000001 1-Methylhistidine InCh… BRMWTNU… C7H11N… 169. <chr> "CN1C…
## 2 HMDB0000002 1,3-Diaminopropa… InCh… XFNJVJP… C3H10N2 74.1 <chr> "NCCC…
## 3 HMDB0000005 2-Ketobutyric ac… InCh… TYEYBOS… C4H6O3 102. <chr> "CCC(…
## 4 HMDB0000008 2-Hydroxybutyric… InCh… AFENDNX… C4H8O3 104. <chr> "CCC(…
## 5 HMDB0000010 2-Methoxyestrone InCh… WHEUWNK… C19H24… 300. <chr> "[H][…
## 6 HMDB0000011 (R)-3-Hydroxybut… InCh… WHBMMWS… C4H8O3 104. <chr> "C[C@…
## 7 HMDB0000012 Deoxyuridine InCh… MXHRCPN… C9H12N… 228. <chr> "OC[C…
## 8 HMDB0004370 N-Methyltryptami… InCh… NCIKQJB… C11H14… 174. <chr> "CNCC…
## 9 HMDB0006719 5,6-trans-Vitami… InCh… QYSXJUF… C27H44O 384. <chr> "CC(C…The tibble contains columns
compound_id: the resource-specific ID of the compound. Can be an integer
or a character.name: the name of the compound, mostly a generic or common name.inchi: the compound’s inchi.inchikey: the INCHI key.formula: the chemical formula of the compound.exactmass: the compounds (monoisotopic) mass.synonyms: a list of aliases/synonyms for the compound.smiles: the SMILES of the compound.To create a simple compound database, we could pass this tibble along with
additional required metadata information to the createCompDb function. In the
present example we want to add however also MS/MS spectrum data to the
database. We thus load below the MS/MS spectra for some of the compounds from
the respective xml files downloaded from HMDB. To this end we pass the path to
the folder in which the files are located to the msms_spectra_hmdb
function. The function identifies the xml files containing MS/MS spectra based
on their their file name and loads the respective spectrum data. The folder can
therefore also contain other files, but the xml files from HMDB should not be
renamed or the function will not recognice them. Note also that at present only
MS/MS spectrum xml files from HMDB are supported (one xml file per spectrum);
these could be downloaded from HMDB with the hmdb_all_spectra.zip file.
## Locate the folder with the xml files
xml_path <- system.file("xml", package = "CompoundDb")
spctra <- msms_spectra_hmdb(xml_path)Also here, spectra information can be manually provided by adhering to the
expected structure of the data.frame (see ?createCompDb for details).
At last we have to create the metadata for the resource. The metadata
information for a CompDb resource is crucial as it defines the origin of the
annotations and its version. This information should thus be carefully defined
by the user. Below we use the make_metadata helper function to create a
data.frame in the expected format. The organism should be provided in the
format e.g. "Hsapiens" for human or "Mmusculus" for mouse, i.e. capital
first letter followed by lower case characters without whitespaces.
metad <- make_metadata(source = "HMDB", url = "http://www.hmdb.ca",
source_version = "4.0", source_date = "2017-09",
organism = "Hsapiens")With all the required data ready we create the SQLite database for the HMDB
subset. With path we specify the path to the directory in which we want to
save the database. This defaults to the current working directory, but for this
example we save the database into a temporary folder.
db_file <- createCompDb(cmps, metadata = metad, msms_spectra = spctra,
path = tempdir())The variable db_file is now the file name of the SQLite database. We can pass
this file name to the CompDb function to get the CompDb objects acting as
the interface to the database.
cmpdb <- CompDb(db_file)
cmpdb## class: CompDb
## data source: HMDB
## version: 4.0
## organism: Hsapiens
## compound count: 9
## MS/MS spectra count: 4To extract all compounds from the database we can use the compounds
function. The parameter columns allows to choose the database columns to
return. Any columns for any of the database tables are supported. To get an
overview of available database tables and their columns, the tables function
can be used:
tables(cmpdb)## $ms_compound
## [1] "compound_id" "name" "inchi" "inchikey" "formula"
## [6] "exactmass" "smiles"
##
## $msms_spectrum
## [1] "original_spectrum_id" "compound_id" "polarity"
## [4] "collision_energy" "predicted" "splash"
## [7] "instrument_type" "instrument" "precursor_mz"
## [10] "spectrum_id" "msms_mz_range_min" "msms_mz_range_max"
##
## $msms_spectrum_peak
## [1] "spectrum_id" "mz" "intensity" "peak_id"
##
## $synonym
## [1] "compound_id" "synonym"Below we extract only selected columns from the compounds table.
compounds(cmpdb, columns = c("name", "formula", "exactmass"))## name formula exactmass
## 1 (R)-3-Hydroxybutyric acid C4H8O3 104.0473
## 2 1,3-Diaminopropane C3H10N2 74.0844
## 3 1-Methylhistidine C7H11N3O2 169.0851
## 4 2-Hydroxybutyric acid C4H8O3 104.0473
## 5 2-Ketobutyric acid C4H6O3 102.0317
## 6 2-Methoxyestrone C19H24O3 300.1725
## 7 5,6-trans-Vitamin D3 C27H44O 384.3392
## 8 Deoxyuridine C9H12N2O5 228.0746
## 9 N-Methyltryptamine C11H14N2 174.1157Analogously we can use the Spectra function to extract spectrum data from the
database. The function returns by default a Spectra object from the
Spectra package with all spectra metadata available as
spectra variables.
library(Spectra)
sps <- Spectra(cmpdb)
sps## MSn data (Spectra) with 4 spectra in a MsBackendCompDb backend:
## msLevel precursorMz polarity
## <integer> <numeric> <integer>
## 1 NA NA 1
## 2 NA NA 1
## 3 NA NA 1
## 4 NA NA 0
## ... 32 more variables/columns.
## Use 'spectraVariables' to list all of them.
## data source: HMDB
## version: 4.0
## organism: HsapiensThe available spectra variables for the Spectra object can be retrieved with
spectraVariables:
spectraVariables(sps)## [1] "msLevel" "rtime"
## [3] "acquisitionNum" "scanIndex"
## [5] "dataStorage" "dataOrigin"
## [7] "centroided" "smoothed"
## [9] "polarity" "precScanNum"
## [11] "precursorMz" "precursorIntensity"
## [13] "precursorCharge" "collisionEnergy"
## [15] "isolationWindowLowerMz" "isolationWindowTargetMz"
## [17] "isolationWindowUpperMz" "compound_id"
## [19] "name" "inchi"
## [21] "inchikey" "formula"
## [23] "exactmass" "smiles"
## [25] "original_spectrum_id" "predicted"
## [27] "splash" "instrument_type"
## [29] "instrument" "spectrum_id"
## [31] "msms_mz_range_min" "msms_mz_range_max"
## [33] "synonym"Individual spectra variables can be accessed with the $ operator:
sps$collisionEnergy## [1] 10 25 NA 20And the actual m/z and intensity values with mz and intensity:
mz(sps)## NumericList of length 4
## [[1]] 109.2 124.2 124.5 170.16 170.52
## [[2]] 83.1 96.12 97.14 109.14 124.08 125.1 170.16
## [[3]] 44.1 57.9 61.4 71.2 73.8 78.3 78.8 ... 142.9 144.1 157.6 158 175.2 193.2
## [[4]] 111.0815386 249.2587746 273.2587746 ... 367.3006394 383.3319396## m/z of the 2nd spectrum
mz(sps)[[2]]## [1] 83.10 96.12 97.14 109.14 124.08 125.10 170.16Note that it is also possible to retrieve specific spectra, e.g. for a provided
compound, or add compound annotations to the Spectra object. Below we use the
filter expression ~ compound_id == "HMDB0000001"to get only MS/MS spectra for
the specified compound. In addition we ask for the "name" and
"inchikey" of the compound.
sps <- Spectra(cmpdb, filter = ~ compound_id == "HMDB0000001",
columns = c(tables(cmpdb)$msms_spectrum, "name",
"inchikey"))
sps## MSn data (Spectra) with 2 spectra in a MsBackendCompDb backend:
## msLevel precursorMz polarity
## <integer> <numeric> <integer>
## 1 NA NA 1
## 2 NA NA 1
## ... 32 more variables/columns.
## Use 'spectraVariables' to list all of them.
## data source: HMDB
## version: 4.0
## organism: HsapiensThe available spectra variables:
spectraVariables(sps)## [1] "msLevel" "rtime"
## [3] "acquisitionNum" "scanIndex"
## [5] "dataStorage" "dataOrigin"
## [7] "centroided" "smoothed"
## [9] "polarity" "precScanNum"
## [11] "precursorMz" "precursorIntensity"
## [13] "precursorCharge" "collisionEnergy"
## [15] "isolationWindowLowerMz" "isolationWindowTargetMz"
## [17] "isolationWindowUpperMz" "compound_id"
## [19] "name" "inchi"
## [21] "inchikey" "formula"
## [23] "exactmass" "smiles"
## [25] "original_spectrum_id" "predicted"
## [27] "splash" "instrument_type"
## [29] "instrument" "spectrum_id"
## [31] "msms_mz_range_min" "msms_mz_range_max"
## [33] "synonym"The compound’s name and INCHI key have thus also been added as spectra variables:
sps$inchikey## [1] "BRMWTNUJHUMWMS-LURJTMIESA-N" "BRMWTNUJHUMWMS-LURJTMIESA-N"To share or archive the such created CompDb database, we can also create a
dedicated R package containing the annotation. To enable reproducible research,
each CompDb package should contain the version of the originating data source
in its file name (which is by default extracted from the metadata of the
resource). Below we create a CompDb package from the generated database
file. Required additional information we have to provide to the function are the
package creator/maintainer and its version.
createCompDbPackage(
db_file, version = "0.0.1", author = "J Rainer", path = tempdir(),
maintainer = "Johannes Rainer <[email protected]>")## Creating package in /tmp/RtmpaoH0E3/CompDb.Hsapiens.HMDB.4.0The function creates a folder (in our case in a temporary directory) that can be
build and installed with R CMD build and R CMD INSTALL.
Special care should also be put on the license of the package that can be passed
with the license parameter. The license of the package and how and if the
package can be distributed will depend also on the license of the originating
resource.
CompDb from custom dataA CompDb database can also be created from custom, manually defined
annotations. To illustrate this we create below first a data.frame with some
arbitrary compound annotations. According to the ?createCompDb help page, the
data frame needs to have columns "compound_id", "name", "inchi",
"inchikey", "formula", "exactmass", "synonyms". All columns except
"compound_id" can also contain missing values. It is also possible to define
additional columns. Below we thus create a data.frame with some compound
annotations as well as additional columns. Note that all these annotations in
this example are for illustration purposes only and are by no means
real. Also, we don’t provide any information for columns "inchi",
"inchikey" and "formula" setting all values for these to NA.
cmps <- data.frame(
compound_id = c("CP_0001", "CP_0002", "CP_0003", "CP_0004"),
name = c("A", "B", "C", "D"),
inchi = NA_character_,
inchikey = NA_character_,
formula = NA_character_,
exactmass = c(123.4, 234.5, 345.6, 456.7),
compound_group = c("G01", "G02", "G01", "G03")
)Next we add also synonyms for each compound. This columns supports multiple values for each row.
cmps$synonyms <- list(
c("a", "AA", "aaa"),
c(),
c("C", "c"),
("d")
)We also need to define the metadata for our database, which we do with the
make_metadata function. With this information we can already create a first
rudimentary CompDb database that contains only compound annotations. We thus
create below our custom CompDb database in a temporary directory. We also
manually specify the name of our database with the dbFile parameter - if not
provided, the name of the database will be constructed based on information from
the metadata parameter. In a real-case scenario, path and dbFile should be
changed to something more meaningful.
metad <- make_metadata(source = "manually defined", url = "",
source_version = "1.0.0", source_date = "2022-03-01",
organism = NA_character_)
db_file <- createCompDb(cmps, metadata = metad, path = tempdir(),
dbFile = "CompDb.test.sqlite")We can now load this toy database using the CompDb function providing the full
path to the database file. Note that we load the database in read-write mode by
specifying flags = RSQLite::SQLITE_RW - by default CompDb will load
databases in read-only mode hence ensuring that the data within the database can
not be compromised. In our case we would however like to add more information to
this database later and hence we load it in read-write mode.
cdb <- CompDb(db_file, flags = RSQLite::SQLITE_RW)
cdb## class: CompDb
## data source: manually defined
## version: 1.0.0
## organism: NA
## compound count: 4We can now retrieve annotations from the database with the compound function.
compounds(cdb)## name inchi inchikey formula exactmass compound_group
## 1 A <NA> <NA> <NA> 123.4 G01
## 2 B <NA> <NA> <NA> 234.5 G02
## 3 C <NA> <NA> <NA> 345.6 G01
## 4 D <NA> <NA> <NA> 456.7 G03Or also search and filter the annotations.
compounds(cdb, filter = ~ name %in% c("B", "A"))## name inchi inchikey formula exactmass compound_group
## 1 A <NA> <NA> <NA> 123.4 G01
## 2 B <NA> <NA> <NA> 234.5 G02Next we would like to add also MS2 spectra data to the database. This could be
either done directly in the createCompDb call with parameter msms_spectra,
or with the insertSpectra function that allows to add MS2 spectra data to an
existing CompDb which can be provided as a Spectra object. We thus below
manually create a Spectra object with some arbitrary MS2 spectra -
alternatively, Spectra can be imported from a variety of input sources,
including MGF or MSP files using e.g. the MsBackendMgf or
MsBackendMsp packages.
#' Define basic spectra variables
df <- DataFrame(msLevel = 2L, precursorMz = c(124.4, 124.4, 235.5))
#' Add m/z and intensity information for each spectrum
df$mz <- list(
c(3, 20, 59.1),
c(2, 10, 30, 59.1),
c(100, 206, 321.1))
df$intensity <- list(
c(10, 13, 45),
c(5, 8, 9, 43),
c(10, 20, 400))
#' Create the Spectra object
sps <- Spectra(df)The Spectra object needs also to have a variable (column) called
"compound_id" which provides the information with which existing compound in
the database the spectrum is associated.
compounds(cdb, "compound_id")## compound_id
## 1 CP_0001
## 2 CP_0002
## 3 CP_0003
## 4 CP_0004sps$compound_id <- c("CP_0001", "CP_0001", "CP_0002")We can also add additional information to the spectra, such as the instrument.
sps$instrument <- "AB Sciex TripleTOF 5600+"And we can now add these spectra to our existing toy CompDb. Parameter
columns allows to specify which of the spectra variables should be stored
into the database.
cdb <- insertSpectra(cdb, spectra = sps,
columns = c("compound_id", "msLevel",
"precursorMz", "instrument"))
cdb## class: CompDb
## data source: manually defined
## version: 1.0.0
## organism: NA
## compound count: 4
## MS/MS spectra count: 3We have thus now a CompDb database with compound annotations and 3 MS2
spectra. We could for example also retrieve the MS2 spectra for the compound
with the name A from the database with:
Spectra(cdb, filter = ~ name == "A")## MSn data (Spectra) with 2 spectra in a MsBackendCompDb backend:
## msLevel precursorMz polarity
## <integer> <numeric> <integer>
## 1 2 124.4 NA
## 2 2 124.4 NA
## ... 31 more variables/columns.
## Use 'spectraVariables' to list all of them.
## data source: manually defined
## version: 1.0.0
## organism: NACompDb from MoNA dataMoNa (Massbank of North America) provides a large SDF file with all spectra
which can be used to create a CompDb object with compound information and
MS/MS spectra. Note however that MoNa is organized by spectra and the annotation
of the compounds is not consistent and normalized. Spectra from the same
compound can have their own compound identified and data that e.g. can differ in
their chemical formula, precision of their exact mass or other fields.
Similar to the example above, compound annotations can be imported with the
compound_tbl_sdf function while spectrum data can be imported with
msms_spectra_mona. In the example below we use however the import_mona_sdf
that wraps both functions to reads both compound and spectrum data from a SDF
file without having to import the file twice. As an example we use a small
subset from a MoNa SDF file that contains only 7 spectra.
mona_sub <- system.file("sdf/MoNa_export-All_Spectra_sub.sdf.gz",
package = "CompoundDb")
mona_data <- import_mona_sdf(mona_sub)## Warning: MoNa data can currently not be normalized and the compound table
## contains thus highly redundant data.As a result we get a list with a data.frame each for compound and spectrum
information. These can be passed along to the createCompDb function to create
the database (see below).
metad <- make_metadata(source = "MoNa",
url = "http://mona.fiehnlab.ucdavis.edu/",
source_version = "2018.11", source_date = "2018-11",
organism = "Unspecified")
mona_db_file <- createCompDb(mona_data$compound, metadata = metad,
msms_spectra = mona_data$msms_spectrum,
path = tempdir())We can now load and use this database, e.g. by extracting all compounds as shown below.
mona <- CompDb(mona_db_file)
compounds(mona)## name
## 1 Sulfachlorpyridazine
## 2 Sulfaclozine
## 3 Sulfadimidine
## 4 Sulfamethazine
## 5 Sulfamethazine
## inchi
## 1 InChI=1S/C10H9ClN4O2S/c11-9-5-6-10(14-13-9)15-18(16,17)8-3-1-7(12)2-4-8/h1-6H,12H2,(H,14,15)
## 2 InChI=1S/C10H9ClN4O2S/c11-9-5-13-6-10(14-9)15-18(16,17)8-3-1-7(12)2-4-8/h1-6H,12H2,(H,14,15)
## 3 InChI=1S/C12H14N4O2S/c1-8-7-9(2)15-12(14-8)16-19(17,18)11-5-3-10(13)4-6-11/h3-7H,13H2,1-2H3,(H,14,15,16)
## 4 InChI=1S/C12H14N4O2S/c1-8-7-9(2)15-12(14-8)16-19(17,18)11-5-3-10(13)4-6-11/h3-7H,13H2,1-2H3,(H,14,15,16)
## 5 InChI=1S/C12H14N4O2S/c1-8-7-9(2)15-12(14-8)16-19(17,18)11-5-3-10(13)4-6-11/h3-7H,13H2,1-2H3,(H,14,15,16)
## inchikey formula exactmass smiles
## 1 XOXHILFPRYWFOD-UHFFFAOYSA-N C10H9ClN4O2S 284.0135 <NA>
## 2 QKLPUVXBJHRFQZ-UHFFFAOYSA-N C10H9ClN4O2S 284.0135 <NA>
## 3 ASWVTGNCAZCNNR-UHFFFAOYSA-N C12H14N4O2S 278.0837 <NA>
## 4 ASWVTGNCAZCNNR-UHFFFAOYSA-N C12H14N4O2S 278.0837 <NA>
## 5 ASWVTGNCAZCNNR-UHFFFAOYSA-N C12H14N4O2S 278.0837 <NA>As stated in the introduction of this section the compound information
contains redundant information and the table has essentially one row per
spectrum. Feedback on how to reduce the redundancy in the ms_compound table is
highly appreciated.
CompDb by sequentially filling with dataAs an alternative to creating a full database from an existing resource it is
also possible to create an empty CompDb database and sequentially filling it
with data. This could for example be used to create a laboratory specific
annotation library with compound, ion and fragment spectra of pure standards
measured on a certain LC-MS setup. Below we create an empty CompDb database
providing the file name of the database. In the example we store the database to
a temporary file but in a real use case a meaningful file name and file path
should be used instead.
dbfile <- tempfile()
mydb <- emptyCompDb(dbfile)
mydb## class: CompDb
## data source: NA
## version: NA
## organism: NA
## compound count: 0We next define some first compound annotation we want to add to the
database. For compound annotations, fields "compound_id" (an arbitrary ID of
the compound), "name" (the compound name), "inchi", "inchikey",
"formula" (the chemical formula) and "exactmass" (the monoisotopic mass) are
expected, but, except of "compound_id", they can also contain missing values
or be completely omitted. Below we define a data.frame with annotations for
some compounds and add this annotation to the database using the
insertCompound function.
cmp <- data.frame(compound_id = c("1", "2"),
name = c("Caffeine", "Glucose"),
formula = c("C8H10N4O2", "C6H12O6"),
exactmass = c(194.080375584, 180.063388116))
mydb <- insertCompound(mydb, cmp)
mydb## class: CompDb
## data source: NA
## version: NA
## organism: NA
## compound count: 2We next add fragment spectra for the compounds. These could for example
represent MS2 spectra measured for the pure standard of the compound and could
be extracted for example from xcms result objects or other sources. Below we
load some fragment spectra for caffeine from an MGF file distributed with this
package. We use the MsBackendMgf package to import
that data into a Spectra object.
library(MsBackendMgf)
caf_ms2 <- Spectra(system.file("mgf", "caffeine.mgf", package = "CompoundDb"),
source = MsBackendMgf())
caf_ms2## MSn data (Spectra) with 2 spectra in a MsBackendMgf backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 2 NA NA
## 2 2 NA NA
## ... 25 more variables/columns.We can evaluate what spectra variables are available in the imported data.
spectraVariables(caf_ms2)## [1] "msLevel" "rtime"
## [3] "acquisitionNum" "scanIndex"
## [5] "dataStorage" "dataOrigin"
## [7] "centroided" "smoothed"
## [9] "polarity" "precScanNum"
## [11] "precursorMz" "precursorIntensity"
## [13] "precursorCharge" "collisionEnergy"
## [15] "isolationWindowLowerMz" "isolationWindowTargetMz"
## [17] "isolationWindowUpperMz" "TITLE"
## [19] "original_spectrum_id" "compound_id"
## [21] "collision_energy" "predicted"
## [23] "splash" "spectrum_id"
## [25] "msms_mz_range_min" "msms_mz_range_max"caf_ms2$rtime## [1] NA NAThere are many variables available, but most of them, like for example the
retention time, or not defined as this information was not provided in the MGF
file. In order to associate these fragment spectra to the caffeine compound we
just added to the database, we need to assign them the ID of the compound (in
our case "1").
caf_ms2$compound_id <- "1"We can then add the spectra to the database using the insertSpectra
function. With parameter columns we specify which of the spectra variables we
actually want to store in the database.
mydb <- insertSpectra(mydb, caf_ms2,
columns = c("compound_id", "msLevel", "splash",
"precursorMz", "collisionEnergy"))
mydb## class: CompDb
## data source: NA
## version: NA
## organism: NA
## compound count: 2
## MS/MS spectra count: 2We thus have now 2 compounds in the database and 2 fragment spectra:
compounds(mydb)## name inchi inchikey formula exactmass
## 1 Caffeine <NA> <NA> C8H10N4O2 194.0804
## 2 Glucose <NA> <NA> C6H12O6 180.0634sps <- Spectra(mydb)
sps$name## [1] "Caffeine" "Caffeine"With insertCommpound and insertSpectra further compounds and fragment
spectra could be added to the database. Note that both functions support also to
add additional columns (fields or variables) to the database. As an example
we define below a compound with an arbitrary additional column and add this to
the database using parameter addColumns = TRUE.
cmps <- data.frame(compound_id = "3", name = "X003",
formula = "C5H2P3O", extra_field = "artificial compound")
mydb <- insertCompound(mydb, cmps, addColumns = TRUE)
compounds(mydb)## name inchi inchikey formula exactmass extra_field
## 1 Caffeine <NA> <NA> C8H10N4O2 194.0804 <NA>
## 2 Glucose <NA> <NA> C6H12O6 180.0634 <NA>
## 3 X003 <NA> <NA> C5H2P3O NA artificial compoundThe additional column is now available in the database. Existing entries in a
CompDb can also be deleted using the deleteCompound or deleteSpectra
functions. Both require as additional input the IDs of the compound(s) (or
spectra) to delete. Below we extract the IDs and names of the compounds from our
database.
compounds(mydb, columns = c("compound_id", "name"))## compound_id name
## 1 1 Caffeine
## 2 2 Glucose
## 3 3 X003We can now delete the compound "X003" with deleteCompound and the ID of this
compound.
mydb <- deleteCompound(mydb, ids = "3")
compounds(mydb)## name inchi inchikey formula exactmass extra_field
## 1 Caffeine <NA> <NA> C8H10N4O2 194.0804 <NA>
## 2 Glucose <NA> <NA> C6H12O6 180.0634 <NA>Note that deleting a compound with associated spectra (or ions) will result in
an error, thus it would not be possible to delete caffeine from the database,
because it contains also MS2 spectra for that compound. Using parameter
recursive = TRUE in the deleteCompound call would however allow to delete
the compound and all associated spectra (and/or ions) along with it. Below
we delete thus caffeine and the associated MS2 spectra which leaves us a
CompDb with a single compound and no more MS2 spectra.
mydb <- deleteCompound(mydb, ids = "1", recursive = TRUE)
compounds(mydb)## name inchi inchikey formula exactmass extra_field
## 1 Glucose <NA> <NA> C6H12O6 180.0634 <NA>Spectra(mydb)## MSn data (Spectra) with 0 spectra in a MsBackendCompDb backend:Note that these functions can also be used to add or remove annotations to/from
any CompDb database, as long as the database is writeable (i.e. the database
is loaded by specifying flags = RSQLite::SQLITE_RW as additional parameter to
the CompDb call to load the database).
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] MsBackendMgf_1.10.0 RSQLite_2.3.1 Spectra_1.12.0
## [4] ProtGenerics_1.34.0 BiocParallel_1.36.0 CompoundDb_1.6.0
## [7] S4Vectors_0.40.0 BiocGenerics_0.48.0 AnnotationFilter_1.26.0
## [10] BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.4 rjson_0.2.21 xfun_0.40
## [4] bslib_0.5.1 ggplot2_3.4.4 htmlwidgets_1.6.2
## [7] Biobase_2.62.0 vctrs_0.6.4 tools_4.3.1
## [10] bitops_1.0-7 generics_0.1.3 parallel_4.3.1
## [13] tibble_3.2.1 fansi_1.0.5 blob_1.2.4
## [16] cluster_2.1.4 pkgconfig_2.0.3 dbplyr_2.3.4
## [19] lifecycle_1.0.3 GenomeInfoDbData_1.2.11 compiler_4.3.1
## [22] munsell_0.5.0 codetools_0.2-19 clue_0.3-65
## [25] GenomeInfoDb_1.38.0 htmltools_0.5.6.1 sass_0.4.7
## [28] RCurl_1.98-1.12 yaml_2.3.7 lazyeval_0.2.2
## [31] pillar_1.9.0 jquerylib_0.1.4 MASS_7.3-60
## [34] DT_0.30 cachem_1.0.8 MetaboCoreUtils_1.10.0
## [37] tidyselect_1.2.0 digest_0.6.33 dplyr_1.1.3
## [40] bookdown_0.36 rsvg_2.6.0 fastmap_1.1.1
## [43] grid_4.3.1 colorspace_2.1-0 cli_3.6.1
## [46] magrittr_2.0.3 base64enc_0.1-3 utf8_1.2.4
## [49] ChemmineR_3.54.0 scales_1.2.1 bit64_4.0.5
## [52] rmarkdown_2.25 XVector_0.42.0 bit_4.0.5
## [55] gridExtra_2.3 png_0.1-8 memoise_2.0.1
## [58] evaluate_0.22 knitr_1.44 GenomicRanges_1.54.0
## [61] IRanges_2.36.0 rlang_1.1.1 Rcpp_1.0.11
## [64] glue_1.6.2 DBI_1.1.3 xml2_1.3.5
## [67] BiocManager_1.30.22 jsonlite_1.8.7 R6_2.5.1
## [70] fs_1.6.3 zlibbioc_1.48.0 MsCoreUtils_1.14.0