Package: MsBackendSql
Authors: Johannes Rainer [aut, cre] (ORCID:
https://orcid.org/0000-0002-6977-7147),
Chong Tang [ctb],
Laurent Gatto [ctb] (ORCID: https://orcid.org/0000-0002-1520-2268)
Compiled: Thu Apr 10 18:18:46 2025
The Spectra Bioconductor package provides a flexible and
expandable infrastructure for Mass Spectrometry (MS) data. The package supports
interchangeable use of different backends that provide additional file support
or different ways to store and represent MS data. The
MsBackendSql package provides backends to store data from whole
MS experiments in SQL databases. The data in such databases can be easily (and
efficiently) accessed using Spectra objects that use the MsBackendSql class
as an interface to the data in the database. Such Spectra objects have a
minimal memory footprint and hence allow analysis of very large data sets even
on computers with limited hardware capabilities. For certain operations, the
performance of this data representation is superior to that of other low-memory
(on-disk) data representations such as Spectra’s MsBackendMzR backend.
Finally, the MsBackendSql supports also remote data access to e.g. a central
database server hosting several large MS data sets.
The package can be installed with the BiocManager package. To install
BiocManager use install.packages("BiocManager") and, after that,
BiocManager::install("MsBackendSql") to install this package.
MsBackendSql SQL databasesMsBackendSql SQL databases can be created either by importing (raw) MS data
from MS data files using the createMsBackendSqlDatabase() or using the
backendInitialize() function by providing in addition to the database
connection also the full MS data to import as a DataFrame. In the first
example we use the createMsBackendSqlDatabase() function to import the full MS
data from the provided MS data files into an (empty) database. Below we first
create an empty SQLite database (in a temporary file) and use the
createMsBackendSqlDatabase() function to create all necessary tables in that
database and import the MS data from two mzML files (from the r Biocpkg("msdata") package).
library(RSQLite)
dbfile <- tempfile()
con <- dbConnect(SQLite(), dbfile)
library(Spectra)
library(MsBackendSql)
fls <- dir(system.file("sciex", package = "msdata"), full.names = TRUE)
createMsBackendSqlDatabase(con, fls)
dbDisconnect(con)By default (with parameters blob = TRUE and peaksStorageMode = "blob2") the
peaks data matrix of each spectrum is stored as a BLOB data type into the
database (one entry per spectrum). This has advantages on the performance to
extract the peaks data from the database, but does not allow to filter
individual peaks by their m/z or intensity values directly in the database. As
an alternative (using blob = FALSE) it is also possible to store the
individual m/z and intensity values in separate columns of the database
table. This long table format results however in considerably larger databases
(with potentially poorer performance). Note also that the code and backend is
optimized for MySQL/MariaDB databases by taking advantage of table partitioning
and specialized table storage options. Any other SQL database server is however
also supported (also portable, self-contained SQLite databases). In fact,
performance for MsBackendSql databases with peaks data stored as BLOB data
type is similar for SQLite and MySQL/MariaDB databases.
The MsBackendSql package provides two backends to interact with such
databases: the MsBackendSql class and the MsBackendOfflineSql class, that
inherits all properties and functions from the former, but does not store the
connection to the database within the object. The MsBackendOfflineSql object
thus supports parallel processing and allows to save/load the object (e.g. using
save and saveRDS). The MsBackendOfflineSql might therefore be used as the
preferred backend to SQL databases for most applications.
To access the data in the database we create below a Spectra object providing
the database connection information in the constructor call and specifying to
use the MsBackendOfflineSql as backend (parameter source). We stored the
data to a SQLite database, thus we provide the database name (SQLite database
file name) and the SQLite DBI driver with parameters dbname and drv. Which
parameters are required to connect to the database depends on the SQL database
and the used driver. For a MySQL/MariaDB database we would use the MariaDB()
driver and would have to provide the database name, user name, password as well
as the host name and port through which the database is accessible.
sps <- Spectra(dbname = dbfile, source = MsBackendOfflineSql(), drv = SQLite())
sps## MSn data (Spectra) with 1862 spectra in a MsBackendOfflineSql backend:
## msLevel precursorMz polarity
## <integer> <numeric> <integer>
## 1 1 NA 1
## 2 1 NA 1
## 3 1 NA 1
## 4 1 NA 1
## 5 1 NA 1
## ... ... ... ...
## 1858 1 NA 1
## 1859 1 NA 1
## 1860 1 NA 1
## 1861 1 NA 1
## 1862 1 NA 1
## ... 35 more variables/columns.
## Use 'spectraVariables' to list all of them.
## Database: /tmp/RtmpZnC7nu/file1f04f144650fe2Spectra objects allow also to change the backend to any other backend
(extending MsBackend) using the setBackend() function. Below we use this
function to first load all data into memory by changing from the
MsBackendOfflineSql to a MsBackendMemory.
sps_mem <- setBackend(sps, MsBackendMemory())
sps_mem## MSn data (Spectra) with 1862 spectra in a MsBackendMemory backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 1 0.280 1
## 2 1 0.559 2
## 3 1 0.838 3
## 4 1 1.117 4
## 5 1 1.396 5
## ... ... ... ...
## 1858 1 258.636 927
## 1859 1 258.915 928
## 1860 1 259.194 929
## 1861 1 259.473 930
## 1862 1 259.752 931
## ... 35 more variables/columns.
## Processing:
## Switch backend from MsBackendOfflineSql to MsBackendMemory [Thu Apr 10 18:18:56 2025]With this function it is also possible to change from any backend to a
MsBackendOfflineSql (or MsBackendSql) in which case a new database is
created and all data from the originating backend is stored in this database. To
change the backend to an MsBackendOfflineSql we need to provide the connection
information to the SQL database as additional parameters. These parameters are
the same that need to be passed to a dbConnect() call to establish the
connection to the database. These parameters include the database driver
(parameter drv), the database name and eventually the user name, host etc (see
?dbConnect for more information). In the simple example below we store the
data into a SQLite database and thus only need to provide the database name,
which corresponds SQLite database file. In our example we store the data into a
temporary file. Optionally, setBackend() supports also the parameters blob
and peaksDataStorage described above for the createMsBackendSqlDatabase()
function.
sps2 <- setBackend(sps_mem, MsBackendOfflineSql(), drv = SQLite(),
dbname = tempfile())
sps2## MSn data (Spectra) with 1862 spectra in a MsBackendOfflineSql backend:
## msLevel precursorMz polarity
## <integer> <numeric> <integer>
## 1 1 NA 1
## 2 1 NA 1
## 3 1 NA 1
## 4 1 NA 1
## 5 1 NA 1
## ... ... ... ...
## 1858 1 NA 1
## 1859 1 NA 1
## 1860 1 NA 1
## 1861 1 NA 1
## 1862 1 NA 1
## ... 35 more variables/columns.
## Use 'spectraVariables' to list all of them.
## Database: /tmp/RtmpZnC7nu/file1f04f16f4b6b1
## Processing:
## Switch backend from MsBackendOfflineSql to MsBackendMemory [Thu Apr 10 18:18:56 2025]
## Switch backend from MsBackendMemory to MsBackendOfflineSql [Thu Apr 10 18:18:56 2025]Similar to any other Spectra object we can retrieve the available spectra
variables using the spectraVariables() function.
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" "peaksCount"
## [19] "totIonCurrent" "basePeakMZ"
## [21] "basePeakIntensity" "ionisationEnergy"
## [23] "lowMZ" "highMZ"
## [25] "mergedScan" "mergedResultScanNum"
## [27] "mergedResultStartScanNum" "mergedResultEndScanNum"
## [29] "injectionTime" "filterString"
## [31] "spectrumId" "ionMobilityDriftTime"
## [33] "scanWindowLowerLimit" "scanWindowUpperLimit"
## [35] "electronBeamEnergy" "spectrum_id_"The MS peak data can be accessed using either the mz(), intensity() or
peaksData() functions. Below we extract the peaks matrix of the 5th spectrum
and display the first 6 rows.
peaksData(sps)[[5]] |>
head()## mz intensity
## [1,] 105.0347 0
## [2,] 105.0362 164
## [3,] 105.0376 0
## [4,] 105.0391 0
## [5,] 105.0405 328
## [6,] 105.0420 0All data (peaks data or spectra variables) are always retrieved on-the-fly
from the database resulting thus in a minimal memory footprint for the Spectra
object.
print(object.size(sps), units = "KB")## 114.6 KbThe backend supports also adding additional spectra variables or changing their values. Below we add 10 seconds to the retention time of each spectrum.
sps$rtime <- sps$rtime + 10Such operations do however not change the data in the database (which is always considered read-only) but are cached locally within the backend object (in memory). The size in memory of the object is thus higher after changing that spectra variable.
print(object.size(sps), units = "KB")## 129.2 KbSuch $<- operations can also be used to cache spectra variables
(temporarily) in memory which can eventually improve performance. Below we test
the time it takes to extract the MS level from each spectrum from the database,
then cache the MS levels in memory using $msLevel <- and test the timing to
extract these cached variable.
system.time(msLevel(sps))## user system elapsed
## 0.013 0.000 0.013sps$msLevel <- msLevel(sps)
system.time(msLevel(sps))## user system elapsed
## 0.006 0.000 0.005We can also use the reset() function to reset the data to its original state
(this will cause any local spectra variables to be deleted and the backend to be
initialized with the original data in the database).
sps <- reset(sps)The need to retrieve any spectra data on-the-fly from the database has an impact
on the performance of data access functions of Spectra objects using
MsBackendSql/MsBackendOfflineSql backends. To evaluate this we compare below
the performance of the MsBackendSql to other Spectra backends, specifically,
the MsBackendMzR which is the default backend to read and represent raw MS
data, and the MsBackendMemory backend that keeps all MS data in memory (and is
thus not suggested for larger MS experiments). Similar to the MsBackendMzR,
also the MsBackendSql keeps only a limited amount of data in memory. These
on-disk backends need thus to retrieve spectra and MS peaks data on-the-fly
from either the original raw data files (in the case of the MsBackendMzR) or
from the SQL database (in the case of the MsBackendSql). The in-memory backend
MsBackendMemory is supposed to provide the fastest data access since all data
is kept in memory.
Below we thus create Spectra objects from the same data but using the
different backends.
con <- dbConnect(SQLite(), dbfile)
sps <- Spectra(con, source = MsBackendSql())
sps_mzr <- Spectra(fls, source = MsBackendMzR())
sps_im <- setBackend(sps_mzr, backend = MsBackendMemory())At first we compare the memory footprint of the 3 backends.
print(object.size(sps), units = "KB")## 112.9 Kbprint(object.size(sps_mzr), units = "KB")## 401.4 Kbprint(object.size(sps_im), units = "KB")## 54509.1 KbThe MsBackendSql has the lowest memory footprint of all 3 backends because it
does not keep any data in memory. The MsBackendMzR keeps all spectra
variables, except the MS peaks data, in memory and has thus a larger size. The
MsBackendMemory keeps all data (including the MS peaks data) in memory and has
thus the largest size in memory.
Next we compare the performance to extract the MS level for each spectrum from
the 4 different Spectra objects.
library(microbenchmark)
microbenchmark(msLevel(sps),
msLevel(sps_mzr),
msLevel(sps_im))## Unit: microseconds
## expr min lq mean median uq
## msLevel(sps) 9218.528 9970.3485 11293.05995 10297.8315 10627.2200
## msLevel(sps_mzr) 630.441 702.6555 787.88521 726.0095 792.1915
## msLevel(sps_im) 15.329 22.4220 41.81995 40.9055 56.7060
## max neval cld
## 30247.502 100 a
## 1434.487 100 b
## 97.642 100 cExtracting MS levels is thus slowest for the MsBackendSql, which is not
surprising because both other backends keep this data in memory while the
MsBackendSql needs to retrieve it from the database.
We next compare the performance to access the full peaks data from each
Spectra object.
microbenchmark(peaksData(sps, BPPARAM = SerialParam()),
peaksData(sps_mzr, BPPARAM = SerialParam()),
peaksData(sps_im, BPPARAM = SerialParam()),
times = 10)## Unit: microseconds
## expr min lq mean
## peaksData(sps, BPPARAM = SerialParam()) 63896.210 85040.580 199722.212
## peaksData(sps_mzr, BPPARAM = SerialParam()) 704080.140 723651.342 1215790.148
## peaksData(sps_im, BPPARAM = SerialParam()) 680.927 1103.355 3576.507
## median uq max neval cld
## 92098.131 145704.979 665382.05 10 a
## 765785.087 1961930.936 2144839.45 10 b
## 1327.347 1497.251 19557.94 10 aAs expected, the MsBackendMemory has the fasted access to the full peaks
data. The MsBackendSql outperforms however the MsBackendMzR providing faster
access to the m/z and intensity values.
Performance can be improved for the MsBackendMzR using parallel
processing. Note that the MsBackendSql does not support parallel
processing and thus parallel processing is (silently) disabled in functions such
as peaksData().
m2 <- MulticoreParam(2)
microbenchmark(peaksData(sps, BPPARAM = m2),
peaksData(sps_mzr, BPPARAM = m2),
peaksData(sps_im, BPPARAM = m2),
times = 10)## Unit: milliseconds
## expr min lq mean median
## peaksData(sps, BPPARAM = m2) 55.30679 79.562303 179.84537 99.03248
## peaksData(sps_mzr, BPPARAM = m2) 718.43306 743.280512 1141.68879 1252.28758
## peaksData(sps_im, BPPARAM = m2) 1.00160 1.215768 1.57279 1.24670
## uq max neval cld
## 113.921173 569.393954 10 a
## 1302.931418 1967.822641 10 b
## 1.571142 4.276366 10 aWe next compare the performance of subsetting operations.
microbenchmark(filterRt(sps, rt = c(50, 100)),
filterRt(sps_mzr, rt = c(50, 100)),
filterRt(sps_im, rt = c(50, 100)))## Unit: microseconds
## expr min lq mean median
## filterRt(sps, rt = c(50, 100)) 3061.133 3396.153 4021.7053 3693.2365
## filterRt(sps_mzr, rt = c(50, 100)) 1991.067 2264.887 2432.2569 2408.1820
## filterRt(sps_im, rt = c(50, 100)) 629.460 699.550 753.4848 735.7525
## uq max neval cld
## 3899.0175 33606.466 100 a
## 2541.0925 4152.161 100 b
## 765.6155 2042.708 100 cThe two on-disk backends MsBackendSql and MsBackendMzR show a comparable
performance for this operation. This filtering does involves access to a spectra
variables (the retention time in this case) which, for the MsBackendSql needs
first to be retrieved from the backend. The MsBackendSql backend allows
however also to cache spectra variables (i.e. they are stored within the
MsBackendSql object). Any access to such cached spectra variables can
eventually be faster because no dedicated SQL query is needed.
To evaluate the performance of a pure subsetting operation we first define the
indices of 10 random spectra and subset the Spectra objects to these.
idx <- sample(seq_along(sps), 10)
microbenchmark(sps[idx],
sps_mzr[idx],
sps_im[idx])## Unit: microseconds
## expr min lq mean median uq max neval
## sps[idx] 201.820 217.2455 242.5694 242.0025 267.806 291.069 100
## sps_mzr[idx] 1023.557 1043.8615 1067.6095 1053.2060 1067.233 1948.672 100
## sps_im[idx] 315.895 337.0125 365.9740 347.9125 372.542 1639.711 100
## cld
## a
## b
## cHere the MsBackendSql outperforms the other backends because it does not keep
any data in memory and hence does not need to subset these. The two other
backends need to subset the data they keep in memory which is in both cases a
data frame with either a reduced set of spectra variables or the full MS data.
At last we compare also the extraction of the peaks data from the such subset
Spectra objects.
sps_10 <- sps[idx]
sps_mzr_10 <- sps_mzr[idx]
sps_im_10 <- sps_im[idx]
microbenchmark(peaksData(sps_10),
peaksData(sps_mzr_10),
peaksData(sps_im_10),
times = 10)## Unit: microseconds
## expr min lq mean median uq
## peaksData(sps_10) 2163.507 3578.159 4584.0039 4053.4435 4906.068
## peaksData(sps_mzr_10) 120097.277 126275.007 133098.1046 133786.6980 140800.305
## peaksData(sps_im_10) 594.111 703.045 835.5756 774.4805 1015.724
## max neval cld
## 11111.764 10 a
## 142141.895 10 b
## 1126.676 10 aThe MsBackendSql outperforms the MsBackendMzR while, not unexpectedly, the
MsBackendMemory provides fasted access.
The backends from the MsBackendSql package use standard SQL calls to retrieve MS data from the database and hence any SQL database system (for which an R package is available) is supported. SQLite-based databases would represent the easiest and most user friendly solution since no database server administration and user management is required. Indeed, performance of SQLite is very high, even for very large data sets. Server-based databases on the other hand have the advantage to enable a centralized storage and control of MS data (inclusive user management etc). Also, such server systems would also allow data set or server-specific configurations to improve performance.
A comparison between a SQLite-based with a MariaDB-based MsBackendSql database for a large data set comprising over 8,000 samples and over 15,000,000 spectra is available here. In brief, performance to extract data was comparable and for individual spectra variables even faster for the SQLite database. Only when more complex SQL queries were involved (combining several primary keys or data fields) the more advanced MariaDB database outperformed SQLite.
MsBackendSqlThe MsBackendSql backend does not support parallel processing since the
database connection can not be shared across the different (parallel)
processes. Thus, all methods on Spectra objects that use a MsBackendSql will
automatically (and silently) disable parallel processing even if a dedicated
parallel processing setup was passed along with the BPPARAM method.
Some functions on Spectra objects require to load the MS peak data (i.e., m/z
and intensity values) into memory. For very large data sets (or computers with
limited hardware resources) such function calls can cause out-of-memory
errors. One example is the lengths() function that determines the number of
peaks per spectrum by loading the peak matrix first into memory. Such functions
should ideally be called using the peaksapply() function with parameter
chunkSize (e.g., peaksapply(sps, lengths, chunkSize = 5000L)). Instead of
processing the full data set, the data will be first split into chunks of size
chunkSize that are stepwise processed. Hence, only data from chunkSize
spectra is loaded into memory in one iteration.
The MsBackendSql provides an MS data representations and storage mode with a
minimal memory footprint (in R) that is still comparably efficient for standard
processing and subsetting operations. This backend is specifically useful for
very large MS data sets, that could even be hosted on remote (MySQL/MariaDB)
servers. A potential use case for this backend could thus be to set up a central
storage place for MS experiments with data analysts connecting remotely to this
server to perform initial data exploration and filtering. After subsetting to a
smaller data set of interest, users could then retrieve/download this data by
changing the backend to e.g. a MsBackendMemory, which would result in a
download of the full data to the user computer’s memory.
sessionInfo()## R version 4.5.0 beta (2025-04-02 r88102)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## 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] microbenchmark_1.5.0 RSQLite_2.3.9 MsBackendSql_1.7.4
## [4] Spectra_1.17.10 BiocParallel_1.41.5 S4Vectors_0.45.4
## [7] BiocGenerics_0.53.6 generics_0.1.3 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] sandwich_3.1-1 sass_0.4.9 MsCoreUtils_1.19.2
## [4] lattice_0.22-7 stringi_1.8.7 hms_1.1.3
## [7] digest_0.6.37 grid_4.5.0 evaluate_1.0.3
## [10] bookdown_0.42 mvtnorm_1.3-3 fastmap_1.2.0
## [13] blob_1.2.4 Matrix_1.7-3 jsonlite_2.0.0
## [16] ProtGenerics_1.39.2 progress_1.2.3 mzR_2.41.4
## [19] DBI_1.2.3 survival_3.8-3 multcomp_1.4-28
## [22] BiocManager_1.30.25 TH.data_1.1-3 codetools_0.2-20
## [25] jquerylib_0.1.4 cli_3.6.4 rlang_1.1.5
## [28] crayon_1.5.3 Biobase_2.67.0 splines_4.5.0
## [31] bit64_4.6.0-1 cachem_1.1.0 yaml_2.3.10
## [34] tools_4.5.0 parallel_4.5.0 memoise_2.0.1
## [37] ncdf4_1.24 fastmatch_1.1-6 vctrs_0.6.5
## [40] R6_2.6.1 zoo_1.8-14 lifecycle_1.0.4
## [43] fs_1.6.5 IRanges_2.41.3 bit_4.6.0
## [46] clue_0.3-66 MASS_7.3-65 cluster_2.1.8.1
## [49] pkgconfig_2.0.3 bslib_0.9.0 data.table_1.17.0
## [52] Rcpp_1.0.14 xfun_0.52 knitr_1.50
## [55] htmltools_0.5.8.1 rmarkdown_2.29 compiler_4.5.0
## [58] prettyunits_1.2.0 MetaboCoreUtils_1.15.0