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: Fri Jan 24 18:18:36 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
## ... 34 more variables/columns.
## Use 'spectraVariables' to list all of them.
## Database: /tmp/RtmpObibvy/file3c6f7027bd2282Spectra 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
## ... 34 more variables/columns.
## Processing:
## Switch backend from MsBackendOfflineSql to MsBackendMemory [Fri Jan 24 18:18:46 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
## ... 34 more variables/columns.
## Use 'spectraVariables' to list all of them.
## Database: /tmp/RtmpObibvy/file3c6f7027473ec7
## Processing:
## Switch backend from MsBackendOfflineSql to MsBackendMemory [Fri Jan 24 18:18:46 2025]
## Switch backend from MsBackendMemory to MsBackendOfflineSql [Fri Jan 24 18:18:46 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] "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")## 114.4 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")## 129.1 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 $msLevel <- and test the timing to
extract these cached variable.
system.time(msLevel(sps))## user system elapsed
## 0.015 0.000 0.015sps$msLevel <- msLevel(sps)
system.time(msLevel(sps))## user system elapsed
## 0.006 0.001 0.006We 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")## 112.7 Kbprint(object.size(sps_mzr), units = "KB")## 386.7 Kbprint(object.size(sps_im), units = "KB")## 54494.5 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 max
## msLevel(sps) 8703.379 9465.2470 9967.6394 9781.8715 10330.071 16483.946
## msLevel(sps_mzr) 621.004 650.8675 721.0301 701.6410 761.307 1726.882
## msLevel(sps_im) 15.259 25.4115 39.9531 43.1595 49.876 73.677
## neval cld
## 100 a
## 100 b
## 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: milliseconds
## expr min lq mean
## peaksData(sps, BPPARAM = SerialParam()) 65.694644 99.55554 298.417111
## peaksData(sps_mzr, BPPARAM = SerialParam()) 671.285176 741.83516 1250.312903
## peaksData(sps_im, BPPARAM = SerialParam()) 1.169557 1.47417 3.693248
## median uq max neval cld
## 113.237639 575.547847 670.3796 10 a
## 1427.405669 1489.275215 2142.0533 10 b
## 1.718607 2.166274 20.6968 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: microseconds
## expr min lq mean median
## peaksData(sps, BPPARAM = m2) 86173.557 92081.653 171813.542 105915.8
## peaksData(sps_mzr, BPPARAM = m2) 737658.734 765322.371 1189197.370 1347494.5
## peaksData(sps_im, BPPARAM = m2) 693.853 1054.159 1369.808 1324.3
## uq max neval cld
## 139078.429 735938.395 10 a
## 1428802.587 1828940.138 10 b
## 1726.417 1844.571 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)) 4346.804 4831.529 5398.4117 5194.2345
## filterRt(sps_mzr, rt = c(50, 100)) 3404.464 3642.531 3931.2537 3929.5645
## filterRt(sps_im, rt = c(50, 100)) 727.883 851.867 920.4284 894.3755
## uq max neval cld
## 5658.4625 19331.416 100 a
## 4105.3345 5701.721 100 b
## 949.6085 2937.560 100 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] 200.870 216.8790 255.3341 246.0010 263.3065 1489.870 100
## sps_mzr[idx] 1011.440 1027.5210 1056.5610 1039.7990 1052.4420 1914.716 100
## sps_im[idx] 301.889 319.5725 340.2897 336.4005 356.4720 450.454 100
## 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) 2042.442 2593.739 3508.5212 3437.1115 3889.577
## peaksData(sps_mzr_10) 124118.816 126988.520 140100.6958 136312.2915 146648.374
## peaksData(sps_im_10) 400.849 594.542 759.1711 701.4085 933.560
## max neval cld
## 5926.821 10 a
## 176097.400 10 b
## 1249.946 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 Under development (unstable) (2025-01-20 r87609)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-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_1.7.3
## [4] Spectra_1.17.5 BiocParallel_1.41.0 S4Vectors_0.45.2
## [7] BiocGenerics_0.53.5 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_1.19.0
## [4] lattice_0.22-6 stringi_1.8.4 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_1.7-2 jsonlite_1.8.9
## [16] ProtGenerics_1.39.2 progress_1.2.3 mzR_2.41.1
## [19] DBI_1.2.3 survival_3.8-3 multcomp_1.4-26
## [22] BiocManager_1.30.25 TH.data_1.1-3 codetools_0.2-20
## [25] jquerylib_0.1.4 cli_3.6.3 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_1.23 fastmatch_1.1-6 vctrs_0.6.5
## [40] R6_2.5.1 zoo_1.8-12 lifecycle_1.0.4
## [43] fs_1.6.5 IRanges_2.41.2 bit_4.5.0.1
## [46] clue_0.3-66 MASS_7.3-64 cluster_2.1.8
## [49] pkgconfig_2.0.3 bslib_0.8.0 data.table_1.16.4
## [52] Rcpp_1.0.14 xfun_0.50 knitr_1.49
## [55] htmltools_0.5.8.1 rmarkdown_2.29 compiler_4.5.0
## [58] prettyunits_1.2.0 MetaboCoreUtils_1.15.0