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: Tue Oct 29 18:27:40 2024
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 which takes a
connection to an (empty) database and the names of the files from which the data
should be imported as input parameters creates all necessary database tables and
stores the full data into the database. Below we create an empty SQLite database
(in a temporary file) and fill that with MS data from two mzML files (from the
msdata package).
library(RSQLite)
dbfile <- tempfile()
con <- dbConnect(SQLite(), dbfile)
library(MsBackendSql)
fls <- dir(system.file("sciex", package = "msdata"), full.names = TRUE)
createMsBackendSqlDatabase(con, fls)By default the m/z and intensity values are stored as BLOB data types in the database. This has advantages on the performance to extract peaks data from the database but would for example not allow to filter peaks by m/z values directly in the database. As an alternative it is also possible to the individual m/z and intensity values in separate rows 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).
The MsBackendSql package provides two backends to interact with such
databases: the (default) MsBackendSql class and the MsBackendOfflineSql,
that inherits all properties and functions from the former, but which does not
store the connection to the database within the object but connects (and
disconnects) to (and from) the database in each function call. This allows to
use the latter also for parallel processing setups or to save/load the object
(e.g. using save and saveRDS). Thus, for most applications the
MsBackendOfflineSql might be used as the preferred backend to SQL databases.
To access the data in the database we create below a Spectra object providing
the connection to the database in the constructor call and specifying to use the
MsBackendSql as backend using the source parameter.
sps <- Spectra(con, source = MsBackendSql())
sps## MSn data (Spectra) with 1862 spectra in a MsBackendSql 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/Rtmp8gBBTc/file143d1420c7b3b1As an alternative, the MsBackendOfflineSql backend could also be used to
interface with MS data in a SQL database. In contrast to the MsBackendSql, the
MsBackendOfflineSql does not contain an active (open) connection to the
database and hence supports serializing (saving) the object to disk using
e.g. the save() function, or parallel processing (if supported by the database
system). Thus, for most use cases the MsBackendOfflineSql should be used
instead of the MsBackendSql. See further below for more information on the
MsBackendOfflineSql.
Spectra 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 MsBackendSql
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 MsBackendSql to MsBackendMemory [Tue Oct 29 18:27:48 2024]With this function it is also possible to change from any backend to a
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.
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/Rtmp8gBBTc/file143d146b79fb4
## Processing:
## Switch backend from MsBackendSql to MsBackendMemory [Tue Oct 29 18:27:48 2024]
## Switch backend from MsBackendMemory to MsBackendOfflineSql [Tue Oct 29 18:27:49 2024]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")## 91.5 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")## 106.2 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 $msLevel <- and test the timing to
extract these cached variable.
system.time(msLevel(sps))## user system elapsed
## 0.011 0.000 0.011sps$msLevel <- msLevel(sps)
system.time(msLevel(sps))## user system elapsed
## 0.004 0.000 0.003We 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)To use the MsBackendOfflineSql backend we need to provide all information
required to connect to the database along with the database driver to the
Spectra function. Which parameters are required to connect to the database
depends on the SQL database and the used driver. In our example the data is
stored in a SQLite database, hence we use the SQLite() database driver and
only need to provide the database name with the dbname parameter. 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_off <- Spectra(dbfile, drv = SQLite(),
source = MsBackendOfflineSql())
sps_off## 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/Rtmp8gBBTc/file143d1420c7b3b1This backend provides the exact same functionality than MsBackendSql with the
difference that the connection to the database is opened and closed for each
function call. While this leads to a slightly lower performance, it allows to to
serialize the object (i.e. save/load the object to/from disk) and to use it (and
hence the Spectra object) also in a parallel processing setup. In contrast,
for the MsBackendSql parallel processing is disabled since it is not possible
to share the active backend connection within the object across different
parallel processes.
Below we compare the performance of the two backends. The performance difference is the result from opening and closing the database connection for each call. Note that this will also depend on the SQL server that is being used. For SQLite databases there is almost no overhead.
library(microbenchmark)
microbenchmark(msLevel(sps), msLevel(sps_off))## Unit: milliseconds
## expr min lq mean median uq max
## msLevel(sps) 9.016711 9.371454 9.688384 9.553314 9.854163 16.67708
## msLevel(sps_off) 11.126440 11.610402 12.149644 12.031719 12.360917 17.73729
## neval cld
## 100 a
## 100 bThe need to retrieve any spectra data on-the-fly from the database will have an
impact on the performance of data access function of Spectra objects using the
MsBackendSql backends. To evaluate its impact we next compare 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.
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")## 91.5 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) 9155.917 9510.0480 10357.54527 10426.8715 10693.403 23931.488
## msLevel(sps_mzr) 602.752 648.9680 722.36111 732.3735 767.286 1328.504
## msLevel(sps_im) 16.425 24.9625 36.57062 37.1310 46.061 75.553
## 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: microseconds
## expr min lq mean
## peaksData(sps, BPPARAM = SerialParam()) 179609.508 210570.233 435571.904
## peaksData(sps_mzr, BPPARAM = SerialParam()) 734720.857 754154.899 1468766.022
## peaksData(sps_im, BPPARAM = SerialParam()) 548.076 893.429 2795.761
## median uq max neval cld
## 472503.361 589876.306 773991.10 10 a
## 1542365.033 2000052.843 2128588.55 10 b
## 1229.717 1496.322 17882.94 10 cAs 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) 141772.183 151239.179 216140.582 179848.938
## peaksData(sps_mzr, BPPARAM = m2) 679118.321 713906.389 984766.815 752415.454
## peaksData(sps_im, BPPARAM = m2) 576.232 797.642 1356.034 1038.514
## uq max neval cld
## 198746.76 619112.549 10 a
## 788389.85 1992323.779 10 b
## 1184.56 3137.828 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)) 4020.767 4313.604 4689.346 4564.6375
## filterRt(sps_mzr, rt = c(50, 100)) 3148.535 3308.447 3643.170 3487.3120
## filterRt(sps_im, rt = c(50, 100)) 688.348 751.235 826.522 790.4525
## uq max neval cld
## 4754.681 16666.294 100 a
## 3735.918 15276.903 100 b
## 856.575 2690.673 100 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 cld
## sps[idx] 195.946 216.6750 236.6283 244.2985 255.2515 295.363 100 a
## sps_mzr[idx] 986.829 998.7695 1021.9471 1005.4880 1017.1670 1849.060 100 b
## sps_im[idx] 297.894 316.3220 345.0002 331.7550 349.0835 1464.198 100 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) 1830.08 3452.003 4354.883 4642.511 5501.802
## peaksData(sps_mzr_10) 111292.51 115834.890 125995.823 122964.986 130627.026
## peaksData(sps_im_10) 353.30 726.696 1382.364 826.642 1013.053
## max neval cld
## 6037.731 10 a
## 157536.842 10 b
## 6626.847 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) (2024-10-21 r87258)
## 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
##
## 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_2.3.7 MsBackendSql_1.7.0
## [4] Spectra_1.17.0 BiocParallel_1.41.0 S4Vectors_0.45.0
## [7] BiocGenerics_0.53.0 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 hms_1.1.3 digest_0.6.37
## [7] grid_4.5.0 evaluate_1.0.1 bookdown_0.41
## [10] mvtnorm_1.3-1 fastmap_1.2.0 blob_1.2.4
## [13] Matrix_1.7-1 jsonlite_1.8.9 ProtGenerics_1.39.0
## [16] progress_1.2.3 mzR_2.41.0 DBI_1.2.3
## [19] survival_3.7-0 multcomp_1.4-26 BiocManager_1.30.25
## [22] TH.data_1.1-2 codetools_0.2-20 jquerylib_0.1.4
## [25] cli_3.6.3 rlang_1.1.4 crayon_1.5.3
## [28] Biobase_2.67.0 splines_4.5.0 bit64_4.5.2
## [31] cachem_1.1.0 yaml_2.3.10 tools_4.5.0
## [34] parallel_4.5.0 memoise_2.0.1 ncdf4_1.23
## [37] vctrs_0.6.5 R6_2.5.1 zoo_1.8-12
## [40] lifecycle_1.0.4 fs_1.6.4 IRanges_2.41.0
## [43] bit_4.5.0 clue_0.3-65 MASS_7.3-61
## [46] cluster_2.1.6 pkgconfig_2.0.3 bslib_0.8.0
## [49] data.table_1.16.2 Rcpp_1.0.13 xfun_0.48
## [52] knitr_1.48 htmltools_0.5.8.1 rmarkdown_2.28
## [55] compiler_4.5.0 prettyunits_1.2.0 MetaboCoreUtils_1.15.0