Package: MsBackendSql
Authors: Johannes Rainer [aut, cre] (https://orcid.org/0000-0002-6977-7147),
Chong Tang [ctb],
Laurent Gatto [ctb] (https://orcid.org/0000-0002-1520-2268)
Compiled: Wed Apr 17 18:09:14 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/RtmpeARCkw/file1aebe236c5c492
As 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 [Wed Apr 17 18:09:24 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/RtmpeARCkw/file1aebe2c7b00d8
## Processing:
## Switch backend from MsBackendSql to MsBackendMemory [Wed Apr 17 18:09:24 2024]
## Switch backend from MsBackendMemory to MsBackendOfflineSql [Wed Apr 17 18:09:25 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 0
All 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 Kb
The 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 + 10
Such 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 Kb
Such $<-
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 $msLevel <-
and test the timing to
extract these cached variable.
system.time(msLevel(sps))
## user system elapsed
## 0.008 0.004 0.012
sps$msLevel <- msLevel(sps)
system.time(msLevel(sps))
## user system elapsed
## 0.003 0.000 0.003
We 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/RtmpeARCkw/file1aebe236c5c492
This 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 neval
## msLevel(sps) 9.156528 9.697692 10.22272 10.00222 10.38695 17.80620 100
## msLevel(sps_off) 11.540665 11.977915 12.48326 12.33522 12.87124 17.61821 100
## cld
## a
## b
The 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 Kb
print(object.size(sps_mzr), units = "KB")
## 386.7 Kb
print(object.size(sps_im), units = "KB")
## 54494.5 Kb
The 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) 9774.683 10268.3610 10817.28761 10850.2420 10991.6135
## msLevel(sps_mzr) 597.802 651.0765 704.57736 708.1325 746.6535
## msLevel(sps_im) 15.917 21.4495 35.83183 38.2945 47.5140
## max neval cld
## 15595.737 100 a
## 1356.352 100 b
## 64.251 100 c
Extracting 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()) 153476.975 201423.166 476515.028
## peaksData(sps_mzr, BPPARAM = SerialParam()) 687792.107 713135.342 863958.577
## peaksData(sps_im, BPPARAM = SerialParam()) 910.552 1057.381 3320.735
## median uq max neval cld
## 542369.772 589672.375 749078.80 10 a
## 735172.474 799825.895 1978811.62 10 b
## 1353.228 1486.447 20404.07 10 c
As 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) 159083.187 175907.33 356737.398 196762.462
## peaksData(sps_mzr, BPPARAM = m2) 690646.356 698033.97 819845.380 734820.748
## peaksData(sps_im, BPPARAM = m2) 777.021 1043.63 1161.928 1120.894
## uq max neval cld
## 662778.861 926009.388 10 a
## 801758.149 1493212.358 10 b
## 1345.487 1555.474 10 c
We 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)) 4737.486 5293.8095 5852.827 5640.6010
## filterRt(sps_mzr, rt = c(50, 100)) 3337.533 3599.8905 4117.802 3844.8410
## filterRt(sps_im, rt = c(50, 100)) 740.734 860.0625 1001.278 952.2345
## uq max neval cld
## 6091.920 19823.927 100 a
## 4523.307 6211.041 100 b
## 1085.462 2992.723 100 c
The 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] 191.502 215.6385 268.8013 248.4905 260.393 3182.903 100 a
## sps_mzr[idx] 968.260 998.9050 1026.4213 1011.6365 1023.846 2405.161 100 b
## sps_im[idx] 282.199 305.8005 333.4840 324.0730 339.432 1416.336 100 a
Here 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) 2887.879 3077.705 3639.5189 3894.479 4143.082
## peaksData(sps_mzr_10) 111128.657 114509.561 120453.8469 117719.923 120975.674
## peaksData(sps_im_10) 345.590 423.803 480.7521 500.337 526.608
## max neval cld
## 4292.133 10 a
## 148816.648 10 b
## 607.329 10 a
The 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.
MsBackendSql
The 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 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-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] microbenchmark_1.4.10 RSQLite_2.3.6 MsBackendSql_1.3.5
## [4] Spectra_1.13.8 ProtGenerics_1.35.4 BiocParallel_1.37.1
## [7] S4Vectors_0.41.6 BiocGenerics_0.49.1 BiocStyle_2.31.0
##
## loaded via a namespace (and not attached):
## [1] sandwich_3.1-0 sass_0.4.9 MsCoreUtils_1.15.6
## [4] lattice_0.22-6 hms_1.1.3 digest_0.6.35
## [7] grid_4.4.0 evaluate_0.23 bookdown_0.39
## [10] mvtnorm_1.2-4 fastmap_1.1.1 blob_1.2.4
## [13] Matrix_1.7-0 jsonlite_1.8.8 progress_1.2.3
## [16] mzR_2.37.3 DBI_1.2.2 survival_3.5-8
## [19] multcomp_1.4-25 BiocManager_1.30.22 TH.data_1.1-2
## [22] codetools_0.2-20 jquerylib_0.1.4 cli_3.6.2
## [25] rlang_1.1.3 crayon_1.5.2 Biobase_2.63.1
## [28] splines_4.4.0 bit64_4.0.5 cachem_1.0.8
## [31] yaml_2.3.8 tools_4.4.0 parallel_4.4.0
## [34] memoise_2.0.1 ncdf4_1.22 vctrs_0.6.5
## [37] R6_2.5.1 zoo_1.8-12 lifecycle_1.0.4
## [40] fs_1.6.3 IRanges_2.37.1 bit_4.0.5
## [43] clue_0.3-65 MASS_7.3-60.2 cluster_2.1.6
## [46] pkgconfig_2.0.3 bslib_0.7.0 data.table_1.15.4
## [49] Rcpp_1.0.12 xfun_0.43 knitr_1.46
## [52] htmltools_0.5.8.1 rmarkdown_2.26 compiler_4.4.0
## [55] prettyunits_1.2.0 MetaboCoreUtils_1.11.3