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: Sun Apr 27 17:00:27 2025

1 Introduction

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.

2 Installation

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.

3 Creating and using MsBackendSql SQL databases

MsBackendSql 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: /private/var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T/Rtmp6xRphE/file9bd96f3bdbb0

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 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 [Sun Apr 27 17:00:31 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: /private/var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T/Rtmp6xRphE/file9bd97e089f44
## Processing:
##  Switch backend from MsBackendOfflineSql to MsBackendMemory [Sun Apr 27 17:00:31 2025]
##  Switch backend from MsBackendMemory to MsBackendOfflineSql [Sun Apr 27 17:00:31 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         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")
## 114.7 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")
## 129.4 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 
##   0.005   0.001   0.006
sps$msLevel <- msLevel(sps)
system.time(msLevel(sps))
##    user  system elapsed 
##   0.002   0.000   0.002

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)

4 Performance comparison with other backends

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")
## 113 Kb
print(object.size(sps_mzr), units = "KB")
## 401.4 Kb
print(object.size(sps_im), units = "KB")
## 54509.1 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))
## Warning in microbenchmark(msLevel(sps), msLevel(sps_mzr), msLevel(sps_im)):
## less accurate nanosecond times to avoid potential integer overflows
## Unit: microseconds
##              expr      min        lq       mean   median        uq      max
##      msLevel(sps) 2559.507 2668.4645 2982.73811 2870.861 3074.5695 5816.506
##  msLevel(sps_mzr)  170.888  184.2950  212.89332  195.611  213.7125  936.317
##   msLevel(sps_im)    5.371    6.9085    9.74201    9.430   11.7465   28.003
##  neval cld
##    100 a  
##    100  b 
##    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())  40210.422  49945.339 134890.012
##  peaksData(sps_mzr, BPPARAM = SerialParam()) 360051.914 391365.172 625597.819
##   peaksData(sps_im, BPPARAM = SerialParam())    195.898    463.505   1452.671
##       median         uq         max neval cld
##   80797.9825 228344.334  408041.143    10  a 
##  430344.9380 798312.804 1146206.127    10   b
##     694.3145    867.601    8156.253    10  a

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)  47592.349  60781.188  85157.8118  63734.1720
##  peaksData(sps_mzr, BPPARAM = m2) 348945.301 368329.937 648797.4767 590777.1180
##   peaksData(sps_im, BPPARAM = m2)    210.494    426.769    631.2442    598.1695
##         uq         max neval cld
##   70265.47  248330.768    10  a 
##  835162.13 1116426.228    10   b
##     817.95    1315.362    10  a

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       uq
##      filterRt(sps, rt = c(50, 100)) 939.269 971.8025 1095.3585 989.986 1012.208
##  filterRt(sps_mzr, rt = c(50, 100)) 584.947 631.3180  696.1091 680.026  721.969
##   filterRt(sps_im, rt = c(50, 100)) 197.005 220.0060  287.1624 235.627  252.232
##       max neval cld
##  7427.478   100 a  
##  1510.440   100  b 
##  3932.023   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]  61.746  65.067  80.0361  67.7730  71.6065 787.610   100 a  
##  sps_mzr[idx] 301.104 318.119 356.4364 328.8200 345.6300 788.430   100  b 
##   sps_im[idx] 104.673 109.798 124.8987 114.7795 121.7290 461.865   100   c

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)   679.534   785.150  2009.935  1823.721  3051.179
##  peaksData(sps_mzr_10) 63789.604 72656.059 76493.741 75164.849 80027.039
##   peaksData(sps_im_10)   130.708   168.059   590.441   398.028   834.391
##        max neval cld
##   4150.512    10  a 
##  86375.151    10   b
##   2136.346    10  a

The MsBackendSql outperforms the MsBackendMzR while, not unexpectedly, the MsBackendMemory provides fasted access.

4.1 Considerations for database systems/servers

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.

5 Other properties of the 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.

6 Summary

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.

7 Session information

sessionInfo()
## R version 4.5.0 Patched (2025-04-21 r88169)
## Platform: aarch64-apple-darwin20
## Running under: macOS Ventura 13.7.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## 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_1.9.0  
## [4] Spectra_1.19.0       BiocParallel_1.43.0  S4Vectors_0.47.0    
## [7] BiocGenerics_0.55.0  generics_0.1.3       BiocStyle_2.37.0    
## 
## loaded via a namespace (and not attached):
##  [1] sandwich_3.1-1         sass_0.4.10            MsCoreUtils_1.21.0    
##  [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_0.43          mvtnorm_1.3-3          fastmap_1.2.0         
## [13] blob_1.2.4             Matrix_1.7-3           jsonlite_2.0.0        
## [16] ProtGenerics_1.41.0    progress_1.2.3         mzR_2.43.0            
## [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_3.6.5              rlang_1.1.6           
## [28] crayon_1.5.3           Biobase_2.69.0         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_1.6.6               IRanges_2.43.0         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_1.17.0