The matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) technology is coupled with mass spectrometry in the Bruker MALDI Biotyper device in order to identify microorganisms. The device generates two types of data:
The following vignette describe how to streamline the import of these
two types of data into R using the {maldipickr}
package
The Bruker MALDI Biotyper generates a report via the Compass software summarizing the identification of the microorganisms using its internal database. While the file is separated by semi-colons, it contains no headers. The report has many columns in a wide format to describe the ten hits when identification is feasible, or only a few when no identification was possible. All-in-all, this makes the table import into R and its manipulation relatively painful.
Below is an example of an import of a single Bruker MALDI Biotyper
report into a {tibble}. By
default, only the best hit of each colony is reported. All hits can be
reported as well, in the long format
(long_format = TRUE), for further explorations with the {tidyverse}
suite.
# Get a example Bruker report
biotyper <- system.file("biotyper.csv", package = "maldipickr")
# Import the report as a tibble
report_tibble <- read_biotyper_report(biotyper)
# Display the tibble
report_tibble
#> # A tibble: 3 × 8
#> name sample_name hit_rank bruker_quality bruker_species bruker_taxid
#> <chr> <chr> <int> <chr> <chr> <dbl>
#> 1 targetA <NA> 1 - not reliable identif… NA
#> 2 targetB <NA> 1 +++ Escherichia coli 562
#> 3 targetC <NA> 1 +++ Kosakonia cowanii 208223
#> # ℹ 2 more variables: bruker_hash <chr>, bruker_log <dbl>During large-scale analysis, batches of identification are run and
can easily be imported using the read_many_biotyper_reports
function along with their custom-made metadata.
Below is an example of such usage, where one report was artificially extended into multiple reports.
# List of Bruker MALDI Biotyper reports
reports_paths <- system.file(
c("biotyper.csv", "biotyper.csv", "biotyper.csv"),
package = "maldipickr"
)
# Read the list of reports and combine them in a single tibble
read_many_biotyper_reports(
reports_paths,
report_ids = c("first", "second", "third"),
# Additional metadata below are passed to dplyr::mutate
growth_temperature = 37.0
)
#> # A tibble: 9 × 10
#> name original_name sample_name hit_rank bruker_quality bruker_species
#> <chr> <chr> <chr> <int> <chr> <chr>
#> 1 first_targetA targetA <NA> 1 - not reliable …
#> 2 first_targetB targetB <NA> 1 +++ Escherichia c…
#> 3 first_targetC targetC <NA> 1 +++ Kosakonia cow…
#> 4 second_targe… targetA <NA> 1 - not reliable …
#> 5 second_targe… targetB <NA> 1 +++ Escherichia c…
#> 6 second_targe… targetC <NA> 1 +++ Kosakonia cow…
#> 7 third_targetA targetA <NA> 1 - not reliable …
#> 8 third_targetB targetB <NA> 1 +++ Escherichia c…
#> 9 third_targetC targetC <NA> 1 +++ Kosakonia cow…
#> # ℹ 4 more variables: bruker_taxid <dbl>, bruker_hash <chr>, bruker_log <dbl>,
#> # growth_temperature <dbl>Other than the identification reports, the Bruker MALDI Biotyper
device outputs the raw data used for the identification (if not the
database) in the form of mass spectra. Thankfully, the {MALDIquant}
and {readBrukerFlexData}
packages help users import and manipulate these data in R.
However, when the Bruker MALDI Biotyper device produces
acqus files (instead of the native acqu
files), the readBrukerFlexDir()
function from the {readBrukerFlexData}
package will fail with the following error message:
Error in .readAcquFile(fidFile = fidFile, verbose = verbose) :
File ‘/data/maldi_dir/targetA/0_D10/1/1SLin/acqu’ doesn't exists!The following import_biotyper_spectra())
function used in the example below circumvent this error by creating a
symbolic link and conveniently helps removing calibration samples.
The toy dataset bundled with this package is a subset of a dataset in
the {MALDIquantExamples}
package and consist here of six spectra: * 1 replicate of species 1 * 2
replicates of species 2 * 3 replicates of species 3
# Get an example directory of six Bruker MALDI Biotyper spectra
directory_biotyper_spectra <- system.file(
"toy-species-spectra",
package = "maldipickr"
)
# Import the six spectra
spectra_list <- import_biotyper_spectra(directory_biotyper_spectra)
# Display the list of spectra
spectra_list
#> [[1]]
#> S4 class type : MassSpectrum
#> Number of m/z values : 20882
#> Range of m/z values : 1962.222 - 20146.522
#> Range of intensity values: 2.4e+02 - 3.608e+04
#> Memory usage : 337.047 KiB
#> Name : species1.G2
#> File : /tmp/RtmpKUvpf0/Rinstb3e1a7f4ccd/maldipickr/toy-species-spectra/species1/0_G2/1/1SLin/fid
#>
#> [[2]]
#> S4 class type : MassSpectrum
#> Number of m/z values : 20882
#> Range of m/z values : 1962.222 - 20146.522
#> Range of intensity values: 1.82e+02 - 1.006e+04
#> Memory usage : 337.047 KiB
#> Name : species2.E11
#> File : /tmp/RtmpKUvpf0/Rinstb3e1a7f4ccd/maldipickr/toy-species-spectra/species2/0_E11/1/1SLin/fid
#>
#> [[3]]
#> S4 class type : MassSpectrum
#> Number of m/z values : 20882
#> Range of m/z values : 1962.222 - 20146.522
#> Range of intensity values: 3.25e+02 - 2.115e+04
#> Memory usage : 337.047 KiB
#> Name : species2.E12
#> File : /tmp/RtmpKUvpf0/Rinstb3e1a7f4ccd/maldipickr/toy-species-spectra/species2/0_E12/1/1SLin/fid
#>
#> [[4]]
#> S4 class type : MassSpectrum
#> Number of m/z values : 20882
#> Range of m/z values : 1961.215 - 20135.904
#> Range of intensity values: 1.94e+02 - 2.055e+04
#> Memory usage : 337.047 KiB
#> Name : species3.F7
#> File : /tmp/RtmpKUvpf0/Rinstb3e1a7f4ccd/maldipickr/toy-species-spectra/species3/0_F7/1/1SLin/fid
#>
#> [[5]]
#> S4 class type : MassSpectrum
#> Number of m/z values : 20882
#> Range of m/z values : 1961.215 - 20135.904
#> Range of intensity values: 1.6e+02 - 1.814e+04
#> Memory usage : 337.047 KiB
#> Name : species3.F8
#> File : /tmp/RtmpKUvpf0/Rinstb3e1a7f4ccd/maldipickr/toy-species-spectra/species3/0_F8/1/1SLin/fid
#>
#> [[6]]
#> S4 class type : MassSpectrum
#> Number of m/z values : 20882
#> Range of m/z values : 1961.215 - 20135.904
#> Range of intensity values: 1.59e+02 - 1.449e+04
#> Memory usage : 337.047 KiB
#> Name : species3.F9
#> File : /tmp/RtmpKUvpf0/Rinstb3e1a7f4ccd/maldipickr/toy-species-spectra/species3/0_F9/1/1SLin/fidOnce the spectra are imported, the check_spectra()
function can easily assess whether all the spectra in the list are not
empty, of the same length and correspond to profile data. If some
spectra do not satisfy these criteria, the function will exit with a
warning and indicate the faulty spectra. Either way, the function
outputs a list of logical vectors (TRUE or
FALSE) indicating whether each of the spectra are empty
(is_empty), of an odd length
(is_outlier_length) or not a profile spectra
(is_not_regular).
# Get an example directory of six Bruker MALDI Biotyper spectra
directory_biotyper_spectra <- system.file(
"toy-species-spectra",
package = "maldipickr"
)
# Import the six spectra
spectra_list <- import_biotyper_spectra(directory_biotyper_spectra)
# Display the list of checks, with FALSE where no anomaly is detected
check_spectra(spectra_list)
#> $is_empty
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE
#>
#> $is_outlier_length
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE
#>
#> $is_not_regular
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE
# The overall sanity can be checked with Reduce
Reduce(any, check_spectra(spectra_list)) # Should be FALSE
#> [1] FALSE