Package: MsBackendMetabolomicsWorkbench
Authors: Gabriele Tomè [aut, cre] (ORCID:
https://orcid.org/0000-0002-3976-6068, fnd: MetaRbolomics4Galaxy
project (CUP: D53C25001030003) co-funded by the Autonomous Province
of Bolzano under the Joint Projects South Tyrol–Germany 2025
program.),
Philippine Louail [aut] (ORCID:
https://orcid.org/0009-0007-5429-6846),
Johannes Rainer [aut] (ORCID: https://orcid.org/0000-0002-6977-7147)
Last modified: 2026-07-31 13:55:35.175183
Compiled: Fri Jul 31 19:01:03 2026
Metabolomics experiments and results including mass spectrometry (MS) data can be deposited in several public repositories, such as Metabolomics Workbench repository, a data resource developed by the NIH Common Fund’s Data Repository and Coordinating Center (DRCC) at the San Diego Supercomputer Center, University of California San Diego. While data is available, manual lookup and download is cumbersome hampering the re-analysis of public data and replication of results. The MsBackendMetabolomicsWorkbench package closes this gap by providing functionality to query, retrieve and cache MS data from Metabolomics Workbench directly from R hence enabling a direct and seamless integration of MS data from Metabolomics Workbench into R-based analysis workflows. MsBackendMetabolomicsWorkbench leverages on Bioconductor’s BiocFileCache for caching remote data locally and provides a MS data backend for the Spectra package to enable loading and integrating cached MS data directly into R.
The package can be installed from within R with the commands below:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MsBackendMetabolomicsWorkbench")Each experiment in Metabolomics Workbench is identified by a unique accession
starting with ST followed by a number. The repository provides programmatic
access via the Metabolomics Workbench REST API and POST requests, so users can
query experiments and associated data files directly. With
MsBackendMetabolomicsWorkbench, you can resolve these accessions and download
supported MS files (mzML/CDF/mzXML) into a local cache, then load them into a
Spectra object for downstream processing.
Below we list all files from Metabolomics Workbench experiment ST002115.
library(MsBackendMetabolomicsWorkbench)## Loading required package: Spectra## Loading required package: S4Vectors## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: generics##
## Attaching package: 'generics'## The following objects are masked from 'package:base':
##
## as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
## setequal, union##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following object is masked from 'package:utils':
##
## data## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
## mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## rank, rbind, rownames, sapply, saveRDS, scale, sequence, table,
## tapply, transform, unique, unsplit, which.max, which.min##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:utils':
##
## findMatches## The following objects are masked from 'package:base':
##
## I, expand.grid, unname## Loading required package: BiocParallel#' List files of a Metabolomics Workbench data set
all_files <- mwb_list_files("ST002115")
head(all_files)## zip_file sample_file
## 1 ST002115_Data.zip HT1080_DMSO_01_RP.mzXML
## 3 ST002115_Data.zip HT1080_DMSO_02_RP.mzXML
## 5 ST002115_Data.zip HT1080_DMSO_03_RP.mzXML
## 7 ST002115_Data.zip HT1080_ML162_01_RP.mzXML
## 9 ST002115_Data.zip HT1080_ML162_02_RP.mzXML
## 11 ST002115_Data.zip HT1080_ML162_03_RP.mzXMLMS data files in supported formats (mzML, CDF, mzXML) can be directly loaded
using the MsBackendMetabolomicsWorkbench backend into R as a Spectra object
(MsBackendMetabolomicsWorkbench directly extends Spectra’s MsBackendMzR
backend and therefore supports import of MS data files in these formats). There
are two supported download modes:
Below we list zip file of Metabolomics Workbench experiment ST002115.
#' List zipped FTP files for a Metabolomics Workbench data set
mwb_ftp_list_files("ST002115")## [1] "ST002115_Data.zip"The FTP archive contains all files for the experiment, which may include
unneeded files. If only a subset of files is needed, the default POST option
(with ftp_zip = FALSE) is more efficient. By default, all MS data files of the
data set would be retrieved, but in our example below we restrict to a few data
files to reduce the amount of data that needs to be downloaded. To this end we
define a pattern matching the file name of only some data files using the
filePattern parameter.
library(Spectra)
#' Load MS data files of one data set
s <- Spectra("ST002115", filePattern = "01_RP.mzXML$", ftp_zip = FALSE,
source = MsBackendMetabolomicsWorkbench())
s## MSn data (Spectra) with 5751 spectra in a MsBackendMetabolomicsWorkbench backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 0 0.569753 1
## 2 0 1.627010 2
## 3 0 2.684380 3
## 4 0 3.739880 4
## 5 0 4.795500 5
## ... ... ... ...
## 5747 0 1498.87 1435
## 5748 0 1499.93 1436
## 5749 0 1500.98 1437
## 5750 0 1502.04 1438
## 5751 0 1503.10 1439
## ... 37 more variables/columns.
##
## file(s):
## ST002115_Data_HT1080_DMSO_01_RP.mzXML
## ST002115_Data_HT1080_ML162_01_RP.mzXML
## ST002115_Data_HT1080_ML210_01_RP.mzXML
## ... 1 more filesThis call downloaded 4 files from the experiment into the local cache and loaded
them as a Spectra object. The downloading and caching of the data is handled
by Bioconductor’s BiocFileCache. The local cache can thus also be
managed directly using functionality from that package. Any subsequent loading
of the same data files will load the locally cached versions avoiding thus
repetitive download of the same data.
The Spectra object with the MS data files of the Metabolomics Workbench data
set enables now any subsequent analysis of the data in R. On top of the spectra
variables and mass peak data values that are provided by the MS data files also
additional information related to the Metabolomics Workbench data set are
available as specific spectra variables. We list all available spectra
variables of the data set below.
spectraVariables(s)## [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" "electronBeamEnergy"
## [23] "ionisationEnergy" "lowMZ"
## [25] "highMZ" "mergedScan"
## [27] "mergedResultScanNum" "mergedResultStartScanNum"
## [29] "mergedResultEndScanNum" "injectionTime"
## [31] "filterString" "spectrumId"
## [33] "ionMobilityDriftTime" "scanWindowLowerLimit"
## [35] "scanWindowUpperLimit" "mwb_id"
## [37] "zip_file" "file_name"The Metabolomics Workbench-specific variables are "mwb_id", "zip_file" and
"file_name" providing the Metabolomics Workbench ID of the data set, the zip
file name in the FTP server and the original data file name in the Metabolomics
Workbench for each individual spectrum.
spectraData(s, c("mwb_id", "zip_file", "file_name"))## DataFrame with 5751 rows and 3 columns
## mwb_id zip_file file_name
## <character> <character> <character>
## 1 ST002115 ST002115_Data.zip HT1080_DMSO_01_RP.mz..
## 2 ST002115 ST002115_Data.zip HT1080_DMSO_01_RP.mz..
## 3 ST002115 ST002115_Data.zip HT1080_DMSO_01_RP.mz..
## 4 ST002115 ST002115_Data.zip HT1080_DMSO_01_RP.mz..
## 5 ST002115 ST002115_Data.zip HT1080_DMSO_01_RP.mz..
## ... ... ... ...
## 5747 ST002115 ST002115_Data.zip HT1080_RSL3_01_RP.mz..
## 5748 ST002115 ST002115_Data.zip HT1080_RSL3_01_RP.mz..
## 5749 ST002115 ST002115_Data.zip HT1080_RSL3_01_RP.mz..
## 5750 ST002115 ST002115_Data.zip HT1080_RSL3_01_RP.mz..
## 5751 ST002115 ST002115_Data.zip HT1080_RSL3_01_RP.mz..basename(s$file_name) |> head()## [1] "HT1080_DMSO_01_RP.mzXML" "HT1080_DMSO_01_RP.mzXML"
## [3] "HT1080_DMSO_01_RP.mzXML" "HT1080_DMSO_01_RP.mzXML"
## [5] "HT1080_DMSO_01_RP.mzXML" "HT1080_DMSO_01_RP.mzXML"The mwb_sync() function can be used to synchronize the local content of
a MsBackendMetabolomicsWorkbench and is useful if, for example, locally cached
files were deleted. The function checks if all data files of the backend are
available locally and eventually downloads and caches missing files.
mwb_sync(s)## MSn data (Spectra) with 5751 spectra in a MsBackendMetabolomicsWorkbench backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 0 0.569753 1
## 2 0 1.627010 2
## 3 0 2.684380 3
## 4 0 3.739880 4
## 5 0 4.795500 5
## ... ... ... ...
## 5747 0 1498.87 1435
## 5748 0 1499.93 1436
## 5749 0 1500.98 1437
## 5750 0 1502.04 1438
## 5751 0 1503.10 1439
## ... 37 more variables/columns.
##
## file(s):
## ST002115_Data_HT1080_DMSO_01_RP.mzXML
## ST002115_Data_HT1080_ML162_01_RP.mzXML
## ST002115_Data_HT1080_ML210_01_RP.mzXML
## ... 1 more filesIn addition, it is also possible to manually cache and download selected files
from Metabolomics Workbench using the mwb_sync_data_files() function. Before
downloading, this function first evaluates if the respective data files are
already cached and only downloads them if needed. As a result, the function
returns a data.frame with the storage location and other information of the
cached file(s). Below we use this function to retrieve the local storage
information on one of the data files of the Metabolomics Workbench data set
ST002115:
res <- mwb_sync_data_files("ST002115",
fileName = "HT1080_DMSO_01_RP.mzXML")
res## rid mwb_id zip_file file_name
## 1 BFC3136 ST002115 ST002115_Data.zip HT1080_DMSO_01_RP.mzXML
## rpath
## 1 /home/biocbuild/.cache/R/BiocFileCache/ST002115_Data_HT1080_DMSO_01_RP.mzXMLThe mwb_cached_data_files() function can be used to inspect and list all
locally cached Metabolomics Workbench data files. This function does not require
an active internet connection since only local content is queried. With the
default settings, a data.frame with all available data files is returned.
mwb_cached_data_files()## rid mwb_id zip_file file_name
## 2615 BFC3136 ST002115 ST002115_Data.zip HT1080_DMSO_01_RP.mzXML
## 2616 BFC3137 ST002115 ST002115_Data.zip HT1080_ML162_01_RP.mzXML
## 2617 BFC3138 ST002115 ST002115_Data.zip HT1080_ML210_01_RP.mzXML
## 2618 BFC3139 ST002115 ST002115_Data.zip HT1080_RSL3_01_RP.mzXML
## rpath
## 2615 /home/biocbuild/.cache/R/BiocFileCache/ST002115_Data_HT1080_DMSO_01_RP.mzXML
## 2616 /home/biocbuild/.cache/R/BiocFileCache/ST002115_Data_HT1080_ML162_01_RP.mzXML
## 2617 /home/biocbuild/.cache/R/BiocFileCache/ST002115_Data_HT1080_ML210_01_RP.mzXML
## 2618 /home/biocbuild/.cache/R/BiocFileCache/ST002115_Data_HT1080_RSL3_01_RP.mzXMLLocally cached files for a Metabolomics Workbench data set can be removed using
the mwb_delete_cache() function providing the ID of the Metabolomics
Workbench data set for which local data files should be removed.
Next to the MsBackendMetabolomicsWorkbench backend for Spectra objects, the
MsBackendMetabolomicsWorkbench package provides also various utility functions
to query and retrieve information from Metabolomics Workbench.
The mwb_rest_request() queries the Metabolomics Workbench REST API for a given
study/analysis ID and output item (e.g. summary, factors). Returns the raw
response as a character string in the format specified by outputFormat
(json or txt).
Below we query the REST API for the summary of the Metabolomics Workbench data set ST002115:
library(jsonlite)
summary <- mwb_rest_request("ST002115", outputItem = "summary",
outputFormat = "json")
fromJSON(summary)## $study_id
## [1] "ST002115"
##
## $study_title
## [1] "LC-MS analysis of metabolic changes induced by GPX4 inhibitor treatment in cultured HT1080 cells"
##
## $species
## [1] "Homo sapiens"
##
## $institute
## [1] "University of Texas MD Anderson Cancer Center"
##
## $analysis_type
## [1] "LC-MS"
##
## $number_of_samples
## [1] "12"
##
## $submission_date
## [1] "2022-04-20"
##
## $release_date
## [1] "2022-04-14"
##
## $version
## [1] "1"
##
## $revision_no
## [1] "1"
##
## $revision_datetime
## [1] "-"
##
## $revision_comment
## [1] "-"
##
## $license
## [1] "CC BY 4.0"
##
## $license_url
## [1] "https://creativecommons.org/licenses/by/4.0/"
##
## $study_url
## [1] "https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?StudyID=ST002115"The mwb_ftp_download() function allows to download the zip archive of the
experiment (directly, i.e., without caching). As an example we download below
the zip archive to a temporary folder. In our example below we do not run it to
reduce the amount of data that needs to be downloaded.
mwb_ftp_download("ST002115", path = tempdir())The mwb_metadata() function retrieves the metadata of a given Metabolomics
Workbench data set as a list of 2 data.frame:
- MS_run: contains the metadata of the MS runs of the data set, identified
by the analysis ID(s),
- sample_annotation: contains the metadata of the samples of the data set.
Not all the experiments have a column with the associated sample file
name, the association cab be retrieved by the mwb_list_files() function.
The function handles the case of multiple analysis IDs by combining the metadata
of all analysis IDs into a single data.frame for the experiment and a single
data.frame for the sample annotation.
Below we retrieve the metadata of the data set ST002115:
meta <- mwb_metadata("ST002115")
meta$MS_run## STUDY_ID ANALYSIS_ID VERSION CREATED_ON
## 1 ST002115 AN003513 1 02-08-2024
## 2 ST002115 AN003514 1 02-08-2024
## PROJECT_TITLE
## 1 A ferroptosis defense mechanism mediated by glycerol 3-phosphate dehydrogenase 2 in mitochondria
## 2 A ferroptosis defense mechanism mediated by glycerol 3-phosphate dehydrogenase 2 in mitochondria
## PROJECT_SUMMARY
## 1 Mechanisms of defense against ferroptosis (an iron-dependent form of cell death induced by lipid peroxidation) in cellular organelles remain poorly understood, hindering our ability to target ferroptosis in disease treatment. In this study, metabolomic analyses revealed that treatment of cancer cells with glutathione peroxidase 4 (GPX4) inhibitors results in intracellular glycerol 3-phosphate (G3P) depletion. We further showed that supplementation of cancer cells with G3P attenuates ferroptosis induced by GPX4 inhibitors in a G3P dehydrogenase 2 (GPD2)-dependent manner; GPD2 deletion sensitizes cancer cells to GPX4 inhibition-induced mitochondrial lipid peroxidation and ferroptosis, and combined deletion of GPX4 and GPD2 synergistically suppresses tumor growth by inducing ferroptosis in vivo. Mechanistically, inner mitochondrial membrane-localized GPD2 couples G3P oxidation with ubiquinone reduction to ubiquinol, which acts as a radical-trapping antioxidant to suppress ferroptosis in mitochondria. Taken together, these results reveal that GPD2 participates in ferroptosis defense in mitochondria by generating ubiquinol.
## 2 Mechanisms of defense against ferroptosis (an iron-dependent form of cell death induced by lipid peroxidation) in cellular organelles remain poorly understood, hindering our ability to target ferroptosis in disease treatment. In this study, metabolomic analyses revealed that treatment of cancer cells with glutathione peroxidase 4 (GPX4) inhibitors results in intracellular glycerol 3-phosphate (G3P) depletion. We further showed that supplementation of cancer cells with G3P attenuates ferroptosis induced by GPX4 inhibitors in a G3P dehydrogenase 2 (GPD2)-dependent manner; GPD2 deletion sensitizes cancer cells to GPX4 inhibition-induced mitochondrial lipid peroxidation and ferroptosis, and combined deletion of GPX4 and GPD2 synergistically suppresses tumor growth by inducing ferroptosis in vivo. Mechanistically, inner mitochondrial membrane-localized GPD2 couples G3P oxidation with ubiquinone reduction to ubiquinol, which acts as a radical-trapping antioxidant to suppress ferroptosis in mitochondria. Taken together, these results reveal that GPD2 participates in ferroptosis defense in mitochondria by generating ubiquinol.
## INSTITUTE LAST_NAME FIRST_NAME
## 1 University of Texas MD Anderson Cancer Center Gan Boyi
## 2 University of Texas MD Anderson Cancer Center Gan Boyi
## ADDRESS EMAIL PHONE
## 1 6565 MD Anderson Blvd, Houston, TX 77030 bgan@mdanderson.org 713-792-8653
## 2 6565 MD Anderson Blvd, Houston, TX 77030 bgan@mdanderson.org 713-792-8653
## DOI
## 1 http://dx.doi.org/10.21228/M8HD8Q
## 2 http://dx.doi.org/10.21228/M8HD8Q
## STUDY_TITLE
## 1 LC-MS analysis of metabolic changes induced by GPX4 inhibitor treatment in cultured HT1080 cells
## 2 LC-MS analysis of metabolic changes induced by GPX4 inhibitor treatment in cultured HT1080 cells
## STUDY_SUMMARY
## 1 HT1080 cells were treated with vehicle (DMSO), RSL3 (10 micromolar), ML210 (10 micromolar), or ML162 (10 micromolar) for 2 hours. Cellular metabolites were then extracted and analyzed by LC-MS.
## 2 HT1080 cells were treated with vehicle (DMSO), RSL3 (10 micromolar), ML210 (10 micromolar), or ML162 (10 micromolar) for 2 hours. Cellular metabolites were then extracted and analyzed by LC-MS.
## SUBMIT_DATE SUBJECT_TYPE SUBJECT_SPECIES TAXONOMY_ID CELL_STRAIN_DETAILS
## 1 2022-03-02 Cultured cells Homo sapiens 9606 HT1080
## 2 2022-03-02 Cultured cells Homo sapiens 9606 HT1080
## COLLECTION_SUMMARY
## 1 Metabolites were extracted from cells in 35 mm culture plates by rapidly aspirating the culture medium and incubating the plates with 0.6 ml of an 80% methanol: 20% water mixture on a cold block on dry ice for 15 min. Next, the cell material was scraped into Eppendorf tubes pre-chilled on ice. After centrifugation at 13,000 RCF for 5 min at 4 °C, the supernatant was collected into a fresh tube and stored on dry ice until analysis.
## 2 Metabolites were extracted from cells in 35 mm culture plates by rapidly aspirating the culture medium and incubating the plates with 0.6 ml of an 80% methanol: 20% water mixture on a cold block on dry ice for 15 min. Next, the cell material was scraped into Eppendorf tubes pre-chilled on ice. After centrifugation at 13,000 RCF for 5 min at 4 °C, the supernatant was collected into a fresh tube and stored on dry ice until analysis.
## SAMPLE_TYPE STORAGE_CONDITIONS
## 1 Cultured cells -80?
## 2 Cultured cells -80?
## TREATMENT_SUMMARY
## 1 Cells were seeded in 35-mm culture plates. When the cell confluence reached 70-80%, cells were treated with RSL3, ML210, or ML162 for 2 hours.
## 2 Cells were seeded in 35-mm culture plates. When the cell confluence reached 70-80%, cells were treated with RSL3, ML210, or ML162 for 2 hours.
## SAMPLEPREP_SUMMARY
## 1 For analysis by reverse phase chromatography, just before analysis, 500 µL of extract was dried under a nitrogen gas flow and then resuspended in 100 µL of water. For analysis by HILIC chromatography, the extracts were analyzed directly.
## 2 For analysis by reverse phase chromatography, just before analysis, 500 µL of extract was dried under a nitrogen gas flow and then resuspended in 100 µL of water. For analysis by HILIC chromatography, the extracts were analyzed directly.
## PROCESSING_STORAGE_CONDITIONS EXTRACT_STORAGE
## 1 4? 4?
## 2 4? 4?
## CHROMATOGRAPHY_SUMMARY
## 1 The gradient was 0 min, 0% B; 2.5 min, 0% B; 5 min, 20% B; 7.5 min, 20% B; 13 min, 55% B; 15.5 min, 95% B; 18.5 min, 95% B; 19 min, 0% B; and 25 min, 0% B. Solvent A was 10 mM tributylamine and 15 mm acetic acid in water; Solvent B was methanol. The injection volume was 10 µL.
## 2 The gradient was 0 min, 85% B; 2 min, 85% B; 3 min, 80% B; 5 min, 80% B; 6 min, 75% B; 7 min, 75% B; 8 min, 70% B; 9 min, 70% B; 10 min, 50% B; 12 min, 50% B; 13 min, 25% B; 16 min, 25% B; 18 min, 0% B; 23 min, 0% B; 24 min, 85% B; 30 min, 85% B. The injection volume was 5 µL.
## INSTRUMENT_NAME COLUMN_NAME
## 1 Thermo Accela 1250 Phenomenex Synergi Hydro RP 100 A (100 x 2mm,2.5um)
## 2 Thermo Accela 1250 Waters XBridge BEH Amide (150 x 2.1mm,2.5um,100A)
## COLUMN_TEMPERATURE FLOW_RATE
## 1 40 200
## 2 40 150
## SOLVENT_A SOLVENT_B
## 1 100% water; 15 mM acetic acid; 10 mM tributylamine 100% methanol
## 2 95% water/5% acetonitrile; 20 mM ammonium acetate, pH 9.4 100% acetonitrile
## CHROMATOGRAPHY_TYPE ANALYSIS_TYPE INSTRUMENT_TYPE MS_TYPE
## 1 Reversed phase MS Orbitrap ESI
## 2 HILIC MS Orbitrap ESI
## MS_COMMENTS
## 1 The scan range was 80-1000 m/z. Raw data files were converted to mzXML format using msconvert (ProteoWizard). Data was analyzed in the MAVEN software suite and metabolite assignments were made using a previously generated list of retention times derived from pure standard solutions.
## 2 The scan range was 80-1000 m/z. Raw data files were converted to mzXML format using msconvert (ProteoWizard). Data was analyzed in the MAVEN software suite and metabolite assignments were made using a previously generated list of retention times derived from pure standard solutions.
## ION_MODE
## 1 NEGATIVE
## 2 NEGATIVEmeta$sample_annotation## Subject ID Sample ID Factors: Treatment
## 1 - HT1080_DMSO_01 DMSO
## 2 - HT1080_DMSO_02 DMSO
## 3 - HT1080_DMSO_03 DMSO
## 4 - HT1080_ML162_01 ML162
## 5 - HT1080_ML162_02 ML162
## 6 - HT1080_ML162_03 ML162
## 7 - HT1080_ML210_01 ML210
## 8 - HT1080_ML210_02 ML210
## 9 - HT1080_ML210_03 ML210
## 10 - HT1080_RSL3_01 RSL3
## 11 - HT1080_RSL3_02 RSL3
## 12 - HT1080_RSL3_03 RSL3
## Additional sample data: RAW_FILE_NAME
## 1 HT1080_DMSO_01.mzXML
## 2 HT1080_DMSO_02.mzXML
## 3 HT1080_DMSO_03.mzXML
## 4 HT1080_ML162_01.mzXML
## 5 HT1080_ML162_02.mzXML
## 6 HT1080_ML162_03.mzXML
## 7 HT1080_ML210_01.mzXML
## 8 HT1080_ML210_02.mzXML
## 9 HT1080_ML210_03.mzXML
## 10 HT1080_RSL3_01.mzXML
## 11 HT1080_RSL3_02.mzXML
## 12 HT1080_RSL3_03.mzXML
## Additional sample data: RAW_FILE_NAME_2
## 1 HT1080_DMSO_01_HILIC.mzXML
## 2 HT1080_DMSO_02_HILIC.mzXML
## 3 HT1080_DMSO_03_HILIC.mzXML
## 4 HT1080_ML162_01_HILIC.mzXML
## 5 HT1080_ML162_02_HILIC.mzXML
## 6 HT1080_ML162_03_HILIC.mzXML
## 7 HT1080_ML210_01_HILIC.mzXML
## 8 HT1080_ML210_02_HILIC.mzXML
## 9 HT1080_ML210_03_HILIC.mzXML
## 10 HT1080_RSL3_01_HILIC.mzXML
## 11 HT1080_RSL3_02_HILIC.mzXML
## 12 HT1080_RSL3_03_HILIC.mzXMLsessionInfo()## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.24-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] jsonlite_2.0.0 MsBackendMetabolomicsWorkbench_0.99.1
## [3] Spectra_1.23.3 BiocParallel_1.47.0
## [5] S4Vectors_0.51.6 BiocGenerics_0.59.10
## [7] generics_0.1.4 BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] xfun_0.60 bslib_0.11.0 httr2_1.3.0
## [4] websocket_1.4.4 processx_3.9.0 Biobase_2.73.2
## [7] vctrs_0.7.3 tools_4.6.1 ps_1.9.3
## [10] curl_7.1.0 parallel_4.6.1 tibble_3.3.1
## [13] RSQLite_3.53.3 cluster_2.1.8.3 blob_1.3.0
## [16] pkgconfig_2.0.3 data.table_1.18.4 dbplyr_2.6.0
## [19] lifecycle_1.0.5 compiler_4.6.1 chromote_0.5.1
## [22] codetools_0.2-20 ncdf4_1.24 clue_0.3-68
## [25] htmltools_0.5.9 sass_0.4.10 yaml_2.3.12
## [28] pillar_1.11.1 later_1.4.8 jquerylib_0.1.4
## [31] tidyr_1.3.2 MASS_7.3-66 cachem_1.1.0
## [34] MetaboCoreUtils_1.21.1 tidyselect_1.2.1 rvest_1.0.5
## [37] digest_0.6.39 purrr_1.2.2 dplyr_1.2.1
## [40] bookdown_0.47 fastmap_1.2.0 archive_1.1.14
## [43] cli_3.6.6 magrittr_2.0.5 withr_3.0.3
## [46] filelock_1.0.3 promises_1.5.0 bit64_4.8.2
## [49] rmarkdown_2.31 httr_1.4.8 bit_4.6.0
## [52] otel_0.2.0 memoise_2.0.1 evaluate_1.0.5
## [55] knitr_1.51 IRanges_2.47.2 BiocFileCache_3.3.0
## [58] rlang_1.3.0 Rcpp_1.1.2 glue_1.8.1
## [61] DBI_1.3.0 mzR_2.47.0 selectr_0.6-0
## [64] BiocManager_1.30.27 xml2_1.6.0 R6_2.6.1
## [67] plyr_1.8.9 fs_2.1.0 ProtGenerics_1.45.0
## [70] MsCoreUtils_1.25.4