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Use bioconductor-msdatahub to retrieve and import the documented [example MS dataset] through its cache accessor into the appropriate Spectra or QFeatures object, using [cache destination] and [label-free or mTRAQ context].
Required inputs
[example MS dataset]
[cache destination]
[label-free or mTRAQ context]
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SKILL.md
name: bioconductor-msdatahubdescription: The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass spectrometry data files, peptide spectrum matches or quantitative data from proteomics and metabolomics experiments.when_to_use: Use when: The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass spectrometry data files, peptide spec; data using datatable; install1 using install; lfdia using readQFeaturesFromDIANN; pledia using readQFeaturesFromDIANN; Mass Spectrometry Data on ExperimentHub. Not for: Requires R ≥ 4.0.0 and Bioconductor ≥ 3.16user-invocable: false
MsDataHub
Workflows
Standard Workflow
The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass spectrometry data files, peptide spectrum matches or quantitative data from proteomics and metabolomics experiments.
library(MsDataHub)
library(Spectra)
library(QFeatures)
# 1. Load raw MS data (TripleTOF DDA) and create a Spectra object
f_dda <- PestMix1_DDA.mzML()
s_dda <- Spectra(f_dda)
# 2. Load DIA-NN label-free DIA data and read into QFeatures
lfdia <- read.delim(MsDataHub::benchmarkingDIA.tsv())
qf <- readQFeaturesFromDIANN(lfdia)
Note: Inputs are cached file paths retrieved from MsDataHub accessor functions; outputs are Spectra or QFeatures objects containing mass spectrometry data.
When to Use
Accessing raw mass spectrometry data files: Retrieve example raw files (e.g., .mzML, .CDF) from standard proteomics/metabolomics experiments via ExperimentHub caching.
Retrieving peptide spectrum matches (PSMs): Load identification files (e.g., .mzid) using PSM() from the PSMatch package.
Loading quantitative proteomics data tables: Import tab-delimited tables (e.g., MaxQuant peptide tables, DIA-NN outputs) into SummarizedExperiment or QFeatures structures using readSummarizedExperiment() or readQFeaturesFromDIANN().
When NOT to Use
For raw MS data preprocessing: For peak picking, alignment, or retention time correction, use xcms because MsDataHub only distributes pre-existing example datasets.
For statistical differential abundance analysis: For downstream statistical modeling of quantitative proteomics data, use MSstats because MsDataHub is a data repository rather than an analytical tool.
For general-purpose biological annotation: For annotating genes or proteins, use AnnotationDbi or biomaRt because MsDataHub only provides mass spectrometry-specific data and contaminant databases.
Data Requirements
Raw MS data files: Open formats such as .mzML (e.g., PestMix1_DDA.mzML()), .CDF (e.g., ko15.CDF()), or compressed .mzML.gz.
Peptide spectrum matches: Standard .mzid format (e.g., TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.20141210.mzid()).
Tab-delimited quantitative tables: Text files (e.g., cptac_peptides.txt(), benchmarkingDIA.tsv()) containing peptide intensities or DIA-NN report outputs.
Key Parameters
multiplexing (NULL): In readQFeaturesFromDIANN(), set to "mTRAQ" for multiplexed plexDIA data.
sep ("\t"): In readSummarizedExperiment(), specifies the field separator character for reading text files.
ecols (integer vector): In readSummarizedExperiment(), specifies the column indices containing quantitative intensity data.
Best Practices
Use MsDataHub() inside DT::datatable() to browse the complete list of available datasets and their metadata.
Load raw data files into a Spectra object using Spectra(f) to leverage memory-efficient backends.
When reading DIA-NN outputs, use read.delim() to load the file path returned by MsDataHub::benchmarkingDIA.tsv() before passing it to readQFeaturesFromDIANN().
Common Pitfalls
Attempting to read raw files directly without caching: Call the specific dataset function (e.g., PestMix1_DDA.mzML()) to download and retrieve the local cache path first.
Mismatched intensity column selection in MaxQuant tables: Use grep("Intensity\\.", names(read.delim(f))) to dynamically identify quantitative columns before calling readSummarizedExperiment().
Incorrect multiplexing parameter for label-free DIA: Ensure multiplexing is omitted or set correctly when using readQFeaturesFromDIANN() on label-free vs mTRAQ plexDIA data.
Alternatives
xcms: For raw LC-MS data preprocessing and peak alignment.
MSnbase: For legacy mass spectrometry data container management and processing.
Spectra: For modern, infrastructure-independent representation of mass spectrometry spectra.
MSstats: For downstream statistical analysis of quantitative proteomics experiments.
Citations
Gatto L (2026). MsDataHub: Mass Spectrometry Data on ExperimentHub. R package version 1.12.0.