| name | bioconductor-msdatahub |
| description | 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. |
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)
f_dda <- PestMix1_DDA.mzML()
s_dda <- Spectra(f_dda)
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.
References
