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.