# Bioconductor Msdatahub

The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass...

- Repository: https://github.com/bioMate-AI/biomate-bioconductor-kb (793 stars)
- Category: DevOps
- License: see repository
- Last updated: 2026-06-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-msdatahub -g -a claude-code -y`
Codex: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-msdatahub -g -a codex -y`
Cursor: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-msdatahub -g -a cursor -y`
Gemini CLI: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-msdatahub -g -a gemini-cli -y`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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.

```r
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.

The rest: https://github.com/bioMate-AI/biomate-bioconductor-kb/blob/HEAD/skills/proteomics/msdatahub/SKILL.md