Articles • mrmhub

All vignettes

Installation: Install mrmhub, verify your setup, and fix common install errors.
MRMhub overview: A technical overview of MRMhub.
The MRMhubExperiment data object: The primary data container of the MRMhub workflow: its data and metadata tables, the identifiers that link them, and the feature variables it stores.
Design decisions behind MRMhub QUANT: The main architectural choices behind MRMhub QUANT and the reasoning for each – for contributors and users who want to understand or extend the package.
Importing analytical data: Importing analytical data from different sources into an MRMhubExperiment.
Metadata tables & matching: The metadata tables MRMhub uses (analyses, features, ISTDs, response curves and QC concentrations), their structure, and how identifiers are matched to the data.
Sample types & QC roles: Reference for the QC-type labels used in MRMhub and their roles in quality control and data processing.
Drift and batch correction: Run-order drift and batch-effect correction: the available methods, their parameters, and when to use each.
Visualisation functions: Reference for the MRMhub plotting functions, grouped by workflow stage, with the canonical argument forms and customisation guidance.
Getting help from an AI assistant: How to use Claude, ChatGPT, or a local model to plan, draft, and troubleshoot MRMhub QUANT workflows without exposing study data.
Troubleshooting and FAQ: Solutions to the most common errors and questions when using MRMhub QUANT.
Quarto workflows: Preserving MRMhub’s coloured console feedback and controlling figure size when a workflow notebook is rendered.
Isotopic interference correction: Reference for MRMhub’s LICAR-based isotopic interference correction: the mrm_pattern labels, the MRM and MS1 derivation levels, and the co-elution rule.
Function map: Every MRMhub function, grouped by pipeline stage and linked to its reference.
Glossary: Definitions of the analytical terms used throughout the MRMhub documentation.
Manual: Complete contents of the MRMhub-QUANT manual.
MRMhub
Preparing and importing data: Import analytical data from the supported platforms and bring in the sample and feature metadata that the MRMhub workflow depends on.
Getting started with MRMhub: Create a MRMhubExperiment, import the bundled demo data, then normalize, plot, and export — a first end-to-end MRMhub analysis in a Quarto notebook.
Lipidomics data processing: Post-process a targeted lipidomics run from peak areas to a curated, quantified dataset: quality assessment, ISTD normalisation, drift and batch correction, and QC filtering.
Drift and batch correction: Correct run-order signal drift and between-batch effects from QC samples, and combine the two in the recommended order.
Exploring QC: RunScatter and PCA: Read run-order signal quality with RunScatter and multivariate structure with PCA, using the built-in lipidomics dataset.
Quantification with external calibration: Fit external calibration curves for a targeted assay, quantify the samples, and check the result against QC samples with known concentrations.
Calibration by a reference sample: Re-calibrate or normalise concentrations against a reference sample (e.g. NIST SRM1950), apply it batch-wise, and check the reference bias.
Exporting to standard and community formats: Move a processed MRMhubExperiment into interchange formats: mzTab-M for repositories, or a Bioconductor SummarizedExperiment for downstream analysis.
Correcting isotopic interference: Annotate the mrm_pattern, derive the M+2 overlaps, inspect them, and subtract them — the step-by-step isotopic interference-correction workflow.
Using the interactive workflow builder: Use the point-and-click builder to turn a data file into a runnable Quarto workflow.
Getting started with R and Quarto notebooks: Install R and an IDE, create a Quarto project, learn to read and run notebook code, and render a report — for readers new to R and Quarto.