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Key Concepts & Glossary

Source: vignettes/articles/manual-01-key-concepts.Rmd
manual-01-key-concepts.Rmd

Manual

MRMhub organizes targeted mass spectrometry data around two axes: analyses, the individual injections or samples in a run, and features, the distinct signals extracted from the MS data. Every measurement is a value — typically a peak area — at the intersection of one analysis and one feature, and all of these values are held together with their metadata in a single object, the MRMhubExperiment. Functions take an MRMhubExperiment and return an updated one, and these calls are chained together to build a workflow. See The MRMhubExperiment Data Object for how the object is structured and Sample Types & QC Roles for the sample roles that drive its QC logic.

Glossary

A feature and an analyte are not one-to-one: one analyte can give several features (isotopes, adducts, transitions), and one feature can carry several analytes when they are isobaric or isomeric (e.g. SM/PC). The feature_id names the signal, while the analyte_id names the compound.

Reading the function names

A naming convention runs through the public API, so once it is internalised the function names are largely predictable:

  • import_data_*() — read raw integrated peak data from a given source
  • import_metadata_*() — read an annotation table from a given source
  • add_metadata() / set_*() — attach annotation or assign a slot on the experiment
  • normalize_by_*() — divide one variable by another (e.g. by ISTD)
  • correct_drift_*() / correct_batch_*() — apply a named correction model
  • quantify_by_*() — compute concentration values via a given strategy
  • calc_*() — compute per-feature or per-analysis metrics
  • filter_*() — apply pass/fail thresholds
  • plot_*() — return a ggplot2 object
  • save_*() — write to disk
  • get_*() — read a derived value from the experiment

Next steps