# MRMhub-QUANT

**MRMhub-QUANT** turns targeted MRM feature intensities into curated,
QC-filtered, quantified results. It is the post-processing module of
[MRMhub](https://slinghub.github.io/MRMhub/), distributed as the R
package `mrmhub`
([`library(mrmhub)`](https://github.com/SLINGhub/MRMhub)), and works
with any intensity data — from
[MRMhub-INTEGRATOR](https://slinghub.github.io/MRMhub/integrator/),
Skyline, Agilent MassHunter, or generic CSV files. MRMhub-QUANT
features:

- **Reproducible pipelines.** Create reproducible computational
  pipelines with QC vizualizations. Script, re-run, and share it.
- **Flexible workflows.** Metabolomics and lipidomics data
  post-processing using dedicated customizable functions.
- **A single data object.** Data, metadata, and processing details are
  stored in single sharable data object (`MRMhubExperiment`).

![](data:image/svg+xml;base64,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)

Visualise at each step with [QC
plots](https://slinghub.github.io/MRMhub/quant/articles/manual-08-visualization.md)
— RunScatter, PCA, run-sequence, normalization QC.

[Run your first analysis (5 min)
→](https://slinghub.github.io/MRMhub/quant/articles/tutorial-00-first-analysis.md)

Prefer point-and-click?
[`build_workflow()`](https://slinghub.github.io/MRMhub/quant/reference/build_workflow.md)
opens a guided app that validates your data and metadata, warns about
pipeline mismatches, and generates a downloadable Quarto (`.qmd`)
workflow.

## Quick Start and Demos

- **[Installation](https://slinghub.github.io/MRMhub/quant/articles/manual-00-installation.md)**
  — install and verify your setup
- **[Your First
  Analysis](https://slinghub.github.io/MRMhub/quant/articles/tutorial-00-first-analysis.md)**
  — a 5-minute run on bundled data
- **[Prepare your
  data](https://slinghub.github.io/MRMhub/quant/articles/manual-04-data-import.md)**
  — file formats and importers
- **[Example: targeted
  lipidomics](https://slinghub.github.io/MRMhub/quant/articles/tutorial-03-lipidomics-workflow.md)**
  — a full lipidomics workflow
- **[Example: external
  calibration](https://slinghub.github.io/MRMhub/quant/articles/recipe-01-ext-calibration-qc.md)**
  — a quantitative assay with calibration curves

## Installation and Updating

Make sure to use a fresh R session without loaded packages (quit
RStudio/Positron first to avoid locked packages):

``` r

if (!require("pak")) install.packages("pak")
pak::pak("SLINGhub/MRMhub")
library(mrmhub); mrmhub::check_setup()
```

`pak` resolves locked packages and parallelises downloads;
`remotes::install_github("SLINGhub/MRMhub")` is an equivalent fallback.
For more details and troubleshooting see
[Installation](https://slinghub.github.io/MRMhub/quant/articles/manual-00-installation.md)
and [Troubleshooting &
FAQ](https://slinghub.github.io/MRMhub/quant/articles/manual-10-troubleshooting.md).

## Contributing

Questions, bug reports, feature requests, and suggestions are welcome
via [GitHub issues](https://github.com/SLINGhub/MRMhub/issues). The
project is released with a [Contributor Code of
Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).

## Dual licensing

The source code is dual-licensed: [GNU
AGPLv3](https://www.gnu.org/licenses/agpl-3.0.en.html) for
non-commercial use, or commercial licensing — contact Jonathan Tan
(<jonathan_tan@nus.edu.sg>).

# Package index

## Getting started

Functions to validate your R environment and explore the MRMhub workflow
interactively.

- [`check_setup()`](https://slinghub.github.io/MRMhub/quant/reference/check_setup.md)
  : Check MRMhub setup
- [`build_workflow()`](https://slinghub.github.io/MRMhub/quant/reference/build_workflow.md)
  : Launch the MRMhub Workflow Builder
- [`generate_workflow_qmd()`](https://slinghub.github.io/MRMhub/quant/reference/generate_workflow_qmd.md)
  : Generate a runnable Quarto (.qmd) mrmhub workflow

## QUANT R package reference

Functions to create, access and query MRMhubExperiment objects, which
are the central data object in the MRMhub workflow.

- [`MRMhubExperiment()`](https://slinghub.github.io/MRMhub/quant/reference/MRMhubExperiment.md)
  : Constructor for the MRMhubExperiment object
- [`MRMhubExperiment-class`](https://slinghub.github.io/MRMhub/quant/reference/MRMhubExperiment-class.md)
  : S4 class representing the MRMhub dataset
- [`` `$`( ``*`<MRMhubExperiment>`*`)`](https://slinghub.github.io/MRMhub/quant/reference/cash-MRMhubExperiment-method.md)
  : Access slots of a MRMhubExperiment object via \$ syntax
- [`set_analysis_order()`](https://slinghub.github.io/MRMhub/quant/reference/set_analysis_order.md)
  : Set analysis order
- [`get_batch_boundaries()`](https://slinghub.github.io/MRMhub/quant/reference/get_batch_boundaries.md)
  : Get the start and end analysis numbers of specified batches
- [`data_sum_features()`](https://slinghub.github.io/MRMhub/quant/reference/data_sum_features.md)
  : Sum up feature intensities per analyte
- [`exclude_analyses()`](https://slinghub.github.io/MRMhub/quant/reference/exclude_analyses.md)
  : Exclude analyses from the dataset
- [`exclude_features()`](https://slinghub.github.io/MRMhub/quant/reference/exclude_features.md)
  : Exclude features from the dataset
- [`get_analyticaldata()`](https://slinghub.github.io/MRMhub/quant/reference/get_analyticaldata.md)
  : Get the annotated or the originally imported analytical data
- [`set_intensity_var()`](https://slinghub.github.io/MRMhub/quant/reference/set_intensity_var.md)
  : Set default variable to be used as feature raw signal value
- [`get_analysis_count()`](https://slinghub.github.io/MRMhub/quant/reference/get_analysis_count.md)
  : Get the number of analyses in the dataset
- [`get_analyis_start()`](https://slinghub.github.io/MRMhub/quant/reference/get_analyis_start.md)
  : Get the start time of the analysis sequence
- [`get_analyis_end()`](https://slinghub.github.io/MRMhub/quant/reference/get_analyis_end.md)
  : Get the end time of the analysis sequence
- [`get_analysis_breaks()`](https://slinghub.github.io/MRMhub/quant/reference/get_analysis_breaks.md)
  : Get the number of analysis breaks in the analysis
- [`get_analysis_duration()`](https://slinghub.github.io/MRMhub/quant/reference/get_analysis_duration.md)
  : Get the total duration of the analysis
- [`get_runtime_median()`](https://slinghub.github.io/MRMhub/quant/reference/get_runtime_median.md)
  : Get the median run time
- [`get_feature_count()`](https://slinghub.github.io/MRMhub/quant/reference/get_feature_count.md)
  : Get the number of features in the dataset
- [`get_featurelist()`](https://slinghub.github.io/MRMhub/quant/reference/get_featurelist.md)
  : Get feature IDs

## Analysis data import

Functions to import analytical data from different sources into
MRMhubExperiment objects. Additionally, the file parser function used
internally by these import functions are available for direct use,
i.e. to import different analytical data into data frames.

- [`import_data_mrmhub()`](https://slinghub.github.io/MRMhub/quant/reference/import_data_mrmhub.md)
  : Import MRMhub peak integration results
- [`import_data_masshunter()`](https://slinghub.github.io/MRMhub/quant/reference/import_data_masshunter.md)
  : Import Agilent MassHunter Quantitative Analysis CSV files
- [`import_data_skyline()`](https://slinghub.github.io/MRMhub/quant/reference/import_data_skyline.md)
  : Import Skyline peak integration results
- [`import_data_csv_wide()`](https://slinghub.github.io/MRMhub/quant/reference/import_data_csv_wide.md)
  : Import analysis results from plain wide-format CSV files
- [`import_data_csv_long()`](https://slinghub.github.io/MRMhub/quant/reference/import_data_csv_long.md)
  : Import analysis results from long-format CSV files
- [`import_data_mztab()`](https://slinghub.github.io/MRMhub/quant/reference/import_data_mztab.md)
  : Import data from an mzTab-M file
- [`parse_mrmhub_result()`](https://slinghub.github.io/MRMhub/quant/reference/parse_mrmhub_result.md)
  : Parses MRMhub peak integration results into a tibble
- [`parse_masshunter_csv()`](https://slinghub.github.io/MRMhub/quant/reference/parse_masshunter_csv.md)
  : Reads and parses one Agilent MassHunter Quant CSV result file
- [`parse_skyline_result()`](https://slinghub.github.io/MRMhub/quant/reference/parse_skyline_result.md)
  : Parses skyline peak integration results into a tibble
- [`parse_plain_wide_csv()`](https://slinghub.github.io/MRMhub/quant/reference/parse_plain_wide_csv.md)
  : Parses a plain wide CSV file
- [`parse_plain_long_csv()`](https://slinghub.github.io/MRMhub/quant/reference/parse_plain_long_csv.md)
  : Parses a plain long CSV file
- [`import_data_csv()`](https://slinghub.github.io/MRMhub/quant/reference/import_data_csv.md)
  : (Deprecated) Import wide CSV files

## Metadata import

Functions to import metadata describing the analyses (samples), features
(analytes), internal standards and other relevant information from the
MRMhub Excel template or CSV files.

- [`import_metadata_analyses()`](https://slinghub.github.io/MRMhub/quant/reference/import_metadata_analyses.md)
  : Import analysis metadata
- [`import_metadata_features()`](https://slinghub.github.io/MRMhub/quant/reference/import_metadata_features.md)
  : Import feature metadata
- [`import_metadata_istds()`](https://slinghub.github.io/MRMhub/quant/reference/import_metadata_istds.md)
  : Import internal standards (ISTD) metadata
- [`import_metadata_responsecurves()`](https://slinghub.github.io/MRMhub/quant/reference/import_metadata_responsecurves.md)
  : Import response curves metadata
- [`import_metadata_qcconcentrations()`](https://slinghub.github.io/MRMhub/quant/reference/import_metadata_qcconcentrations.md)
  : Import calibration curves metadata
- [`import_metadata_msorganiser()`](https://slinghub.github.io/MRMhub/quant/reference/import_metadata_msorganiser.md)
  : Import metadata from a MRMhub Metadata Organizer file
- [`import_metadata_from_data()`](https://slinghub.github.io/MRMhub/quant/reference/import_metadata_from_data.md)
  : Retrieve metadata from imported analysis data
- [`save_metadata_templates()`](https://slinghub.github.io/MRMhub/quant/reference/save_metadata_templates.md)
  : Saves a Excel (xlsx) file with metadata templates
- [`save_metadata_msorganiser_template()`](https://slinghub.github.io/MRMhub/quant/reference/save_metadata_msorganiser_template.md)
  : Saves a MRMhub Metadata Organizer template
- [`add_metadata()`](https://slinghub.github.io/MRMhub/quant/reference/add_metadata.md)
  : Add metadata to an MRMhubExperiment object

## Isotope correction

Functions to perform type II isotopic correction

- [`correct_interferences()`](https://slinghub.github.io/MRMhub/quant/reference/correct_interferences.md)
  : Apply interference correction
- [`correct_interference_manual()`](https://slinghub.github.io/MRMhub/quant/reference/correct_interference_manual.md)
  : Manual isotopic interference correction

## External Calibration

Function to plot and analyze external calibration curves

- [`quantify_by_calibration()`](https://slinghub.github.io/MRMhub/quant/reference/quantify_by_calibration.md)
  : Calculate concentrations based on external calibration
- [`plot_calibrationcurves()`](https://slinghub.github.io/MRMhub/quant/reference/plot_calibrationcurves.md)
  : Plot calibration curves
- [`calc_calibration_results()`](https://slinghub.github.io/MRMhub/quant/reference/calc_calibration_results.md)
  : Calculate external calibration curve results
- [`get_calibration_metrics()`](https://slinghub.github.io/MRMhub/quant/reference/get_calibration_metrics.md)
  : Get calibration metrics
- [`get_qc_bias_variability()`](https://slinghub.github.io/MRMhub/quant/reference/get_qc_bias_variability.md)
  : Retrieve calibration regression results

## Normalization, Quantification

Functions for normalization by internal standards and sample amounts, to
calculate analyte concentrations based on internal standards amounts or
external calibration curves. Function to for absolute or relative
calibration using a reference sample.

- [`normalize_by_istd()`](https://slinghub.github.io/MRMhub/quant/reference/normalize_by_istd.md)
  : Normalize feature intensities using internal standards
- [`quantify_by_istd()`](https://slinghub.github.io/MRMhub/quant/reference/quantify_by_istd.md)
  : Calculate analyte concentrations using internal standards
- [`calibrate_by_reference()`](https://slinghub.github.io/MRMhub/quant/reference/calibrate_by_reference.md)
  : Calibrate feature values using a reference sample

## Drift/Batch Correction

Function for drift and batch correction correction

- [`correct_drift_gaussiankernel()`](https://slinghub.github.io/MRMhub/quant/reference/correct_drift_gaussiankernel.md)
  : Drift correction by Gaussian kernel smoothing
- [`correct_drift_cubicspline()`](https://slinghub.github.io/MRMhub/quant/reference/correct_drift_cubicspline.md)
  : Drift correction by cubic spline smoothing
- [`correct_drift_loess()`](https://slinghub.github.io/MRMhub/quant/reference/correct_drift_loess.md)
  : Drift correction by LOESS smoothing
- [`correct_drift_gam()`](https://slinghub.github.io/MRMhub/quant/reference/correct_drift_gam.md)
  : Drift correction by generalized additive model (GAM) smoothing
- [`correct_batch_centering()`](https://slinghub.github.io/MRMhub/quant/reference/correct_batch_centering.md)
  : Batch centering correction

## Quality Control and Filtering

Functions to calculate feature QC metrics and apply QC filtering, and
vizualize the filtering results.

- [`calc_qc_metrics()`](https://slinghub.github.io/MRMhub/quant/reference/calc_qc_metrics.md)
  : Calculate quality control (QC) metrics for features
- [`filter_features_qc()`](https://slinghub.github.io/MRMhub/quant/reference/filter_features_qc.md)
  : Feature filtering based on QC criteria
- [`detect_outlier_pca()`](https://slinghub.github.io/MRMhub/quant/reference/detect_outlier_pca.md)
  : Get list of analyses classified as technical outliers
- [`plot_qc_summary_byclass()`](https://slinghub.github.io/MRMhub/quant/reference/plot_qc_summary_byclass.md)
  : Plot QC filtering summary by feature class
- [`plot_qc_summary_overall()`](https://slinghub.github.io/MRMhub/quant/reference/plot_qc_summary_overall.md)
  : Plot overall QC filtering summary
- [`plot_abundanceprofile()`](https://slinghub.github.io/MRMhub/quant/reference/plot_abundanceprofile.md)
  : Plot abundance profile

## Quality Control Plots

Functions to plots diverse QC visualizatios.

- [`plot_runsequence()`](https://slinghub.github.io/MRMhub/quant/reference/plot_runsequence.md)
  : RunSequence plot
- [`plot_runscatter()`](https://slinghub.github.io/MRMhub/quant/reference/plot_runscatter.md)
  : RunScatter plot
- [`plot_rla_boxplot()`](https://slinghub.github.io/MRMhub/quant/reference/plot_rla_boxplot.md)
  : Relative log abundance (RLA) plot
- [`plot_pca()`](https://slinghub.github.io/MRMhub/quant/reference/plot_pca.md)
  : PCA plot for quality control
- [`plot_pca_loading()`](https://slinghub.github.io/MRMhub/quant/reference/plot_pca_loading.md)
  : Plot PCA loadings
- [`plot_feature_correlations()`](https://slinghub.github.io/MRMhub/quant/reference/plot_feature_correlations.md)
  : Plot highly correlated feature pairs
- [`plot_rt_vs_chain()`](https://slinghub.github.io/MRMhub/quant/reference/plot_rt_vs_chain.md)
  : Plot retention time versus chain length and saturation
- [`plot_qc_matrixeffects()`](https://slinghub.github.io/MRMhub/quant/reference/plot_qc_matrixeffects.md)
  : Plot standardized feature intensities grouped by QC type
- [`plot_normalization_qc()`](https://slinghub.github.io/MRMhub/quant/reference/plot_normalization_qc.md)
  : Compare feature variability before and after normalization
- [`plot_qcmetrics_comparison()`](https://slinghub.github.io/MRMhub/quant/reference/plot_qcmetrics_comparison.md)
  : Comparison of two feature QC metrics variables
- [`plot_qc_interferences()`](https://slinghub.github.io/MRMhub/quant/reference/plot_qc_interferences.md)
  : Plot the results of interference correction

## Response Curves

Functions to calculate and visualize response curves

- [`plot_responsecurves()`](https://slinghub.github.io/MRMhub/quant/reference/plot_responsecurves.md)
  : Plot response curves
- [`get_response_curve_stats()`](https://slinghub.github.io/MRMhub/quant/reference/get_response_curve_stats.md)
  : Linear regression statistics of response curves

## Data Reporting and Sharing

Functions to export processed and raw datasets and the processing steps
in different formats.

- [`save_report_xlsx()`](https://slinghub.github.io/MRMhub/quant/reference/save_report_xlsx.md)
  : Write a data-processing report (Excel)
- [`save_dataset_csv()`](https://slinghub.github.io/MRMhub/quant/reference/save_dataset_csv.md)
  : Export data to a CSV file
- [`save_dataset_mztab()`](https://slinghub.github.io/MRMhub/quant/reference/save_dataset_mztab.md)
  : Export an experiment to mzTab-M (HUPO-PSI)
- [`save_dataset_summarizedexperiment()`](https://slinghub.github.io/MRMhub/quant/reference/save_dataset_summarizedexperiment.md)
  : Export an experiment to a Bioconductor SummarizedExperiment
- [`save_feature_qc_metrics()`](https://slinghub.github.io/MRMhub/quant/reference/save_feature_qc_metrics.md)
  : Save feature QC metrics to CSV

## Lipidomics

Functions specific to lipidomics data processing and analysis.

- [`parse_lipid_feature_names()`](https://slinghub.github.io/MRMhub/quant/reference/parse_lipid_feature_names.md)
  : Get lipid class, species and transition names

## Datasets

Example datasets for testing and demonstration.

- [`lipidomics_dataset`](https://slinghub.github.io/MRMhub/quant/reference/lipidomics_dataset.md)
  : Plasma lipidomics dataset with metadata
- [`quant_lcms_dataset`](https://slinghub.github.io/MRMhub/quant/reference/quant_lcms_dataset.md)
  : LC-MS dataset with external calibration curve and metadata
- [`data_load_example()`](https://slinghub.github.io/MRMhub/quant/reference/data_load_example.md)
  : Load an example MRMhubExperiment dataset

## Helper functions

A collection of functions that may be useful in the context of mass
spectrometry is also available.

- [`cv()`](https://slinghub.github.io/MRMhub/quant/reference/cv.md) :
  Percent coefficient of variation (%CV)
- [`cv_log()`](https://slinghub.github.io/MRMhub/quant/reference/cv_log.md)
  : Percent coefficient of variation (%CV) based on log-transformation
- [`calc_average_molweight()`](https://slinghub.github.io/MRMhub/quant/reference/calc_average_molweight.md)
  : Calculate average molecular weight from chemical formulas
- [`correct_drift()`](https://slinghub.github.io/MRMhub/quant/reference/correct_drift.md)
  : Drift correction by custom function
- [`fun_gauss.kernel.smooth()`](https://slinghub.github.io/MRMhub/quant/reference/fun_gauss.kernel.smooth.md)
  : Gaussian kernel smoothing helper function
- [`fun_loess()`](https://slinghub.github.io/MRMhub/quant/reference/fun_loess.md)
  : Loess smoothing helper function
- [`fun_cspline()`](https://slinghub.github.io/MRMhub/quant/reference/fun_cspline.md)
  : Cubic spline smoothing helper function
- [`fun_gam_smooth()`](https://slinghub.github.io/MRMhub/quant/reference/fun_gam_smooth.md)
  : Generalized additive model (GAM) smoothing helper function
- [`get_mad_tails()`](https://slinghub.github.io/MRMhub/quant/reference/get_mad_tails.md)
  : Get MAD-based tails
- [`get_iqr_tails()`](https://slinghub.github.io/MRMhub/quant/reference/get_iqr_tails.md)
  : Get Tukey's IQR fences
- [`get_outlier_bounds()`](https://slinghub.github.io/MRMhub/quant/reference/get_outlier_bounds.md)
  : Get outlier bounds via different methods
- [`order_chained_columns_tbl()`](https://slinghub.github.io/MRMhub/quant/reference/order_chained_columns_tbl.md)
  : Reorder a data frame based on a chain of linked values in two
  columns

# Articles

### All vignettes

- [Installation](https://slinghub.github.io/MRMhub/quant/articles/manual-00-installation.md):

  Install mrmhub, verify your setup, and fix common install errors.

- [Key Concepts &
  Glossary](https://slinghub.github.io/MRMhub/quant/articles/manual-01-key-concepts.md):

  Core vocabulary of MRMhub — the data model, terminology, and the
  function-naming convention.

- [The MRMhubExperiment Data
  Object](https://slinghub.github.io/MRMhub/quant/articles/manual-02-data-object.md):

  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: Why This
  Architecture?](https://slinghub.github.io/MRMhub/quant/articles/manual-03-design-decisions.md):

  Explains the key design decisions behind MRMhub QUANT – for power
  users and contributors who want to understand or extend the package.

- [Importing Analytical
  Data](https://slinghub.github.io/MRMhub/quant/articles/manual-04-data-import.md):

  Importing analytical data from different sources into an
  MRMhubExperiment.

- [Importing
  Metadata](https://slinghub.github.io/MRMhub/quant/articles/manual-05-metadata.md):

  Attaching and validating sample, feature, ISTD and QC metadata on an
  MRMhubExperiment.

- [Sample Types & QC
  Roles](https://slinghub.github.io/MRMhub/quant/articles/manual-06-sample-types.md):

  Reference for the QC-type labels used in MRMhub and their roles in
  quality control and data processing.

- [Drift and Batch
  Correction](https://slinghub.github.io/MRMhub/quant/articles/manual-07-corrections.md):

  MRMhub: postprocessing and quality control of small molecule mass
  spectrometry data

- [Visualisation
  Functions](https://slinghub.github.io/MRMhub/quant/articles/manual-08-visualization.md):

  Reference for the MRMhub plotting functions, grouped by workflow
  stage, with the canonical argument forms and customisation guidance.

- [Writing Pipelines with AI
  Assistants](https://slinghub.github.io/MRMhub/quant/articles/manual-09-ai-assistants.md):

  How to use large language models (Claude, ChatGPT, or local models) to
  help write MRMhub QUANT pipelines – how to ground them in the real
  API, and how to verify what they produce.

- [Troubleshooting and
  FAQ](https://slinghub.github.io/MRMhub/quant/articles/manual-10-troubleshooting.md):

  Solutions to the most common errors and questions when using MRMhub
  QUANT.

- [Manual](https://slinghub.github.io/MRMhub/quant/articles/manual-index.md):

  Complete contents of the MRMhub-QUANT manual.

- [MRMhub](https://slinghub.github.io/MRMhub/quant/articles/mrmhub.md):

- [Quantitative assay with Ext. calibration and
  QC](https://slinghub.github.io/MRMhub/quant/articles/recipe-01-ext-calibration-qc.md):

  MRMhub: Postprocessing and Quality Control of Small-Molecule Mass
  Spectrometry Data

- [Custom QC
  Report](https://slinghub.github.io/MRMhub/quant/articles/recipe-02-custom-qc-report.md):

  Create a detailed HTML QC report from a processed MRMhubExperiment
  using a parameterized Quarto template.

- [Import & Export
  mzTab-M](https://slinghub.github.io/MRMhub/quant/articles/recipe-03-mztab-export.md):

  Exchange data with the HUPO-PSI mzTab-M standard format: export a
  processed MRMhubExperiment for sharing or MetaboLights submission, and
  import results from tools such as Lipid Data Analyzer, MS-DIAL or
  MZmine.

- [Export to Bioconductor
  (SummarizedExperiment)](https://slinghub.github.io/MRMhub/quant/articles/recipe-04-summarizedexperiment.md):

  Export a processed MRMhubExperiment as a Bioconductor
  SummarizedExperiment, and take it downstream: differential abundance
  with limma, or lipid-specific analysis with lipidr.

- [Your First
  Analysis](https://slinghub.github.io/MRMhub/quant/articles/tutorial-00-first-analysis.md):

  A 5-minute walkthrough: import demo data, inspect, normalize, and
  export.

- [Preparing and importing
  data](https://slinghub.github.io/MRMhub/quant/articles/tutorial-01-prep-data.md):

  How to prepare data files and import analytical data and metadata into
  MRMhub.

- [Basic MRMhub
  Workflow](https://slinghub.github.io/MRMhub/quant/articles/tutorial-02-basic-workflow.md):

  A realistic end-to-end workflow covering project setup, metadata
  import, drift/batch correction, QC filtering, and export.

- [Lipidomics Data
  Processing](https://slinghub.github.io/MRMhub/quant/articles/tutorial-03-lipidomics-workflow.md):

  MRMhub: Postprocessing and Quality Control of Small-Molecule Mass
  Spectrometry Data

- [Drift and Batch
  Correction](https://slinghub.github.io/MRMhub/quant/articles/tutorial-04-drift-correction.md):

- [Exploring QC: RunScatter and
  PCA](https://slinghub.github.io/MRMhub/quant/articles/tutorial-05-run-scatter.md):

- [Calibration by a Reference
  Sample](https://slinghub.github.io/MRMhub/quant/articles/tutorial-07-calibration-reference.md):

- [Validating and Fixing
  Metadata](https://slinghub.github.io/MRMhub/quant/articles/tutorial-10-metadata-validation.md):

  How to validate analysis, feature, and ISTD annotations before
  processing, diagnose common defects, and generate a metadata template
  from your data.

- [Interference
  Correction](https://slinghub.github.io/MRMhub/quant/articles/tutorial-11-interference-correction.md):

  Identify and correct isotopic or isobaric interferences between MRM
  transitions using a contribution-based subtraction model.

- [Build a Workflow Without
  Code](https://slinghub.github.io/MRMhub/quant/articles/tutorial-12-workflow-builder.md):

  Use the point-and-click builder to validate your data and metadata and
  generate a runnable Quarto workflow.