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Your First Analysis

Source: vignettes/articles/tutorial-00-first-analysis.Rmd
tutorial-00-first-analysis.Rmd

Tutorial

This walkthrough produces a normalized dataset in under 5 minutes using bundled demo data; no external files are needed.

Time ~5 min  ·  Level Beginner  ·  Prerequisites MRMhub installed

The complete workflow in one script

The complete workflow on the bundled demo data is shown below. The printed summary and plot are produced by this code.

library(mrmhub)

# 1. Import the bundled demo dataset (produced by INTEGRATOR)
demo_file <- system.file("extdata", "MRMhub_demo.tsv", package = "mrmhub")
mexp <- MRMhubExperiment()
mexp <- import_data_mrmhub(mexp, path = demo_file, import_metadata = TRUE)
mexp

# 2. Normalise each feature by its internal standard
mexp <- normalize_by_istd(mexp)

# 3. Inspect the normalised signal across the analytical run
plot_runscatter(mexp, variable = "norm_intensity")

build_workflow() offers an interactive alternative — a point-and-click application that validates your data and metadata, warns about any pipeline mismatches, and generates an equivalent Quarto (.qmd) workflow to download (see Build a Workflow Without Code) — while the code-first workflow above remains the reproducible path. What each line does is described below.

What the script does

The imported object is an MRMhubExperiment, a structured container holding the peak area data, sample annotations, and feature metadata. A few helpers summarise what was imported, and normalize_by_istd() corrects for extraction and injection variability; its result is recorded in mexp@is_istd_normalized, and the original data is always preserved in mexp@dataset_orig.

get_analysis_count(mexp)   # number of analyses
get_feature_count(mexp)    # number of features
get_featurelist(mexp)      # the feature list

Exporting

Export the processed data to an Excel report (multiple sheets) or a tidy CSV:

save_report_xlsx(mexp, path = "my_first_results.xlsx")
save_dataset_csv(mexp, path = "my_first_results.csv")

Next steps