Generate Basic LAB QC Plots — plot_basic_lab

This function creates a set of four QC plots for an LAB assay. The plots include:

An overall value distribution histogram with a density overlay and missing value percentage.
A boxplot comparing value distributions across sample types.
A scatter plot with a loess smooth showing the trend of values over injection or sample order.
A bar plot summarizing the percentage of missing data by sample type.

Usage

plot_basic_lab_qc(
  results,
  results_long,
  out_qc_folder = NULL,
  output_prefix,
  printPDF = TRUE,
  verbose = TRUE
)

Arguments

results: A data frame containing assay results. Expected to have one row per analyte, with columns such as analyte_name and other metadata.
results_long: A long-format data frame containing sample-level data. Expected columns include: sample_id, sample_type, value, sample_order, and sample_id_ordered.
out_qc_folder: Character. The folder where QC plot PDFs will be saved. If NULL, the function will create a default folder.
output_prefix: Character. A prefix to be used in the names of output PDF files.
printPDF: Logical. If TRUE, the function outputs the plots to PDF files.
verbose: Logical. If TRUE, the function prints progress messages to the console.

Value

Invisibly returns a gridExtra::grid.arrange object containing the arranged plots. The primary output is the saved PDF file(s) if printPDF = TRUE.

Details

The function uses ggplot2 syntax to generate clean and informative plots, avoiding clutter even with large numbers of samples (e.g., > 1400 samples). It calculates an overall missing value percentage for the measured values, and provides visual summaries of the data distribution and missing data prevalence.