Getting started • ampseqQC

ampseqQC turns the outputs of the Mad4hatter pipeline and a sample manifest into an HTML QC report, with pass, repool and reprep calls for every sample.

See an example report.

Install

# install.packages("remotes")
remotes::install_github("EPPIcenter/ampseq-qc")

You also need the Quarto CLI to render the report.

What you need

The results directory from Mad4hatter.
A sample manifest (CSV, TSV or Excel) with one row per well and the columns:

Column	Contents
`sample_name`	Sample name, matching the names given to Mad4hatter
`SampleType`	`sample`, `positive` or `negative`
`Batch`	The plate or batch the sample was processed in
`Column`	Plate column
`Row`	Plate row
`Parasitemia`	qPCR parasitemia

Render the report

library(ampseqQC)

render_qc_report(
  results_dir = "path/to/mad4hatter/results",
  manifest_file = "path/to/manifest.csv",
  output_dir = "qc_output"
)

Or from the command line:

Rscript -e 'ampseqQC::render_qc_report("path/to/results", "path/to/manifest.csv", output_dir = "qc_output")'

To try it on the example run included with the package:

tutorial_dir <- system.file("extdata", "tutorial", package = "ampseqQC")

render_qc_report(
  results_dir = file.path(tutorial_dir, "results"),
  manifest_file = file.path(tutorial_dir, "manifest.csv"),
  output_dir = "qc_output"
)

qc_output will contain QC_report.html and the QC tables, including samples_to_repool.csv, samples_to_reprep.csv and reprep_repool_summary.csv.

Change the settings

Thresholds and other settings are arguments to render_qc_report(). For example, to change the reads needed for a target to count as amplified and the reprep threshold:

render_qc_report(
  results_dir = "path/to/mad4hatter/results",
  manifest_file = "path/to/manifest.csv",
  output_dir = "qc_output",
  read_threshold = 50,
  reprep_threshold = 0.4
)

All of the settings and their defaults are listed on the render_qc_report() reference page, or with ?render_qc_report in R.

The report works out which pools were run from the Mad4hatter panel information. The MAD4HatTeR and PfPHAST pools are mapped to their mPCR reactions by default; for other pools or a different reaction layout, see panel_settings().

Going further

To run the QC steps yourself or build your own summaries, see Building a custom QC with the package functions.