Version checked for this guide: 1.3.02 · 2026-09-23
MetNormalizer is the standalone QC-based normalization package linked from the TidyMass package catalogue. Its file-based metNor() interface is different from masscleaner::normalize_data() and should not be substituted into a mass_dataset pipeline without preparing its input format.
Prepare the MS1 table and sample-information CSV according to the package example, including the required QC and acquisition metadata. Work in a dedicated directory, review filtering fractions and optimization settings, then inspect the saved normalized data and diagnostics. Preserve the input and method settings.
multiple = 1 uses injection-order-based SVR in the documented interface; larger values use correlated peaks. Choose according to acquisition and study design, not simply because a larger value looks more powerful. For a new object-based TidyMass workflow, masscleaner usually provides the direct integration point.
Functional scope
Input checks and a standalone QC/SVR normalization workflow.
Example
# Requires MetNormalizer-format CSV files in normalization_input/.
# MetNormalizer::metNor(
# ms1.data.name = "data.csv",
# sample.info.name = "sample.info.csv",
# path = "normalization_input", optimization = TRUE,
# multiple = 1, threads = 2
# )
help("metNor", package = "MetNormalizer")
Find a function
The checked source exports the following APIs, including compatibility interfaces and utilities. Consult the installed version with help("function_name", package = "MetNormalizer") for argument details.
Show exported functions
MetNormalizer_logo, checkData, metNor
Package source and reference documentation · Complete workflow