PQNNorm: Probabilistic quotient normalization.
In Waddlessss/MAFFIN: Integrated Sample Normalization by MAFFIN Algorithm

View source: R/PQNNorm.R

PQNNorm

R Documentation

Probabilistic quotient normalization.

Description

Sample normalization using median fold change.

Usage

PQNNorm(
  FeatureTable,
  IntThreshold = 0,
  SampleInCol = TRUE,
  output = FALSE,
  OutputNormFactors = FALSE,
  RunEvaluation = TRUE
)

Arguments

`FeatureTable`	Data frame with features in row and samples in column (default).
`IntThreshold`	A numeric value indicating the feature intensity threshold. Feature is detected when its intensity larger than this value.
`SampleInCol`	`TRUE` if samples are in column. `FALSE` if samples are in row.
`output`	`TRUE` will output the result table in current working directory
`OutputNormFactors`	`TRUE` will print the normalization factors after normalization
`RunEvaluation`	`TRUE` will evaluate the normalization results using intragroup variation.

Details

FeatureTable contains measured or corrected signal intensities of metabolic features, with features in row and samples in column (default). The column names should be sample names, and the first row should be sample group names (e.g. control, case).
The first column should be unique feature identifiers. For group names, please do not use "blank", "RT", "QC", or "SQC_###" for real biological samples.
An example of FeatureTable is provided as TestingData in this package.

Value

This function will return a list that contains four items if RunEvaluation = TRUE: the normalized feature table, normalization factors, PRMAD of original data, and PRMAD of normalized data. The last two items will not be generated if RunEvaluation = FALSE

References

Yu, Huaxu, and Tao Huan. "MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities." bioRxiv (2021).
Dieterle, Frank, et al. "Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics." Analytical chemistry 78.13 (2006): 4281-4290.