glog_transform: Generalised logarithmic transform

In computational-metabolomics/structToolbox: Data processing & analysis tools for Metabolomics and other omics

Generalised logarithmic transform

Description

The generalised logarithm (glog) transformation applies a log transformation while applying an offset to account for technical variation.

Usage

glog_transform(qc_label = "QC", factor_name, lambda = NULL, ...)

Arguments

qc_label	(character) The label used to identify QC samples. The default is "QC".
factor_name	(character) The name of a sample-meta column to use.
lambda	(numeric, NULL) The value of lambda to use. If NULL then the pmp package will be used to determine an "optimal" value for lambda. The default is NULL.
...	Additional slots and values passed to struct_class.

Details

This object makes use of functionality from the following packages:

• pmp

Value

A glog_transform object with the following output slots:

transformed	(DatasetExperiment) A DatasetExperiment object containing the glog transformed data.
error_flag	(logical) A logical indicating whether the glog optimisation for lambda was successful. If not then PMP returns a default value for lambda.

References

Jankevics A, Lloyd GR, Weber RJM (2022). pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets. R package version 1.10.0.

Durbin B, Hardin J, Hawkins D, Rocke D (2002). “A variance-stabilizing transformation for gene-expression microarray data.” Bioinformatics, 18(Suppl 1), S105-S110.

Parsons HM, Ludwig C, Gunther UL, Viant MR (2007). “Improved classification accuracy in 1- and ', '2-dimensional NMR metabolomics data using the variance ', 'stabilising generalised logarithm transformation.” Bioinformatics, 8(1), 234.

Examples

D = iris_DatasetExperiment()
M = glog_transform(qc_label='versicolor',factor_name='Species')
M = model_apply(M,D)

computational-metabolomics/structToolbox documentation built on Feb. 6, 2023, 2:43 p.m.