glog_transform {structToolbox} | R Documentation

## 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 (2021). pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets. R package version 1.5.0.

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)

[Package structToolbox version 1.6.1 Index]