The generalised logarithm (glog) transformation applies a log transformation while applying an offset to account for technical variation.
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.
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. |
Inheritance
A glog_transform object inherits the following struct classes: [glog_transform] >> [model] >> [struct_class]
References
Jankevics A, Lloyd GR, Weber RJM (????). pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets. R package version 1.15.1.
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
M = glog_transform(
qc_label = "QC",
factor_name = "V1",
lambda = NULL)
D = iris_DatasetExperiment()
M = glog_transform(qc_label='versicolor',factor_name='Species')
M = model_apply(M,D)
#> Error!Lambda tending to infinity! Using standard
#> Error!Lambda tending to infinity! Using standard