glog_transform | R Documentation |
The generalised logarithm (glog) transformation applies a log transformation while applying an offset to account for technical variation.
glog_transform(qc_label = "QC", factor_name, lambda = NULL, ...)
qc_label |
(character) The label used to identify QC samples. The default is |
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 |
... |
Additional slots and values passed to |
This object makes use of functionality from the following packages:
pmp
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. |
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.
D = iris_DatasetExperiment() M = glog_transform(qc_label='versicolor',factor_name='Species') M = model_apply(M,D)
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