glog_transformation: Variance stabilising generalised logarithm (glog)...

View source: R/glog_transformation.R

glog_transformationR Documentation

Variance stabilising generalised logarithm (glog) transformation


Performs glog transformation on the data set. QC samples can be used to estimate technical variation in the data set and calculate transformation parameter \lambda (lambda). QC samples usually comprise a pool of aliquots taken from all other samples in the study and analysed repeatedly throughout an analytical batch.


glog_transformation(df, classes, qc_label, lambda = NULL)



A matrix-like (e.g. an ordinary matrix, a data frame) or RangedSummarizedExperiment-class object with all values of class numeric() or integer() of peak intensities, areas or other quantitative characteristic.


character(), vector of class labels. Must be the same length as the number of sample in the input peak table. If input is SummarizedExperiment object, use SummarizedExperiment_object$meta_data_column_name.


character(1) or NULL, class label used to identify QC samples.


NULL or numeric(1), if not NULL will use provided value for glog lambda.


Many univariate and multivariate statistical tests require homogeneity and n ormality of dataset variance. Real-world metabolomics datasets often fail to meet these criteria due to asymmetric (i.e. non-'normal') and/or heteroscedatic (i.e. non-homogenous) variance structure. To address this issue, glog data transformations may be applied.
For each cell within the data matrix, transform the raw value (x) according to: log10(x + sqrt(x^2 + \lambda)). The parameter \lambda is typically calculated using quality control (QC) samples analysed throughout an analysis batch.


Object of class SummarizedExperiment. If input data are a matrix-like (e.g. an ordinary matrix, a data frame) object, function returns the same R data structure as input with all value of data type numeric().


Parsons HM et. al., BMC Bionf., 8(234), 2007.


df <- MTBLS79[, MTBLS79$Batch == 1]
out <- mv_imputation(df=df, method="knn")
out <- glog_transformation (df=out, classes=df$Class,

computational-metabolomics/pmp documentation built on March 9, 2024, 4:25 p.m.