View source: R/glog_transformation.R
glog_transformation  R Documentation 
Performs glog transformation on the data set. QC samples can be used to estimate technical variation in the data set and calculate transformation parameter λ (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)
df 
A matrixlike (e.g. an ordinary matrix, a data frame) or
RangedSummarizedExperimentclass object with
all values of class 
classes 

qc_label 

lambda 

Many univariate and multivariate statistical tests require homogeneity and
n ormality of dataset variance. Realworld metabolomics datasets often fail
to meet these criteria due to asymmetric (i.e. non'normal') and/or
heteroscedatic (i.e. nonhomogenous) 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 + λ)). The parameter λ is
typically calculated using quality control (QC) samples analysed throughout
an analysis batch.
Object of class SummarizedExperiment
. If input data are a
matrixlike (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. https://doi.org/10.1186/147121058234
df < MTBLS79[, MTBLS79$Batch == 1] out < mv_imputation(df=df, method="knn") out < glog_transformation (df=out, classes=df$Class, qc_label="QC")
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