View source: R/normalisation.R
pqn_normalisation  R Documentation 
For every feature the mean response is calculated across all QC samples. A reference vector is then generated. The median between the reference vector and every sample is computed obtaining a vector of coefficients related to each sample. Each sample is then divided by the median value of the vector of coefficients; this median value is different for each sample. This method was adapted by Dieterle et al. (2006) (see references). Its purpose is to take into account the concentration changes of some metabolite features that affect limited regions of the data.
pqn_normalisation( df, classes, qc_label, ref_mean = NULL, qc_frac = 0, sample_frac = 0, ref_method = "mean" )
df 
A matrixlike (e.g. an ordinary matrix, a data frame) or
RangedSummarizedExperimentclass object with
all values of class 
classes 

qc_label 

ref_mean 

qc_frac 

sample_frac 

ref_method 

Object of class SummarizedExperiment
. If input data are
matrixlike (e.g. an ordinary matrix, a data frame) object, the same R data
structure as the input will be returned with all values of the data type.
numeric()
.
Dieterle F. et al., Anal. Chem., 78(13), 2006. http://dx.doi.org/10.1021/ac051632c
df < MTBLS79[ , MTBLS79$Batch==1] pqn_normalisation(df=df, classes=df$Class, qc_label='QC')
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