pqn_normalisation: Probabilistic quotient normalisation (PQN)

In pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets

Description

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.

Usage

pqn_normalisation(df, classes, qc_label, ref_mean = NULL)

Arguments

df	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.
classes	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.
qc_label	character(1) or NULL, class label used to identify QC samples.
ref_mean	numeric() or NULL, Vector of reference mean values to use instead of calculating from QC sample group. If set to NULL, QC sample data will be used.

Value

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().

References

Dieterle F. et al., Anal. Chem., 78(13), 2006. http://dx.doi.org/10.1021/ac051632c

Examples

df <- MTBLS79[ , MTBLS79$Batch==1]
pqn_normalisation(df=df,
    classes=df$Class, qc_label='QC')

pmp documentation built on April 1, 2021, 6:01 p.m.