pqn_normalisation {pmp} | R Documentation

## Probabilistic quotient normalisation (PQN)

### 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')

[Package pmp version 1.2.1 Index]