pqn_norm {structToolbox} | R Documentation

## Probabilistic Quotient Normalisation (PQN)

### Description

PQN is used to normalise for differences in concentration between samples. It makes use of Quality Control (QC) samples as a reference. PQN scales by the median change relative to the reference in order to be more robust against changes caused by response to perturbation.

### Usage

    pqn_norm(
      qc_label = "QC",
      factor_name,
      qc_frac = 0,
      sample_frac = 0,
      ref_method = "mean",
      ref_mean = NULL,
      ...
    )

### Arguments

`qc_label` | (character) The label used to identify QC samples. The default is `"QC"`.
`factor_name` | (character) The name of a sample-meta column to use.
`qc_frac` | (numeric) A value between 0 and 1 to indicate the minimum proportion of QC samples a feature must be present in for it to be included when computing the reference. Default qc_frac = 0. . The default is `0`.
`sample_frac` | (numeric) A value between 0 and 1 to indicate the minimum proportion of samples a feature must be present in for it to be considered when computing the normalisation coefficients. . The default is `0`.
`ref_method` | (character) Reference computation method. Allowed values are limited to the following: `"mean"`: The reference is computed as the mean of the samples matching the qc_label input. `"median"`: The reference is computed as the median of the samples matching the qc_label_input. The default is `"mean"`.
`ref_mean` | (numeric, NULL) A single sample to use as the reference for normalisation. If set to NULL then the reference will be computed based on the other input parameters (ref_mean, qc_label etc). . The default is `NULL`.
`...` | Additional slots and values passed to `struct_class`.

### Details

This object makes use of functionality from the following packages:

- `pmp`

### Value

A `pqn_norm` object with the following `output` slots:

`normalised` | (DatasetExperiment) A DatasetExperiment object containing the normalised data.
`coeff` | (data.frame) The normalisation coefficients calculated by PQN.

### References

Jankevics A, Lloyd GR, Weber RJM (2021). pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets. R package version 1.5.0.

### Examples

    D = iris_DatasetExperiment()
    M = pqn_norm(factor_name='Species',qc_label='all')
    M = model_apply(M,D)

[Package structToolbox version 1.6.1 Index]