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Probabilistic Quotient Normalisation (PQN)

Source: R/pqn_norm_method_class.R
pqn_norm.Rd

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.

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.

Details

This object makes use of functionality from the following packages:

  • pmp

Inheritance

A pqn_norm object inherits the following struct classes:

[pqn_norm] >> [model] >> [struct_class]

References

Jankevics A, Lloyd GR, Weber RJM (????). pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets. R package version 1.15.1.

Examples

M = pqn_norm(
      qc_label = "QC",
      factor_name = "V1",
      qc_frac = 0,
      sample_frac = 0,
      ref_mean = NULL,
      ref_method = "mean")

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