pqn_norm {structToolbox} | R Documentation |
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.
pqn_norm( qc_label = "QC", factor_name, qc_frac = 0, sample_frac = 0, ref_method = "mean", ref_mean = NULL, ... )
qc_label |
(character) The label used to identify QC samples. The default is |
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 |
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 |
ref_method |
(character) Reference computation method. Allowed values are limited to the following:
The default is |
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 |
... |
Additional slots and values passed to |
This object makes use of functionality from the following packages:
pmp
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. |
Jankevics A, Lloyd GR, Weber RJM (2021). pmp: Peak Matrix Processing and signal batch correction for metabolomics datasets. R package version 1.5.0.
D = iris_DatasetExperiment() M = pqn_norm(factor_name='Species',qc_label='all') M = model_apply(M,D)