normalize_pqn {lipidr} | R Documentation

## Perform Probabilistic Quotient Normalization for intensities.

### Description

Perform Probabilistic Quotient Normalization (PQN) for sample intensities. The PQN method determines a dilution factor for each sample by comparing the distribution of quotients between samples and a reference spectrum, followed by sample normalization using this dilution factor. The reference spectrum in this method is the average lipid abundance of all samples (excluding blanks).

### Usage

    normalize_pqn(data, measure = "Area", exclude = "blank", log = TRUE)

### Arguments

`data` | LipidomicsExperiment object.
`measure` | Which measure to use as intensity, usually Area, Area Normalized or Height. Default is `Area`.
`exclude` | Samples to exclude, can be either: "blank" - automatically detected blank samples and exclude them logical vector with the same length as samples. Default.
`log` | Whether the normalized values should be log2 transformed. Default is `TRUE`.

### Value

A LipidomicsExperiment object with normalized values

### References

Dieterle, F., Ross, A., Schlotterbeck, G., & Senn, H. (2006). Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical chemistry, 78(13), 4281-4290.

### Examples

    datadir <- system.file("extdata", package = "lipidr")
    filelist <- list.files(datadir, "data.csv", full.names = TRUE)
    d <- read_skyline(filelist)
    clinical_file <- system.file("extdata", "clin.csv", package = "lipidr")
    d <- add_sample_annotation(d, clinical_file)
    d_summarized <- summarize_transitions(d, method = "average")

    # Normalize data that have been summarized (single value per molecule).
    data_normalized <- normalize_pqn(
      d_summarized,
      measure = "Area", exclude = "blank", log = TRUE
    )

[Package lipidr version 2.4.0 Index]