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).

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)
#> Successfully read 3 methods. #> Your data contain 58 samples, 10 lipid classes, 277 lipid molecules.
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 )
#> Warning: Area contains missing/non-finite values. Replacing with mimnum detected value.