Perform quantile normalization
normalize_quantile(omicsData)an object of the class 'pepData', 'proData', 'metabData',
'lipidData', 'nmrData', created by as.pepData,
as.proData, as.metabData,
as.lipidData, or as.nmrData, respectively.
The normalized data is returned in an object of the appropriate S3 class (e.g. pepData), on the same scale as omicsData (e.g. if omicsData contains log2 transformed data, the normalization will be performed on the non-log2 scale and then re-scaled after normalization to be returned on the log2 scale).
Quantile normalization is an algorithm for normalizing a set of data vectors by giving them the same distribution. It is applied to data on the abundance scale (e.g. not a log scale). It is often used for microarray data.
The method is implemented as described in Bolstad et al. (2003).
Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19(2), 185-193.
library(pmartRdata)
myfilt <- molecule_filter(omicsData = metab_object)
# quantile normalization requires complete data
# summary(myfilt, min_num = 50)
mymetab <- applyFilt(filter_object = myfilt, omicsData = metab_object, min_num = 50)
norm_data <- normalize_quantile(omicsData = mymetab)