Quant_Norm: Quantile normalization of count data
In pmartR/pmartRseq: Analysis and Visualization of 16S Data

Description Usage Arguments Details Value Author(s) Examples

The method normalizes count data by a specified quantile (e.g., 75th quantile)

1	Quant_Norm(e_data, edata_id, q = 0.75)

`e_data`	a p \times n data.frame of count data, where p is the number of features and n is the number of samples. Each row corresponds to data for a feature, with the first column giving the feature name.
`edata_id`	character string indicating the name of the feature identifier. Usually obtained by calling `attr(omicsData, "cnames")$edata_cname`.
`q`	a number to indicate which quantile to normalize with. Default is 0.75.

Count data is normalized by a given quantile, dividing by the given quantile of the sample and multiplying by the given quantile of the entire dataset.

List containing 3 elements: norm_data is a data.frame with same structure as e_data that contains the quantile-normalized data, location_param is NULL, and scale_param is a numeric vector containing, for every sample, the value of the sample at the designated quantile (q) divided by the value of the global quantile (q).

Allison Thompson, Lisa Bramer

library(mintJansson)
data(cDNA_hiseq_data)
cDNA_quant <- Quant_Norm(e_data = cDNA_hiseq_data$e_data, edata_id = attr(cDNA_hiseq_data, "cnames")$edata_cname)
norm_factors <- attr(cDNA_quant,"data_info")$scale_param