TSS_Norm: Total sum scaling 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 the total number of counts in each sample

1	TSS_Norm(e_data, edata_id)

`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`.

Count data is normalized by the total count, dividing by the total count of each sample

List containing 3 elements: norm_data is a data.frame with same structure as e_data that contains the TSS-normalized data, location_param is NULL, scale_param is a numeric vector of the total count in each sample.

Allison Thompson, Lisa Bramer

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