TMM_Norm: Trimmed Mean of M Values 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 trimmed mean of m values in each sample

1	TMM_Norm(e_data, edata_id, reference = NULL, qm = 0.3, qa = 0.05)

`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`.
`reference`	which column in e_data should be used as the reference, default is to use the sample with the least amount of missing data.
`qm`	percentage by which to trim M values (gene-wise log-fold-changes), default is 0.30 (30%)
`qa`	percentage by which to trim A values (absolute expression levels), default is 0.05 (5%)

Count data is normalized by the trimmed mean of m values.

List containing 3 elements: norm_data is a data.frame with same structure as e_data that contains the TMM-normalized data, location_param is a numeric vector of the TMM values for each sample, and scale_param is NULL.

Allison Thompson, Lisa Bramer

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