Log_Norm: Log transformation 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 log2 transforming all of the counts

1	Log_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 a log2 transformation

List containing 3 elements: norm_data is a data.frame with same structure as e_data that contains the Log2-normalized data, location_param is NULL, scale_param is a numeric vector of 1's, for later use.

Allison Thompson

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library(mintJansson)
data(cDNA_hiseq_data)
cDNA_log <- Log_Norm(e_data = cDNA_hiseq_data$e_data, edata_id = attr(cDNA_hiseq_data, "cnames")$edata_cname)