poisson_norm: Normalization of count data via Poisson Sampling
In pmartR/pmartRseq: Analysis and Visualization of 16S Data

Description Usage Arguments Details Value Author(s) References Examples

The method normalizes count data by resampling the data from a Poisson distribution with parameter estimated from the raw counts. More specifically, the quantity that is returned is given by equation 2.4 of Li and Tibshirani (2013).

1	poisson_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 resampled from the Poisson distribution

List containing 3 elements: norm_data is a data.frame with same structure as e_data that contains the normalized data, location_param is NULL, scale_param is a numeric vector of DESeq scores.

Bryan Stanfill

Li, Jun, and Robert Tibshirani. "Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data." Statistical methods in medical research 22.5 (2013): 519-536.

library(mintJansson)
e_data <- rRNA_data$e_data
e_data_id <- attr(rRNA_data, "cnames")$edata_cname
pois_ndata <- poisson_norm(rRNA_data$e_data, e_data_id)