med_scounts_norm: Normalization of count data via DESeq scale factors

In pmartR/pmartRseq: Analysis and Visualization of 16S Data

Description

The method normalizes count data by scaling the raw counts by the median scaled counts. The normalized factors used by this procedure are also called DEseq size scores (Anders et al. 2010). The same normalization technique is used in DESeq2 as well (Love et al. 2014).

Usage

med_scounts_norm(e_data, edata_id)

Arguments

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.

Details

Count data is normalized by the median scaled score

Value

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.

Author(s)

Bryan Stanfill

References

Anders, Simon, and Wolfgang Huber. "Differential expression analysis for sequence count data." Genome biol 11.10 (2010): R106. Love, Michael I., Wolfgang Huber, and Simon Anders. "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome biology 15.12 (2014): 1-21.

Examples

library(mintJansson)
e_data_id <- attr(rRNA_data, "cnames")$edata_cname
deseq_n <- med_scounts_norm(e_data = rRNA_data$e_data, e_data_id)

pmartR/pmartRseq documentation built on May 25, 2019, 9:20 a.m.