normalization: Statistical depth based normalization
In AliciaNieto/fdaRNA: RNA-Seq and Microarray Preprocessing

Description Usage Arguments Value Author(s) References Examples

Gene expression normalization equates the scales at which the various gene expressions have been measured. This function follows the procedure in Nieto-Reyes and Cabrera (2020) that equates to the statistical sample functional depth in Nieto-Reyes (2011).

1	normalization(x)

`x`	Cell files from Affy micrarray experiment: a matrix where the columns are the sample elements, RNA-seq or microarray, and the rows the variables (the genes).

It returns a matrix of the same dimension as the argument, which consists of the normalized data.

A. Nieto and J. Cabrera

Amaratunga D, Cabrera J, Shkedy Z. Exploration and analysis of DNA microarray and other high dimensional data. J. Wiley & Sons, 2014. Nieto-Reyes A. (2011) On the Properties of Functional Depth. In: Ferraty F. (eds) Recent Advances in Functional Data Analysis and Related Topics. Contributions to Statistics. Physica-Verlag HD. Nieto-Reyes A, Cabrera J. Statistical depth based normalization and outlier detection of gene expression data. Preprint.

## Computes the normalization of the RNA-seq's in the Airway dataset.
N = normalization(Airway)

## Displays in a row the boxplots of the "log( +1)" transform of 
## the data before and after normalization.
normalization.boxplot(Airway, before = TRUE, par.r = TRUE)