# rTransformer: Root Based Transformation In countTransformers: Transform Counts in RNA-Seq Data Analysis

## Description

Root based transformation.

## Usage

 1 rTransformer(mat, low = 1e-04, upp = 100) 

## Arguments

 mat G x n data matrix, where G is the number of genes and n is the number of subjects low lower bound for the model parameter upp upper bound for the model parameter

## Details

Denote x_{gi} as the expression level of the g-th gene for the i-th subject. We perform the root transformation

y_{gi}=\frac{x_{gi}^{(1/η)}}{(1/η)}

. The optimal value for the parameter η is to minimize the squared difference between the sample mean and the sample median of the pooled data y_{gi}, g=1, …, G, i=1, …, n, where G is the number of genes and n is the number of subjects.

## Value

 res.eta  An object returned by optimize function eta  model parameter mat2  transformed data matrix having the same dimension as mat

## Author(s)

Zeyu Zhang, Danyang Yu, Minseok Seo, Craig P. Hersh, Scott T. Weiss, Weiliang Qiu

## References

Zhang Z, Yu D, Seo M, Hersh CP, Weiss ST, Qiu W. Novel Data Transformations for RNA-seq Differential Expression Analysis. (2019) 9:4820 https://rdcu.be/brDe5

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 library(Biobase) data(es) print(es) # expression set ex = exprs(es) print(dim(ex)) print(ex[1:3,1:2]) # mean-median before transformation vec = c(ex) m = mean(vec) md = median(vec) diff = m - md cat("m=", m, ", md=", md, ", diff=", diff, "\n") res = rTransformer(mat = ex) # estimated model parameter print(res$eta) # mean-median after transformation vec2 = c(res$mat2) m2 = mean(vec2) md2 = median(vec2) diff2 = m2 - md2 cat("m2=", m2, ", md2=", md2, ", diff2=", diff2, "\n") 

countTransformers documentation built on May 1, 2019, 7:59 p.m.