lTransformer: Log-based transformation In countTransformers: Transform Counts in RNA-Seq Data Analysis

Description

Log-based transformation.

Usage

 1 lTransformer(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 log transformation

y_{gi}=\log_2≤ft(x_{gi} + \frac{1}{δ}\right)

. The optimal value for the parameter δ 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

A list with 3 elements:

 res.delta  An object returned by optimize function delta  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 = lTransformer(mat = ex) # estimated model parameter print(res$delta) # mean-median after transformation vec2 = c(res$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.