rTransformer: Root Based Transformation

In countTransformers: Transform Counts in RNA-Seq Data Analysis

Description

Root based transformation.

Usage

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

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.