rv2Transformer: Root and VOOM Based Count Transformation Minimizing Sum of...
In countTransformers: Transform Counts in RNA-Seq Data Analysis

Description Usage Arguments Details Value Author(s) References Examples

View source: R/rv2Transformer.R

Root and VOOM based count transformation minimizing sum of sample-specific squared difference.

1	rv2Transformer(mat, low = 1e-04, upp = 1000, lib.size = NULL)

`mat`	G x n data matrix, where G is the number of genes and n is the number of subjects
`lib.size`	By default, `lib.size` is a vector of column sums of `mat`
`low`	lower bound for the model parameter
`upp`	upper bound for the model parameter

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

y_{gi}=\frac{t_{gi}^{(1/η)}}{(1/η)}

, where

t_{gi}=\frac{≤ft(x_{gi}+0.5\right)}{X_i+1}\times 10^6

and X_i=∑_{g=1}^{G} x_{gi} is the column sum for the i-th column of the matrix mat. The optimal value for the parameter η is to minimize the sum of the squared difference between the sample mean and the sample median across n subjects

∑_{i=1}^{n}≤ft(\bar{y}_i - \tilde{y}_i\right)^2

, \bar{y}_i=∑_{g=1}^{G}y_{gi}/G and \tilde{y}_i is the median of y_{1i}, …, y_{Gi}, and where G is the number of genes and n is the number of subjects.

A list with 3 elements:

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

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

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

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 = rv2Transformer(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")