Package 'countTransformers' reference manual

Title:	Transform Counts in RNA-Seq Data Analysis
Description:	Provide data transformation functions to transform counts in RNA-seq data analysis. Please see the reference: Zhang Z, Yu D, Seo M, Hersh CP, Weiss ST, Qiu W. (2019) <doi.org/10.1038/s41598-019-41315-w>.
Authors:	Zeyu Zhang [aut, cre], Danyang Yu [aut, ctb], Minseok Seo [aut, ctb], Craig P. Hersh [aut, ctb], Scott T. Weiss [aut, ctb], Weiliang Qiu [aut, ctb]
Maintainer:	Zeyu Zhang <[email protected]>
License:	GPL (>= 2)
Version:	0.0.6
Built:	2025-02-26 03:44:40 UTC
Source:	https://github.com/cran/countTransformers

A Simulated Data Set

Description

A simulated data set based on the R code provided by Law et al.'s (2014) paper.

Usage

data("es")data("es")

Format

The format is: Formal class 'ExpressionSet' [package "Biobase"]

Details

The simulated data set contains RNA-seq counts of 1000 genes for 6 samples (3 cases and 3 controls). The library sizes of the 6 samples are not equal.

Source

The dataset was generated based on the R code Simulation_Full.R from the website http://bioinf.wehi.edu.au/voom/.

References

Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology. 2014; 15:R29

Examples

library(Biobase)

data(es)
print(es)

# expression set
ex = exprs(es)
print(dim(ex))
print(ex[1:3,1:2])

# phenotype data
pDat = pData(es)
print(dim(pDat))
print(pDat[1:2,])

# feature data
fDat = fData(es)
print(dim(fDat))
print(fDat[1:2,])

library(Biobase)

data(es)
print(es)

# expression set
ex = exprs(es)
print(dim(ex))
print(ex[1:3,1:2])

# phenotype data
pDat = pData(es)
print(dim(pDat))
print(pDat[1:2,])

# feature data
fDat = fData(es)
print(dim(fDat))
print(fDat[1:2,])

Calculate Jaccard Index for Two Binary Vectors

Description

Calculate Jaccard index for two binary vectors.

Usage

getJaccard(cl1, cl2)
getJaccard(cl1, cl2)

Arguments

`cl1`	n by 1 binary vector of classification 1 for the n subjects
`cl2`	n by 1 binary vector of classification 2 for the n subjects

Details

Jaccard Index is defined as the ratio

$d/(b+c+d$

, where $d$ is the number of subjects who were classified to group 1 by both classification rules, $b$ is the number of subjects who were classified to group 1 by classification rule 1 and were classified to group 0 by classification rule 2, $c$ is the number of subjects who were classified to group 0 by classification rule 1 and were classified to group 1 by classification rule 2.

Value

The Jaccard Index

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

  n = 10
  set.seed(1234567)

  # generate two random binary vector of size n
  cl1 = sample(c(1,0), size = n, prob = c(0.5, 0.5), replace = TRUE)
  cl2 = sample(c(1,0), size = n, prob = c(0.5, 0.5), replace = TRUE)
  cat("\n2x2 contingency table >>\n")
  print(table(cl1, cl2))

  JI = getJaccard(cl1, cl2)
  cat("Jaccard index = ", JI, "\n")


n = 10
  set.seed(1234567)

  # generate two random binary vector of size n
  cl1 = sample(c(1,0), size = n, prob = c(0.5, 0.5), replace = TRUE)
  cl2 = sample(c(1,0), size = n, prob = c(0.5, 0.5), replace = TRUE)
  cat("\n2x2 contingency table >>\n")
  print(table(cl1, cl2))

  JI = getJaccard(cl1, cl2)
  cat("Jaccard index = ", JI, "\n")

Log Based Count Transformation Minimizing Sum of Sample-Specific Squared Difference

Description

Log based count transformation minimizing sum of sample-specific squared difference.

Usage

l2Transformer(mat, low = 1e-04, upp = 1000)
l2Transformer(mat, low = 1e-04, upp = 1000)

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

$y_{gi}=\log_2\left(x_{gi} + \frac{1}{\delta}\right)$

. The optimal value for the parameter $\delta$ is to minimize the sum of the squared difference between the sample mean and the sample median across $n$ subjects

$\sum_{i=1}^{n}\left(\bar{y}_i - \tilde{y}_i\right)^2$

, $\bar{y}_i=\sum_{g=1}^{G}y_{gi}/G$ and $\tilde{y}_i$ is the median of $y_{1i}, \ldots, y_{Gi}$ , and 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

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 = l2Transformer(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")


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 = l2Transformer(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")

Log-based transformation

Description

Log-based transformation.

Usage

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

$y_{gi}=\log_2\left(x_{gi} + \frac{1}{\delta}\right)$

. The optimal value for the parameter $\delta$ is to minimize the squared difference between the sample mean and the sample median of the pooled data $y_{gi}$ , $g=1, \ldots, G$ , $i=1, \ldots, n$ , 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

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")
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")

Log and VOOM Based Count Transformation Minimizing Sum of Sample-Specific Squared Difference

Description

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

Usage

lv2Transformer(mat, lib.size = NULL, low = 0.001, upp = 1000)
lv2Transformer(mat, lib.size = NULL, low = 0.001, upp = 1000)

Arguments

`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

Details

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

$y_{gi}=\log_2\left(t_{gi} + \frac{1}{\delta}\right)$

, where

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

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

$\sum_{i=1}^{n}\left(\bar{y}_i - \tilde{y}_i\right)^2$

, $\bar{y}_i=\sum_{g=1}^{G}y_{gi}/G$ and $\tilde{y}_i$ is the median of $y_{1i}, \ldots, y_{Gi}$ , and 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

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 = lv2Transformer(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")
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 = lv2Transformer(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")

Log and VOOM Transformation

Description

Log and VOOM Transformation.

Usage

lvTransformer(mat, lib.size=NULL, low=0.001, upp=1000)
lvTransformer(mat, lib.size=NULL, low=0.001, upp=1000)

Arguments

`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

Details

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

$y_{gi}=\log_2\left(t_{gi} + \frac{1}{\delta}\right)$

, where

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

and $X_i=\sum_{g=1}^{G} x_{gi}$ is the column sum for the $i$ -th column of the matrix mat. The optimal value for the parameter $\delta$ is to minimize the squared difference between the sample mean and the sample median of the pooled data $y_{gi}$ , $g=1, \ldots, G$ , $i=1, \ldots, n$ , 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

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 = lvTransformer(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")
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 = lvTransformer(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")

Root Based Count Transformation Minimizing Sum of Sample-Specific Squared Difference

Description

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

Usage

r2Transformer(mat, low = 1e-04, upp = 1000)
r2Transformer(mat, low = 1e-04, upp = 1000)

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 and voom transformation

$y_{gi}=\frac{x_{gi}^{(1/\eta)}}{(1/\eta)}$

, The optimal value for the parameter $\eta$ is to minimize the sum of the squared difference between the sample mean and the sample median across $n$ subjects

$\sum_{i=1}^{n}\left(\bar{y}_i - \tilde{y}_i\right)^2$

, $\bar{y}_i=\sum_{g=1}^{G}y_{gi}/G$ and $\tilde{y}_i$ is the median of $y_{1i}, \ldots, y_{Gi}$ , and 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
`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 = r2Transformer(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")
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 = r2Transformer(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")

Root Based Transformation

Description

Root based transformation.

Usage

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

$y_{gi}=\frac{x_{gi}^{(1/\eta)}}{(1/\eta)}$

. The optimal value for the parameter $\eta$ is to minimize the squared difference between the sample mean and the sample median of the pooled data $y_{gi}$ , $g=1, \ldots, G$ , $i=1, \ldots, n$ , 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")
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")

Root and VOOM Based Count Transformation Minimizing Sum of Sample-Specific Squared Difference

Description

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

Usage

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

Arguments

`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

Details

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

$y_{gi}=\frac{t_{gi}^{(1/\eta)}}{(1/\eta)}$

, where

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

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

$\sum_{i=1}^{n}\left(\bar{y}_i - \tilde{y}_i\right)^2$

, $\bar{y}_i=\sum_{g=1}^{G}y_{gi}/G$ and $\tilde{y}_i$ is the median of $y_{1i}, \ldots, y_{Gi}$ , and 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
`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 = 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")
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")

Root and VOOM Transformation

Description

Root and vOOM transformation.

Usage

rvTransformer(mat, lib.size = NULL, low = 0.001, upp = 1000)
rvTransformer(mat, lib.size = NULL, low = 0.001, upp = 1000)

Arguments

`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

Details

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

$y_{gi}=\frac{t_{gi}^{(1/\eta)}}{(1/\eta)}$

, where

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

Value

A list with 3 elements:

`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 = rvTransformer(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")
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 = rvTransformer(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")

Wrapper Function for Wilcoxon Rank Sum Test

Description

Wrapper function for wilcoxon rank sum test.

Usage

wilcoxWrapper(mat, grp)
wilcoxWrapper(mat, grp)

Arguments

`mat`	G x n data matrix, where G is the number of genes and n is the number of subjects
`grp`	n x 1 vector of subject group info

Details

For each row of mat, we perform Wilcoxon rank sum test.

Value

A G x 1 vector of p-values.

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

Package 'countTransformers'

Help Index

A Simulated Data Set

Description

Usage

Format

Details

Source

References

Examples

Calculate Jaccard Index for Two Binary Vectors

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Log Based Count Transformation Minimizing Sum of Sample-Specific Squared Difference

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Log-based transformation

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Log and VOOM Based Count Transformation Minimizing Sum of Sample-Specific Squared Difference

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Log and VOOM Transformation

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Root Based Count Transformation Minimizing Sum of Sample-Specific Squared Difference

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Root Based Transformation

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Root and VOOM Based Count Transformation Minimizing Sum of Sample-Specific Squared Difference

Description

Usage

Arguments

Details

Value

Author(s)