Package 'corTest' reference manual

Title:	Robust Tests for Equal Correlation
Description:	There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The score statistic U is proportion to difference of two correlations based on different types of correlation in 6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4(). We highly recommend ST5() according to the article titled ''New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs'' published in Scientific Reports. Please see the reference: Yu et al. (2019) <doi:10.1038/s41598-019-40167-8>.
Authors:	Danyang Yu, Weiliang Qiu, Zeyu Zhang, Kimberly Glass, Jessica Su, Dawn L. DeMeo, Kelan Tantisira, Scott T. Weiss
Maintainer:	Danyang Yu <[email protected]>
License:	GPL (>= 2)
Version:	1.0.7
Built:	2024-12-21 06:39:54 UTC
Source:	CRAN

Construct Differential Correlation Network

Description

Construct differential correlation network with expressionSet,st5 is recommand for testing equal correlation.

Usage

construct_network(es,
                  cor_method = "st5",
                  var.grp,
                  pseudo_adjust_cutoff = FALSE,
                  pAdjMethod = 'fdr',
                  cutoff = 0.05,
                  nPseudo = 25)
construct_network(es,
                  cor_method = "st5",
                  var.grp,
                  pseudo_adjust_cutoff = FALSE,
                  pAdjMethod = 'fdr',
                  cutoff = 0.05,
                  nPseudo = 25)

Arguments

`es`	an ExpressionSet object of microRNA dataset
`cor_method`	a string represents the method for equal correlation, 'st5' is recommand.
`var.grp`	character. phenotype variable name indicating case-control status,0 as control, 1 as case.
`pseudo_adjust_cutoff`	if the value is TRUE, pseudo probes will be used for setting the cutoff of p-value for differential corrlation test. Otherwise, adjusted p-value will be compared with `cutoff`.
`pAdjMethod`	if `pAdjMethod='none'`, the function will not do mutiple testing adjustment. If pAdjMethod="fdr"/"BH"/"BY"/"holm"/"hochberg"/"hommel"/"bonferroni"/"BH"/"BY", the specific method will be used for adjusting p-value. `pAdjMethod` will only be used when `pseudo_adjust_cutoff=FALSE`
`cutoff`	if p value is smaller than the cutoff, there will be an edge between the two nodes. `cutoff` will only be used when `pseudo_adjust_cutoff=FALSE`.
`nPseudo`	if `pseudo_adjust_cutoff=TRUE`, then `nPseudo` genes will be randomly chosen to produce pseudo genes (i.e., non-differentially correlated with other genes between cases and controls). We will use the minimum of the p-values of these pseudo genes as the cutoff of p-values for differential correlation analysis.

Value

A list with 6 elements:

`my_graph`	obtained network as igraph object
`my_dat`	obtained netork as data frame with 3 columns: edge id, node_id1,node_id2
`pvalMat`	raw p-values for testing differential correlation for each pair of genes
`pAdjMat`	adjusted p-values for testing differential correlation for each pair of genes
`pvalPseudo`	p-values for testing differential correlation between pseudo genes and other genes
`alpha1`	cutoff for p-values for testing differential correlation

Author(s)

Danyang Yu <[email protected]>, Weiliang Qiu <[email protected]>

References

Danyang Yu, Zeyu Zhang, Kimberly Glass, Jessica Su, Dawn L. DeMeo, Kelan Tantisira, Scott T. Weiss, Weiliang Qiu(corresponding author). New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs. Scientific Reports 9, Article number: 3499 (2019)

Examples

set.seed(1234567)
res = generate_data(n1 = 50, n2 = 60, p1 = 5, p2 = 50)
es = res$es
print(es)

covCtrl = res$covCtrl
covCase = res$covCase

# we expect cov for 1st 5 genes are different between cases and controls
print(round(covCtrl[1:5, 1:5], 2))
print(round(covCase[1:5, 1:5], 2))

# we expect cov for other genes are same between cases and controls
print(round(covCtrl[6:10, 6:10], 2))
print(round(covCase[6:10, 6:10], 2))

res2 = construct_network(es = es,
                  cor_method = "st5",
                  pseudo_adjust_cutoff = FALSE,
                  var.grp = "grp",
                  pAdjMethod = 'fdr',
                  cutoff = 0.05,
                  nPseudo = 25)

print(res2$graph)
print(res2$network_dat)

set.seed(1234567)
res = generate_data(n1 = 50, n2 = 60, p1 = 5, p2 = 50)
es = res$es
print(es)

covCtrl = res$covCtrl
covCase = res$covCase

# we expect cov for 1st 5 genes are different between cases and controls
print(round(covCtrl[1:5, 1:5], 2))
print(round(covCase[1:5, 1:5], 2))

# we expect cov for other genes are same between cases and controls
print(round(covCtrl[6:10, 6:10], 2))
print(round(covCase[6:10, 6:10], 2))

res2 = construct_network(es = es,
                  cor_method = "st5",
                  pseudo_adjust_cutoff = FALSE,
                  var.grp = "grp",
                  pAdjMethod = 'fdr',
                  cutoff = 0.05,
                  nPseudo = 25)

print(res2$graph)
print(res2$network_dat)

A BioConductor ExpressionSet Object Storing Gene Expression Data

Description

A BioConductor ExpressionSet object storing gene expression data.

Usage

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

Format

The expression data are stored in a matrix, which contains 55 row genes and 110 column subjects.

The phenotye data contains two variables: sid (subject ID) and grp (case-control status). grp=1 indicates the subject is a case. grp=0 indicates the subject is a control.

The feature data contains two variables: geneid (gene ID) and memGenes. memGenes=1 indicates the gene is differentially correlated between cases and controls with at least one other gene. memGenes=0 indicates the gene is not diffferentially correlated with other genes.

Details

The data contains 50 cases and 60 controls. The first 5 genes are differentially correlated with at least one other genes, while the remaining 50 genes are non-differentially correlated with other genes.

The covariance matrix of the first 5 genes in cases were generated by the function rcorrmatrix in R package clusterGeneration. The covariance matrix of the first 5 genes in controls are identical matrix.

The covariance matrix of the remaining 50 genes in cases were generated by the function rcorrmatrix. The covariance matrix of the 50 genes in controls were the same as that in cases.

The first 50 subjects are cases. The remaining 60 subjects are controls.

References

Examples

data(es)

print(es)

data(es)

print(es)

Test for equal correlation

Description

Compute p-value with Fisher’s Z-transformation test. If biasCorrection is true, the corrected correlation is used. The formula is rho.corrected = rho - rho/(2*(n-1)).

Usage

fisher_transfer_test(x1, z1, x0, z0, biasCorrection = TRUE)
fisher_transfer_test(x1, z1, x0, z0, biasCorrection = TRUE)

Arguments

`x1`	a numeric vector
`z1`	a numeric vector with same length as `x1`
`x0`	a numeric vector
`z0`	a numeric vector with same length as `x0`
`biasCorrection`	a boolean value

Value

p-value of test for testing if correlation between x1 and z1 is the same as that between x0 and z0

Author(s)

Danyang Yu <[email protected]>, Weiliang Qiu <[email protected]>

References

Examples

x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
p = fisher_transfer_test(x1, z1, x0, z0)
print(p)
x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
p = fisher_transfer_test(x1, z1, x0, z0)
print(p)

Generate Data for Control Subjects and Case Subjects

Description

The function is to generate expression level matrixes of control subjects and case subjects. X matrix is for control subjects with the default sample size n1=50. Z matrix is for case subjects with the default sample size n2=60. X is generated from multivariate normal distribution N (0, SigmaX), where SigmaX is a block matrix ((SigmaP1, 0), (0, SigmaP2)), sigmaP1 is the p1*p1 matrix and SigmaP2 is the p2*p2 matrix. Z is generated from multivariate normal distribution N (0, SigmaZ), where SigmaZ is a block matrix ((E_P1, 0), (0, SigmaP2)) and E_P1 is p1*p1 identity matrix.

Usage

generate_data(n1, n2, p1, p2)
generate_data(n1, n2, p1, p2)

Arguments

`n1`	a numeric value representing the sample size of control subjects
`n2`	a numeric value representing the sample size of case subjects
`p1`	a numeric value representing the dimension of sigma_1
`p2`	a numeric value representing the dimension of sigma_0

Value

A list with 3 elements:

`es`	An ExpressionSet object contains gene expression data.
`covCase`	Covariance matrix of all genes for case subjects.
`covCtrl`	Covariance matrix of all genes for control subjects.

Author(s)

Danyang Yu <[email protected]>, Weiliang Qiu <[email protected]>

References

Examples

library(Biobase)
set.seed(1234567)
res = generate_data(n1 = 50, n2 = 60, p1 = 5, p2 = 50)
es = res$es
print(es)


# gene expression data
dat = exprs(es)
print(dim(dat))
print(dat[1:2,1:3])

# phenotype data
pDat = pData(es)
print(dim(pDat))
print(pDat[1:2,])
print(table(pDat$grp, useNA = "ifany"))

# feature data
fDat = fData(es)
print(dim(fDat))
print(fDat[1:2,])
print(table(fDat$memGenes, useNA = "ifany"))

covCtrl = res$covCtrl
covCase = res$covCase

# we expect cov for 1st 5 genes are different between cases and controls
print(round(covCtrl[1:5, 1:5], 2))
print(round(covCase[1:5, 1:5], 2))

# we expect cov for other genes are same between cases and controls
print(round(covCtrl[6:10, 6:10], 2))
print(round(covCase[6:10, 6:10], 2))

library(Biobase)
set.seed(1234567)
res = generate_data(n1 = 50, n2 = 60, p1 = 5, p2 = 50)
es = res$es
print(es)


# gene expression data
dat = exprs(es)
print(dim(dat))
print(dat[1:2,1:3])

# phenotype data
pDat = pData(es)
print(dim(pDat))
print(pDat[1:2,])
print(table(pDat$grp, useNA = "ifany"))

# feature data
fDat = fData(es)
print(dim(fDat))
print(fDat[1:2,])
print(table(fDat$memGenes, useNA = "ifany"))

covCtrl = res$covCtrl
covCase = res$covCase

# we expect cov for 1st 5 genes are different between cases and controls
print(round(covCtrl[1:5, 1:5], 2))
print(round(covCase[1:5, 1:5], 2))

# we expect cov for other genes are same between cases and controls
print(round(covCtrl[6:10, 6:10], 2))
print(round(covCase[6:10, 6:10], 2))

Generate an ExpressionSet Object

Description

Generate an ExpressionSet object.

Usage

genEset(ex, pDat, fDat = NULL, annotation = "")
genEset(ex, pDat, fDat = NULL, annotation = "")

Arguments

`ex`	a data matrix stores gene expression data. Rows are genes and columns are subjects.
`pDat`	a data frame stores phenotype data. Rows are subjects and columns are variables describing subjects. Row names of `pDat` must be idential to the column names of `ex`.
`fDat`	a data frame stores feature data. Rows are genes and columns are variables describing genes. Row names of `fDat` must be idential to the row names of `ex`.
`annotation`	character. indicates R Bioconductor annotation package that provides annotation for genes.

Value

A BioConductor ExpressionSet object.

Author(s)

Danyang Yu <[email protected]>, Weiliang Qiu <[email protected]>

generating varibles from g-and-h distribution

Description

generating varibles from g-and-h distribution

Usage

ghdist(n, g = 0,h = 0)
ghdist(n, g = 0,h = 0)

Arguments

`n`	the number of the varibles you want to generate
`g`	the parameter g of g-and-h distribution
`h`	the parameter h of g-and-h distribution

Value

n varibles generated from g-and-h distribution

Examples

x = ghdist(50, 0.2, 0.2)
print(x)
x = ghdist(50, 0.2, 0.2)
print(x)

Scatter Plot of 2 Genes for Cases and Controls

Description

Scatter plot of 2 genes for cases and controls, superimposed with linear regression lines.

Usage

plotDiffCor(x1, 
	    z1, 
	    x0, 
	    z0, 
	    pval = NULL, 
	    xlab = "gene1", 
	    ylab = "gene2", 
	    title = "scatter plots") 
plotDiffCor(x1, 
	    z1, 
	    x0, 
	    z0, 
	    pval = NULL, 
	    xlab = "gene1", 
	    ylab = "gene2", 
	    title = "scatter plots")

Arguments

`x1`	numeric. vector of gene expression for gene 1 for cases.
`z1`	numeric. vector of gene expression for gene 2 for cases.
`x0`	numeric. vector of gene expression for gene 1 for controls.
`z0`	numeric. vector of gene expression for gene 2 for controls.
`pval`	numeric. p-value for testing differential correlation of the 2 genes between cases and controls.
`xlab`	character. label for x-axis.
`ylab`	character. label for y-axis.
`title`	character. plot title.

Value

A list with 4 elments:

`g`	A ggplot2 object.
`dat`	a data frame with 3 variables: x, z, and grp.
`coef1`	a vector of length two giving the intercept and slope of linear regression for cases.
`coef0`	a vector of length two giving the intercept and slope of linear regression for controls.

Author(s)

Danyang Yu <[email protected]>, Weiliang Qiu <[email protected]>

References

Examples

library(Biobase)
set.seed(1234567)
res = generate_data(n1 = 50, n2 = 60, p1 = 5, p2 = 50)
es = res$es
print(es)


# gene expression data
dat = exprs(es)
print(dim(dat))
print(dat[1:2,1:3])

# 3rd gene
x = dat[3,]
# 5th gene
z = dat[5,]

# for cases
x1 = x[which(es$grp == 1)]
z1 = z[which(es$grp == 1)]

# for controls
x0 = x[which(es$grp == 0)]
z0 = z[which(es$grp == 0)]

# st5
res2 = st5(x1 = x1, z1 = z1, x0 = x0, z0 = z0)
pval = res2$pval

plotDiffCor(x1 = x1, 
	    z1 = z1, 
	    x0 = x0, 
	    z0 = z0, 
	    pval = pval, 
	    xlab = "gene3", 
	    ylab = "gene5", 
	    title = "scatter plots" 
	    )


library(Biobase)
set.seed(1234567)
res = generate_data(n1 = 50, n2 = 60, p1 = 5, p2 = 50)
es = res$es
print(es)


# gene expression data
dat = exprs(es)
print(dim(dat))
print(dat[1:2,1:3])

# 3rd gene
x = dat[3,]
# 5th gene
z = dat[5,]

# for cases
x1 = x[which(es$grp == 1)]
z1 = z[which(es$grp == 1)]

# for controls
x0 = x[which(es$grp == 0)]
z0 = z[which(es$grp == 0)]

# st5
res2 = st5(x1 = x1, z1 = z1, x0 = x0, z0 = z0)
pval = res2$pval

plotDiffCor(x1 = x1, 
	    z1 = z1, 
	    x0 = x0, 
	    z0 = z0, 
	    pval = pval, 
	    xlab = "gene3", 
	    ylab = "gene5", 
	    title = "scatter plots" 
	    )

Test Differential Correlation Using st1 Method

Description

Compute p-value for the equal correlation test with Pearson correlation based on a logistic regression model corresponding to two independent groups

Usage

st1(x1, z1, x0, z0)
st1(x1, z1, x0, z0)

Arguments

`x1`	a numeric vector
`z1`	a numeric vector with same length as `x1`
`x0`	a numeric vector
`z0`	a numeric vector with same length as `x0`

Value

`stat`	Test statistic for testing if correlation between `x1` and `z1` is the same as that between `x0` and `z0`
`p-value`	p-value of test
`signedStat`	Signed test statistic

Author(s)

Danyang Yu <[email protected]>, Weiliang Qiu <[email protected]>

References

Examples

x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
p = st1(x1, z1, x0, z0)
print(p)
x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
p = st1(x1, z1, x0, z0)
print(p)

Test Differential Correlation Using st2 Method

Description

Compute p-value for the equal correlation test with mad-replacing-Pearson correlation based on a logistic regression model corresponding to two independent groups

Usage

st2(x1, z1, x0, z0)
st2(x1, z1, x0, z0)

Arguments

`x1`	a numeric vector
`z1`	a numeric vector with same length as `x1`
`x0`	a numeric vector
`z0`	a numeric vector with same length as `x0`

Value

`stat`	Test statistic for testing if correlation between `x1` and `z1` is the same as that between `x0` and `z0`
`p-value`	p-value of test
`signedStat`	Signed test statistic

Author(s)

Danyang Yu <[email protected]>, Weiliang Qiu <[email protected]>

References

Examples

x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
p = st2(x1, z1, x0, z0)
print(p)
x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
p = st2(x1, z1, x0, z0)
print(p)

Test Differential Correlation Using st3 Method

Description

Compute p-value for the equal correlation test with percentage bend correlation based on a logistic regression model corresponding to two independent groups

Usage

st3(x1, z1, x0, z0)
st3(x1, z1, x0, z0)

Arguments

`x1`	a numeric vector
`z1`	a numeric vector with same length as `x1`
`x0`	a numeric vector
`z0`	a numeric vector with same length as `x0`

Value

`stat`	Test statistic for testing if correlation between `x1` and `z1` is the same as that between `x0` and `z0`
`p-value`	p-value of test
`signedStat`	Signed test statistic

Author(s)

Danyang Yu <[email protected]>, Weiliang Qiu <[email protected]>

References

Examples

x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
p = st3(x1, z1, x0, z0)
print(p)
x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
p = st3(x1, z1, x0, z0)
print(p)

Test Differential Correlation Using st4 Method

Description

Compute p-value for the equal correlation test with Spearman corrleaion based on a logistic regression model corresponding to two independent groups

Usage

st4(x1, z1, x0, z0)
st4(x1, z1, x0, z0)

Arguments

`x1`	a numeric vector
`z1`	a numeric vector with same length as `x1`
`x0`	a numeric vector
`z0`	a numeric vector with same length as `x0`

Value

`stat`	Test statistic for testing if correlation between `x1` and `z1` is the same as that between `x0` and `z0`
`p-value`	p-value of test
`signedStat`	Signed test statistic

Author(s)

Danyang Yu <[email protected]>, Weiliang Qiu <[email protected]>

References

Examples

x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
p = st4(x1, z1, x0, z0)
print(p)
x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
p = st4(x1, z1, x0, z0)
print(p)

Test Differential Correlation Using st5 Method

Description

# Compute p-value for the equal correlation test with combination of Spearman corrleaion and percentage bend correlation based on a logistic regression model corresponding to two independent groups

Usage

st5(x1, z1, x0, z0)
st5(x1, z1, x0, z0)

Arguments

`x1`	a numeric vector
`z1`	a numeric vector with same length as `x1`
`x0`	a numeric vector
`z0`	a numeric vector with same length as `x0`

Value

`stat`	Test statistic for testing if correlation between `x1` and `z1` is the same as that between `x0` and `z0`
`p-value`	p-value of test
`signedStat`	Signed test statistic

Author(s)

Danyang Yu <[email protected]>, Weiliang Qiu <[email protected]>

References

Examples

x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
p = st5(x1, z1, x0, z0)
print(p)
x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
p = st5(x1, z1, x0, z0)
print(p)

Test Differential Correlation Using st6 Method

Description

Compute p-value for the equal correlation test with another way to combine Spearman corrleaion and percentage bend correlation based on a multiple logistic regression model corresponding to two independent groups

Usage

st6(x1, z1, x0, z0)
st6(x1, z1, x0, z0)

Arguments

`x1`	a numeric vector
`z1`	a numeric vector with same length as `x1`
`x0`	a numeric vector
`z0`	a numeric vector with same length as `x0`

Value

A list with 3 elements:

`u`	the vector of Fisher score.
`pval`	p-value of test for testing if correlation between `x1` and `z1` is the same as that between `x0` and `z0`
`cov_u`	covariance matrix for `u`.

Author(s)

Danyang Yu <[email protected]>, Weiliang Qiu <[email protected]>

References

Examples

x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
res = st6(x1, z1, x0, z0)
print(res)
x1 = ghdist(n = 100, g = 0.2, h = 0.2)
x0 = ghdist(n = 100, g = 0.2, h = 0.2)
z1 = x1 + ghdist(n = 100, g = 0.2, h = 0.2)
z0 = x0 + ghdist(n = 100, g = 0.2, h = 0.2)
res = st6(x1, z1, x0, z0)
print(res)

Package 'corTest'

Help Index

Construct Differential Correlation Network

Description

Usage

Arguments

Value

Author(s)

References

Examples

A BioConductor ExpressionSet Object Storing Gene Expression Data

Description

Usage

Format

Details

References

Examples

Test for equal correlation

Description

Usage

Arguments

Value

Author(s)

References

Examples

Generate Data for Control Subjects and Case Subjects

Description

Usage

Arguments

Value

Author(s)

References

Examples

Generate an ExpressionSet Object

Description

Usage

Arguments

Value

Author(s)

generating varibles from g-and-h distribution

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Scatter Plot of 2 Genes for Cases and Controls

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Test Differential Correlation Using st1 Method

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Test Differential Correlation Using st2 Method

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Test Differential Correlation Using st3 Method

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Test Differential Correlation Using st4 Method

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Usage