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)

Arguments

Value

A list with 6 elements:

Author(s)

Danyang Yu <dyu33@jhu.edu>, Weiliang Qiu <weiliang.qiu@gmail.com>

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)

A BioConductor ExpressionSet Object Storing Gene Expression Data

Description

A BioConductor ExpressionSet object storing gene expression data.

Usage

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

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

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)

Arguments

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 <dyu33@jhu.edu>, Weiliang Qiu <weiliang.qiu@gmail.com>

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

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 an ExpressionSet Object

Description

Generate an ExpressionSet object.

Usage

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

Arguments

Value

A BioConductor ExpressionSet object.

Author(s)

Danyang Yu <dyu33@jhu.edu>, Weiliang Qiu <weiliang.qiu@gmail.com>

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)

Arguments

Value

A list with 3 elements:

Author(s)

Danyang Yu <dyu33@jhu.edu>, Weiliang Qiu <weiliang.qiu@gmail.com>

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

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))

generating varibles from g-and-h distribution

Description

generating varibles from g-and-h distribution

Usage

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

Arguments

Value

n varibles generated from g-and-h distribution

Examples

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") 

Arguments

Value

A list with 4 elments:

Author(s)

Danyang Yu <dyu33@jhu.edu>, Weiliang Qiu <weiliang.qiu@gmail.com>

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

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)

Arguments

Value

Author(s)

Danyang Yu <dyu33@jhu.edu>, Weiliang Qiu <weiliang.qiu@gmail.com>

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

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)

Arguments

Value

Author(s)

Danyang Yu <dyu33@jhu.edu>, Weiliang Qiu <weiliang.qiu@gmail.com>

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

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)

Arguments

Value

Author(s)

Danyang Yu <dyu33@jhu.edu>, Weiliang Qiu <weiliang.qiu@gmail.com>

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

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)

Arguments

Value

Author(s)

Danyang Yu <dyu33@jhu.edu>, Weiliang Qiu <weiliang.qiu@gmail.com>

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

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)

Arguments

Value

Author(s)

Danyang Yu <dyu33@jhu.edu>, Weiliang Qiu <weiliang.qiu@gmail.com>

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

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)

Arguments

Value

A list with 3 elements:

Author(s)

Danyang Yu <dyu33@jhu.edu>, Weiliang Qiu <weiliang.qiu@gmail.com>

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

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)