Package 'iDINGO' reference manual

Title:	Integrative Differential Network Analysis in Genomics
Description:	Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The methods are described in Class et. al., (2018) <doi:10.1093/bioinformatics/btx750> and Ha et. al., (2015) <doi:10.1093/bioinformatics/btv406>.
Authors:	Caleb A. Class <[email protected]>, Min Jin Ha <[email protected]>
Maintainer:	Caleb A. Class <[email protected]>
License:	GPL-2
Version:	1.0.4
Built:	2024-11-13 06:33:46 UTC
Source:	CRAN

iDINGO: Integrative Differential Network Analysis in Genomics

Description

This packages jointly estimates group specific partial correlations in a multi-level/platform data set.

Details

This packages jointly estimates group specific partial correlations in a multi-level/platform data set, considering the directionality of effects between platforms using a chain graph model.

Author(s)

Min Jin Ha, Caleb Class, Veerabhadran Baladandayuthapani, and Kim-Anh Do

Modified TCGA Breast Cancer data

Description

Modified TCGA Breast Cancer data.

Usage

data(brca)data(brca)

Format

Three data frames, with columns as standardized miRNA, gene, and protein expressions. One vector with the two classes of the samples (tumor or normal tissue). Also one iDINGO fit object, obtained by running the example in the idingo manual entry.

Fit DINGO model

Description

This function fit DINGO model and calculates edge-wise differential scores for all pairwise edges among p variables.

Usage

dingo(dat,x,rhoarray=NULL,diff.score=T,B=100,verbose=T,cores=1)
dingo(dat,x,rhoarray=NULL,diff.score=T,B=100,verbose=T,cores=1)

Arguments

`dat`	nxp data with colnames as genename
`x`	a length n vector representing a binary covariate
`rhoarray`	a vector representing candidate tuning parameters of glasso for fitting global network model. If it is one value, then we use the value as the tuning parameter. It is set by NULL as default and we select 100 candidate values.
`diff.score`	a logical value. If TRUE, edge-wise differential scores are calculated from bootstrap standard error. Otherwise, we fit Steps 1 and 2 of DINGO model to get group specific GGMs (partial correlations)
`B`	the number of bootstrap samples to calculate differential scores.
`verbose`	if TRUE, lists the procedure
`cores`	the number of cores to run in parallel for bootstrapping, set to 1 as a default. If more cores are specified than the recommended maximum (the number of cores detected minus 1), this value will be replaced by the recommended value.

Value

`genepair`	a p(p-1)/2 x 2 matrix indicating all pairs of genes
`levels.x`	a length 2 vector indicating levels of the binary covariate x, the first element is for group 1 and the second element is for group 2
`R1`	a length p(p-1)/2 vector indicating partial correlations for group 1 and the order is corresponding to the order of genepair
`R2`	a length p(p-1)/2 vector indicating partial correlations for group 2 and the order is corresponding to the order of genepair
`boot.diff`	a p(p-1)/2 x boot.B matrix indicating bootstrapped difference, Fisher's Z transformed R1 - R2. The rows are corresponding to the order of gene pair and the columns are corresponding to the bootstrap samples
`diff.score`	a p(p-1)/2 vector of differential score corresponding to genepair
`p.val`	a p(p-1)/2 vector of corrected p-values corresponding to genepair
`rho`	selected tuning parameter of glasso fit
`P`	p by p matrix of Global component of the DINGO model
`Q`	p by 2 matrix of the coefficient parameter of the local group specific component L(x) of the DINGO model.
`Psi`	p by p diagonal matrix of the noise covariance parameter of the local group specific component L(x) of the DINGO model.
`step.times`	a length 3 vector containing the elapsed time for Step 1, Step 2, and Bootstrap Scoring, respectively.

Author(s)

Min Jin HA [email protected], Caleb CLASS [email protected]

Examples

data(gbm)
# Run DINGO (the first column, 'x', contains the group data).
# This may take 5-10 minutes.
## Not run: fit <- dingo(gbm[,-1], gbm$x, diff.score = TRUE, B = 100, cores = 2)
data(gbm)
# Run DINGO (the first column, 'x', contains the group data).
# This may take 5-10 minutes.
## Not run: fit <- dingo(gbm[,-1], gbm$x, diff.score = TRUE, B = 100, cores = 2)

Extended bayesian information criteria for gaussian graphical models

Description

Extended bayesian information criteria for gaussian graphical models

Usage

extendedBIC(gamma,omegahat,S,n)
extendedBIC(gamma,omegahat,S,n)

Arguments

`gamma`	a tuning parameter taking a scalar in [0,1] and leading to stronger penalization of large graphs
`omegahat`	a p x p matrix indicating an estimates of precision (inverse covariance) matrix
`S`	a p x p matrix indicating sample covariance matrix
`n`	a scalar indicating sample size

Value

Extended BIC penalized by the size of graphs

Author(s)

Min Jin Ha <[email protected]>

References

Foygel, R. and Drton, M. (2010). Extended bayesian information criteria for gaussian graphical models. arXiv preprint arXiv:1011.6640 .

Examples

library(glasso)
data(gbm)
x = gbm[,1]
Y = gbm[,-1]

# Estimating inverse covariance matrix using GLasso #
S = cov(Y)

rhoarray = exp(seq(log(0.001),log(1),length=100))
BIC = rep(0,length(rhoarray))
for (rh in 1:length(rhoarray)) {
    fit.gl1 = glasso(S,rho=rhoarray[rh])
    BIC[rh] = extendedBIC(gamma=0,omegahat=fit.gl1$wi,S=S,n=nrow(Y))
}
rho = rhoarray[which.min(BIC)]
fit.gl2 = glasso(S,rho=rho)
Omega = fit.gl2$wi


library(glasso)
data(gbm)
x = gbm[,1]
Y = gbm[,-1]

# Estimating inverse covariance matrix using GLasso #
S = cov(Y)

rhoarray = exp(seq(log(0.001),log(1),length=100))
BIC = rep(0,length(rhoarray))
for (rh in 1:length(rhoarray)) {
    fit.gl1 = glasso(S,rho=rhoarray[rh])
    BIC[rh] = extendedBIC(gamma=0,omegahat=fit.gl1$wi,S=S,n=nrow(Y))
}
rho = rhoarray[which.min(BIC)]
fit.gl2 = glasso(S,rho=rho)
Omega = fit.gl2$wi

Modified TCGA Glioblastoma data

Description

Modified TCGA Glioblastoma data.

Usage

data(gbm)data(gbm)

Format

A data frame with first column as a covariate and other columns as standardized gene expressions.

Fitting precision regression models

Description

This function fits the covariance regression model by Hoff and Niu (2012) using EM algorithm with the restriction of diagonal matrix for the noise variance

Usage

Greg.em(formula, data = NULL, R = 1, tol = 1e-10, itmax = 1000, verbose = F)
Greg.em(formula, data = NULL, R = 1, tol = 1e-10, itmax = 1000, verbose = F)

Arguments

`formula`	an object of class "formula" used in model.frame function
`data`	a data frame used in model.frame function
`R`	rank of the model
`tol`	a stopping criterion
`itmax`	maximum number of iteration
`verbose`	If true, estimation results for each iteration are printed

Value

`A`	MLE of the baseline covariance matrix
`B`	MLE of the regression coefficients

Author(s)

Min Jin Ha <[email protected]>

References

Hoff, P. D. and Niu, X. (2012) A covariance regression model. Statistica Sinica, 22, 729-753.

Examples

library(glasso)
data(gbm)
x = gbm[,1]
Y = as.matrix(gbm[,-1])
p = ncol(Y)
# Estimating inverse covariance matrix using GLasso #
S = cov(Y)
w.upper = which(upper.tri(S))

rhoarray = exp(seq(log(0.001),log(1),length=100))
BIC = rep(0,length(rhoarray))
for (rh in 1:length(rhoarray)) {
    fit.gl1 = glasso(S,rho=rhoarray[rh])
    BIC[rh] = extendedBIC(gamma=0,omegahat=fit.gl1$wi,S=S,n=nrow(Y))
}
rho = rhoarray[which.min(BIC)]
fit.gl2 = glasso(S,rho=rho)
Omega = fit.gl2$wi

# Fitting (Covariance Regression on transformed data)
diag.Omega = diag(Omega)
P = -Omega/diag.Omega
diag(P) = 0

tY = Y 
mdat = apply(tY,2,mean)
sdat = apply(tY,2,sd)
std.tY = t((t(tY) - mdat)/sdat)
smat = diag(sdat)

## rank 1 covariance regression
fit.g = Greg.em(std.tY~x,R=1) 
library(glasso)
data(gbm)
x = gbm[,1]
Y = as.matrix(gbm[,-1])
p = ncol(Y)
# Estimating inverse covariance matrix using GLasso #
S = cov(Y)
w.upper = which(upper.tri(S))

rhoarray = exp(seq(log(0.001),log(1),length=100))
BIC = rep(0,length(rhoarray))
for (rh in 1:length(rhoarray)) {
    fit.gl1 = glasso(S,rho=rhoarray[rh])
    BIC[rh] = extendedBIC(gamma=0,omegahat=fit.gl1$wi,S=S,n=nrow(Y))
}
rho = rhoarray[which.min(BIC)]
fit.gl2 = glasso(S,rho=rho)
Omega = fit.gl2$wi

# Fitting (Covariance Regression on transformed data)
diag.Omega = diag(Omega)
P = -Omega/diag.Omega
diag(P) = 0

tY = Y 
mdat = apply(tY,2,mean)
sdat = apply(tY,2,sd)
std.tY = t((t(tY) - mdat)/sdat)
smat = diag(sdat)

## rank 1 covariance regression
fit.g = Greg.em(std.tY~x,R=1)

Fit iDINGO model

Description

This function fits the iDINGO model and calculates edge-wise differential scores for all pairwise edges among p variables between multiple platforms.

Usage

idingo(dat,dat2=NULL,dat3=NULL,x,plats=NULL,rhoarray=NULL,
        diff.score=T,B=100,verbose=T,cores=1)
idingo(dat,dat2=NULL,dat3=NULL,x,plats=NULL,rhoarray=NULL,
        diff.score=T,B=100,verbose=T,cores=1)

Arguments

`dat`	nxp dataframe/matrix with colnames as genename
`dat2`	Second nxp dataframe/matrix with colnames as genename (optional)
`dat3`	Third nxp dataframe/matrix with colnames as genename (optional)
`x`	a length n vector representing a binary covariate
`plats`	a length 1-3 vector (corresponding to the number of data sets submitted, with names for the platforms/levels of the data, such as "microRNA" or "RNAseq". This is optional, and default names "platN" will be used if names are not provided.
`rhoarray`	a vector representing candidate tuning parameters of glasso for fitting global network model. If it is one value, then we use the value as the tuning parameter. It is set by NULL as default and we select 100 candidate values.
`diff.score`	a logical value. If TRUE, edge-wise differential scores are calculated from bootstrap standard error. Otherwise, we fit Steps 1 and 2 of DINGO model to get group specific GGMs (partial correlations)
`B`	the number of bootstrap samples to calculate differential scores.
`verbose`	if TRUE, lists the procedure
`cores`	the number of cores to run in parallel for bootstrapping, set to 1 as a default. If more cores are specified than the recommended maximum (the number of cores detected minus 1), this value will be replaced by the recommended value.

Value

`genepair`	a p(p-1)/2 x 2 matrix indicating all pairs of genes
`levels.x`	a length 2 vector indicating levels of the binary covariate x, the first element is for group 1 and the second element is for group 2
`R1`	a length p(p-1)/2 vector indicating partial correlations for group 1 and the order is corresponding to the order of genepair
`R2`	a length p(p-1)/2 vector indicating partial correlations for group 2 and the order is corresponding to the order of genepair
`diff.score`	a p(p-1)/2 vector of differential score corresponding to genepair
`p.val`	a p(p-1)/2 vector of corrected p-values corresponding to genepair

Author(s)

Caleb CLASS [email protected], Min Jin HA [email protected]

Examples

data(brca)

# Run iDINGO with microRNA, RNA, and protein data.
# Generally, we recommend a minimum of 100 bootstraps.
## Not run: fit <- idingo(brca$mirna, dat2 = brca$rna, dat3 = brca$prot,
    x = brca$class, plats = c("microRNA", "RNA", "Protein"),
    diff.score = TRUE, B = 20, cores = 2)
## End(Not run)
data(brca)

# Run iDINGO with microRNA, RNA, and protein data.
# Generally, we recommend a minimum of 100 bootstraps.
## Not run: fit <- idingo(brca$mirna, dat2 = brca$rna, dat3 = brca$prot,
    x = brca$class, plats = c("microRNA", "RNA", "Protein"),
    diff.score = TRUE, B = 20, cores = 2)
## End(Not run)

Plot differential network

Description

This function plots the differential network from a completed DINGO or iDINGO model.

Usage

plotNetwork(fit, threshold=0.05, thresh.type="p.val", layout="circular",
            legend.pos="left")
plotNetwork(fit, threshold=0.05, thresh.type="p.val", layout="circular",
            legend.pos="left")

Arguments

`fit`	output from running dingo() or idingo()
`threshold`	a numeric value containing the threshold for which edges will be included in the differential network plot. If 'thresh.type' is 'p.val', all edges with p-values below this threshold will be included in the plot. If 'thresh.type' is 'diff.score', all edges with absolute differential scores above this threshold will be included in the plot.
`thresh.type`	either 'p.val' or 'diff.score', defining which variable is used as threshold for edge inclusion.
`layout`	either 'circular' or one of igraph's supported layouts. If 'circular', dingo networks will be plotted in a circle, and idingo networks will be plotted as a cylinder (with each platform/level as a separate circle).
`legend.pos`	Legend position for multi-platform networks, in c("left", "right"). Legend is not included for single-platform networks.

Value

visNet

a network plot, using igraph and visNetwork

Note

To calculate differential scores and p-values for use in network plot thresholding, diff.score must be set to TRUE in dingo() or idingo().

Author(s)

Caleb CLASS [email protected], Min Jin HA [email protected]

Examples

data(brca)

# Plot the iDINGO result using a p-value threshold of 0.01.
plotNetwork(brca$fit, threshold = 0.01, thresh.type = "p.val")
data(brca)

# Plot the iDINGO result using a p-value threshold of 0.01.
plotNetwork(brca$fit, threshold = 0.01, thresh.type = "p.val")

scale a square matrix

Description

scale a square matrix to have unit diagonal elements.

Usage

scaledMat(x)
scaledMat(x)

Arguments

`x`	a square matrix with positive diagonal elements

Value

scaled matrix of x

Author(s)

Min Jin Ha [email protected]

Calculating differential score

Description

This function calculates standard errors for edge-wise partial correlation differences obtained from DINGO model.

Usage

scoring.boot(stddat,z,Omega,A,B,boot.B=100,verbose=T)
scoring.boot(stddat,z,Omega,A,B,boot.B=100,verbose=T)

Arguments

`stddat`	standardized nxp data with colnames as genename
`z`	a length n vector representing a binary covariate
`Omega`	a p x p precision matrix for std dat which implies the global network
`A`	p x p matrix of the MLE for the baseline covariance matrix which is obtained from A value of the Greg.em function.
`B`	p x 2 matrix of the MLE for the regression coefficient which is obtained from B value of the Greg.em function
`boot.B`	a scalar indicating the number of bootstraps
`verbose`	if TRUE, lists the bootstrap replications

Value

`genepair`	a p(p-1)/2 x 2 matrix indicating all pairs of genes
`levels.z`	a length 2 vector indicating levels of the binary covariate z, the first element is for group 1 and the second element is for group 2
`R1`	a length p(p-1)/2 vector indicating partial correlations for group 1 and the order is corresponding to the order of genepair
`R2`	a length p(p-1)/2 vector indicating partial correlations for group 2 and the order is corresponding to the order of genepair
`boot.diff`	a p(p-1)/2 x boot.B matrix indicating bootstrapped difference, Fisher's Z transformed R1 - R2. The rows are corresponding to the order of gene pair and the columns are corresponding to the bootstrap samples
`diff.score`	a p(p-1)/2 vector of differential score corresponding to genepair
`p.val`	a p(p-1)/2 vector of corrected p-values corresponding to genepair

Author(s)

Min Jin HA [email protected]

Calculating differential score with parallel bootstrap scoring

Description

This function calculates standard errors for edge-wise partial correlation differences obtained from DINGO model. Bootstrapping is done in parallel using parSapply from the "parallel" library.

Usage

scoring.boot.parallel(stddat,z,Omega,A,B,boot.B=100,verbose=T,cores=1)
scoring.boot.parallel(stddat,z,Omega,A,B,boot.B=100,verbose=T,cores=1)

Arguments

`stddat`	standardized nxp data with colnames as genename
`z`	a length n vector representing a binary covariate
`Omega`	a p x p precision matrix for std dat which implies the global network
`A`	p x p matrix of the MLE for the baseline covariance matrix which is obtained from A value of the Greg.em function.
`B`	p x 2 matrix of the MLE for the regression coefficient which is obtained from B value of the Greg.em function
`boot.B`	a scalar indicating the number of bootstraps
`verbose`	if TRUE, lists the bootstrap replications
`cores`	the number of cores to run in parallel for bootstrapping, set to 1 as a default.

Value

`genepair`	a p(p-1)/2 x 2 matrix indicating all pairs of genes
`levels.z`	a length 2 vector indicating levels of the binary covariate z, the first element is for group 1 and the second element is for group 2
`R1`	a length p(p-1)/2 vector indicating partial correlations for group 1 and the order is corresponding to the order of genepair
`R2`	a length p(p-1)/2 vector indicating partial correlations for group 2 and the order is corresponding to the order of genepair
`boot.diff`	a p(p-1)/2 x boot.B matrix indicating bootstrapped difference, Fisher's Z transformed R1 - R2. The rows are corresponding to the order of gene pair and the columns are corresponding to the bootstrap samples
`diff.score`	a p(p-1)/2 vector of differential score corresponding to genepair
`p.val`	a p(p-1)/2 vector of corrected p-values corresponding to genepair

Author(s)

Min Jin HA [email protected], Caleb CLASS [email protected]

group specific covariance matrices

Description

From parameters of DINGO model, group specific covariance matrices are obtained

Usage

Sigmax(P = NULL, Q, Psi, x)
Sigmax(P = NULL, Q, Psi, x)

Arguments

`P`	a p x p matrix specifying global component
`Q`	the coefficient parameter matrix of covariance regression model using Greg.em function
`Psi`	the diagonal error variance matrix of covariance regression model using Greg.em function
`x`	a vector specifying group. This must be corresponding to the design matrix of Greg.em function

Value

group specific precision matrix

Author(s)

Min Jin Ha <[email protected]>

Examples

library(glasso)
data(gbm)
x = gbm[,1]
Y = as.matrix(gbm[,-1])
p = ncol(Y)
# Estimating inverse covariance matrix using GLasso #
S = cov(Y)
w.upper = which(upper.tri(S))

rhoarray = exp(seq(log(0.001),log(1),length=100))
BIC = rep(0,length(rhoarray))
for (rh in 1:length(rhoarray)) {
    fit.gl1 = glasso(S,rho=rhoarray[rh])
    BIC[rh] = extendedBIC(gamma=0,omegahat=fit.gl1$wi,S=S,n=nrow(Y))
}
rho = rhoarray[which.min(BIC)]
fit.gl2 = glasso(S,rho=rho)
Omega = fit.gl2$wi

# Fitting (Covariance Regression on transformed data)
diag.Omega = diag(Omega)
P = -Omega/diag.Omega
diag(P) = 0

tY = Y 
mdat = apply(tY,2,mean)
sdat = apply(tY,2,sd)
std.tY = t((t(tY) - mdat)/sdat)
smat = diag(sdat)

## rank 1 covariance regression
fit.g = Greg.em(std.tY~x,R=1) 
## obtain covariance matrix of Y when x=1
sigmaX1 = Sigmax(Q=fit.g$B,P=P,Psi=fit.g$A,x=c(1,1)) 
library(glasso)
data(gbm)
x = gbm[,1]
Y = as.matrix(gbm[,-1])
p = ncol(Y)
# Estimating inverse covariance matrix using GLasso #
S = cov(Y)
w.upper = which(upper.tri(S))

rhoarray = exp(seq(log(0.001),log(1),length=100))
BIC = rep(0,length(rhoarray))
for (rh in 1:length(rhoarray)) {
    fit.gl1 = glasso(S,rho=rhoarray[rh])
    BIC[rh] = extendedBIC(gamma=0,omegahat=fit.gl1$wi,S=S,n=nrow(Y))
}
rho = rhoarray[which.min(BIC)]
fit.gl2 = glasso(S,rho=rho)
Omega = fit.gl2$wi

# Fitting (Covariance Regression on transformed data)
diag.Omega = diag(Omega)
P = -Omega/diag.Omega
diag(P) = 0

tY = Y 
mdat = apply(tY,2,mean)
sdat = apply(tY,2,sd)
std.tY = t((t(tY) - mdat)/sdat)
smat = diag(sdat)

## rank 1 covariance regression
fit.g = Greg.em(std.tY~x,R=1) 
## obtain covariance matrix of Y when x=1
sigmaX1 = Sigmax(Q=fit.g$B,P=P,Psi=fit.g$A,x=c(1,1))

Calculating differential score for a single bootstrap

Description

This function calculates the edge-wise partial correlation difference for a single bootstrap.

Usage

single.boot(i, z, n, tY.org, P, levels.z, w.upper)
single.boot(i, z, n, tY.org, P, levels.z, w.upper)

Arguments

`i`	iteration number. This is not used within this function, but necessary for parSapply within scoring.boot.parallel function.
`z`	a length n vector representing a binary covariate
`n`	the number of rows in data
`tY.org`	the transformed standardized data
`P`	the global correlation component
`levels.z`	the levels of the covariates
`w.upper`	the upper triangular of Omega

Value

boot.diff

the difference for this bootstrap

Author(s)

Min Jin HA [email protected], Caleb CLASS [email protected]

Fisher's Z-transformation

Description

Fisher's Z-transformation of (partial) correlation.

Arguments

`x`	a vector having entries between -1 and 1

Value

Fisher's Z-transformed values

Author(s)

Min Jin HA [email protected]

Package 'iDINGO'

Help Index

iDINGO: Integrative Differential Network Analysis in Genomics

Description

Details

Author(s)

Modified TCGA Breast Cancer data

Description

Usage

Format

Fit DINGO model

Description

Usage

Arguments

Value

Author(s)

Examples

Extended bayesian information criteria for gaussian graphical models

Description

Usage

Arguments

Value

Author(s)

References

Examples

Modified TCGA Glioblastoma data

Description

Usage

Format

Fitting precision regression models

Description

Usage

Arguments

Value

Author(s)

References

Examples

Fit iDINGO model

Description

Usage

Arguments

Value

Author(s)

Examples

Plot differential network

Description

Usage

Arguments

Value

Note

Author(s)

Examples

scale a square matrix

Description

Usage

Arguments

Value

Author(s)

Calculating differential score

Description

Usage

Arguments

Value

Author(s)

Calculating differential score with parallel bootstrap scoring

Description

Usage

Arguments

Value

Author(s)

group specific covariance matrices

Description

Usage

Arguments

Value

Author(s)

Examples

Calculating differential score for a single bootstrap

Description

Usage