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nethet (version 1.4.2)

A bioconductor package for high-dimensional exploration of biological network heterogeneity

Description

Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013).

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Version

Version

1.4.2

License

GPL-2

Maintainer

Nicolas Staedler

Last Published

February 15th, 2017

Functions in nethet (1.4.2)

NetHet-package

NetHet-package
diffnet_singlesplit

Differential Network for user specified data splits
est2.my.ev2

Weights of sum-w-chi2
aic.glasso

AIC.glasso
cv.glasso

Crossvalidation for GLasso
generate_inv_cov

generate_inv_cov
agg.score.iriz.shift

Irizarry aggregate score (shift)
glasso.invcov

Graphical Lasso based on inverse correlation penalty
EXPStep.mix

Performs EStep
beta.mat.diffregr

Computation beta matrix
mytrunc.method

Additional thresholding
gsea.iriz.shift

Irizarry approach (shift only)
agg.pval

P-value aggregation (Meinshausen et al 2009)
mixglasso

mixglasso
diffregr_multisplit

Differential Regression (multi-split version).
plot_2networks

Plot two networks (GGMs)
ggmgsa_singlesplit

Single-split GGMGSA
glasso.invcor

Graphical Lasso based on inverse covariance penalty
agg.score.iriz.scale

Irizarry aggregate score (scale)
buildDotPlotDataFrame

Build up dataframe for plotting dot plot with ggplot2
logratio

Log-likelihood-ratio statistics used in DiffNet
q.matrix.diffregr4

Computation Q matrix
logratio.diffregr

Log-likelihood ratio statistics for Differential Regression.
bic.glasso

BIC.glasso
invcov2parcor

Convert inverse covariance to partial correlation
make_grid

Make grid
my.ttest2

T-test
screen_cvtrunc.lasso

Cross-validated Lasso screening and additional truncation.
gsea.iriz.scale

Irizarry approach (scale only)
plot.diffregr

Plotting function for object of class 'diffregr'
screen_cv.glasso

Cross-validated glasso with additional thresholding
screen_aic.glasso

AIC-tuned glasso with additional thresholding
diffregr_pval

Computation "split-asym" p-values.
screen_cv1se.lasso

Cross-validated Lasso screening (lambda.1se-rule)
summary.ggmgsa

Summary function for object of class 'ggmgsa'
getinvcov

Generate an inverse covariance matrix with a given sparsity and dimensionality
diffnet_multisplit

Differential Network
t2diagcov.lr

Diagonal-restricted likelihood-ratio test
my.ttest

T-test
cv.fold

Make folds
ww.mat2.diffregr

Computation M matrix and eigenvalues
plot.ggmgsa

Plotting function for object of class 'ggmgmsa'
plot.diffnet

Plotting function for object of class 'diffnet'
plotCV

plotCV
mle.ggm

MLE in GGM
dot_plot

Create a plot showing the edges with the highest partial correlation in any cluster.
screen_bic.glasso

BIC-tuned glasso with additional thresholding
func.uinit

Initialization of MixGLasso
gsea.t2cov

GSA using T2cov-test
sim_mix_networks

sim_mix_networks
symmkldist

Compute symmetric kull-back leibler distance
ww.mat2

Calculates eigenvalues of weight-matrix (using 1st order simplification)
screen_full

Screen_full
ggmgsa_multisplit

Multi-split GGMGSA (parallelized computation)
test.sd

High-Dim Two-Sample Test (Srivastava, 2006)
sim_mix

Simulate from mixture model.
test.t2

HotellingsT2
sparse_conc

Generates sparse inverse covariance matrices
lambda.max

Lambdamax
q.matrix.diffregr3

Computation Q matrix
screen_mb2

Screen_mb2
est2.ww.mat.diffregr

Estimate weights
est2.my.ev2.diffregr

Compute weights of sum-w-chi2 (2nd order simplification)
het_cv_glasso

Cross-validated glasso on heterogeneous dataset with grouping
gsea.highdimT2

GSA based on HighdimT2
my.ev2.diffregr

Computation eigenvalues
screen_shrink

Shrinkage approach for estimating Gaussian graphical model
t2cov.lr

Classical likelihood-ratio test
est2.ww.mat2

Weights of sum-w-chi2
w.kldist

Distance between comps based on symm. kl-distance
generate_2networks

Generate sparse invcov with overlap
ww.mat.diffregr

Computation M matrix and eigenvalues
screen_lasso

Screen_lasso
hugepath

Graphical Lasso path with huge package
error.bars

Error bars for plotCV
inf.mat

Information Matrix of Gaussian Graphical Model
q.matrix3

Compute Q-matrix
screen_cvfix.lasso

Cross-validated Lasso screening and upper bound on number of predictors.
screen_cvsqrt.lasso

Cross-validated Lasso screening and sqrt-truncation.
twosample_single_regr

old single-split function for diffregr
bwprun_mixglasso

bwprun_mixglasso
MStepGlasso

MStep of MixGLasso
q.matrix4

q.matrix4
shapiro_screen

Filter "non-normal" genes
summary.diffnet

Summary function for object of class 'diffnet'
tr

Compute trace of matrix
est2.my.ev3.diffregr

Compute weights of sum-of-weighted-chi2s
est2.ww.mat2.diffregr

Estimate weights
invcov2parcor_array

Convert inverse covariance to partial correlation for several inverse covariance matrices collected in an array.
mixglasso_init

mixglasso_init
perm.diffregr_teststat

Auxiliary function for computation of "split-perm" p-value.
plot.nethetclustering

Plot networks
mixglasso_ncomp_fixed

mixglasso_ncomp_fixed
screen_cvmin.lasso

Cross-validation lasso screening (lambda.min-rule)
sumoffdiag

Sum of non-diag elements of a matrix
aggpval

Meinshausen p-value aggregation
diffregr_singlesplit

Differential Regression (single-split version).
beta.mat

Compute beta-matrix
est2.my.ev3

Compute weights of sum-of-weighted-chi2s
export_network

Export networks as a CSV table.
gsea.iriz

Irizarry approach for gene-set testing
glasso.parcor

Graphical Lasso based on partial correlation penalty
lambdagrid_lin

Lambda-grid
mcov

Compute covariance matrix
my.p.adjust

P-value adjustment
perm.diffregr_pval

Computation "split-perm" p-value.
print.nethetsummary

Print function for object of class 'nethetsummmary'
summary.nethetclustering

Summary function for object of class 'nethetclustering'
ww.mat

Weight-matrix and eigenvalues
diffnet_pval

P-value calculation
lambdagrid_mult

Lambda-grid
loglik_mix

Log-likelihood for mixture model
q.matrix.diffregr

Computation Q matrix
scatter_plot

Create a scatterplot showing correlation between specific nodes in the network for each pre-specified group.
screen_mb

Node-wise Lasso-regressions for GGM estimation
summary.diffregr

Summary function for object of class 'diffregr'