nethet package - RDocumentation

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Package for analysing heterogeneous network data

Consists of:

mixglasso - Mixture Graphical Lasso

ggm_gsa - Graphical Gaussian Model Gene Set Analysis

DiffNet and DiffRegr - Statistical Test for difference in network and regression models.

Plotting and exporting - Convenient display and analysis of results.

Easy installation from github:

library('devtools')

install_github('FrankD/NetHet')

Also available on Bioconductor-devel (with R-devel > 3.2 only):

source("http://bioconductor.org/biocLite.R")

biocLite("nethet")

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Version

1.4.0

License

GPL-2

Maintainer

Nicolas Staedler

Last Published

February 15th, 2017

Functions in nethet (1.4.0)

beta.mat.diffregr

Computation beta matrix

buildDotPlotDataFrame

Build up dataframe for plotting dot plot with ggplot2

P-value aggregation (Meinshausen et al 2009)

aggpval

Meinshausen p-value aggregation

bwprun_mixglasso

agg.score.iriz.shift

Irizarry aggregate score (shift)

agg.score.iriz.scale

Irizarry aggregate score (scale)

dot_plot

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

diffregr_multisplit

Differential Regression (multi-split version).

diffnet_singlesplit

Differential Network for user specified data splits

cv.glasso

Crossvalidation for GLasso

diffregr_pval

Computation "split-asym" p-values.

error.bars

Error bars for plotCV

cv.fold

Make folds

diffregr_singlesplit

Differential Regression (single-split version).

diffnet_multisplit

Differential Network

func.uinit

Initialization of MixGLasso

export_network

Export networks as a CSV table.

est2.my.ev3

Compute weights of sum-of-weighted-chi2s

est2.my.ev2.diffregr

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

est2.ww.mat2

Weights of sum-w-chi2

est2.my.ev3.diffregr

Compute weights of sum-of-weighted-chi2s

est2.my.ev2

Weights of sum-w-chi2

est2.ww.mat.diffregr

Estimate weights

est2.ww.mat2.diffregr

Irizarry approach for gene-set testing

gsea.highdimT2

GSA based on HighdimT2

het_cv_glasso

Cross-validated glasso on heterogeneous dataset with grouping

Log-likelihood for mixture model

ggmgsa_multisplit

Multi-split GGMGSA (parallelized computation)

getinvcov

Generate an inverse covariance matrix with a given sparsity and dimensionality

Compute covariance matrix

q.matrix3

Compute Q-matrix

screen_aic.glasso

AIC-tuned glasso with additional thresholding

my.ev2.diffregr

Computation eigenvalues

screen_bic.glasso

BIC-tuned glasso with additional thresholding

sim_mix

Simulate from mixture model.

sim_mix_networks

symmkldist

Compute symmetric kull-back leibler distance

invcov2parcor_array

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

generate_2networks

Generate sparse invcov with overlap

invcov2parcor

Convert inverse covariance to partial correlation

generate_inv_cov

mixglasso_init

mixglasso_ncomp_fixed

my.ttest2

T-test

t2cov.lr

Classical likelihood-ratio test

hugepath

Graphical Lasso path with huge package

inf.mat

Information Matrix of Gaussian Graphical Model

screen_cvsqrt.lasso

Cross-validated Lasso screening and sqrt-truncation.

perm.diffregr_teststat

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

screen_cvtrunc.lasso

Cross-validated Lasso screening and additional truncation.

plot_2networks

Plot two networks (GGMs)

plotCV

print.nethetsummary

Print function for object of class 'nethetsummmary'

Summary function for object of class 'diffregr'

summary.ggmgsa

Summary function for object of class 'ggmgsa'

glasso.parcor

Graphical Lasso based on partial correlation penalty

glasso.invcov

Graphical Lasso based on inverse correlation penalty

gsea.iriz.shift

Irizarry approach (shift only)

mixglasso

gsea.iriz.scale

Irizarry approach (scale only)

Plotting function for object of class 'ggmgmsa'

plot.nethetclustering

Plot networks

screen_cvfix.lasso

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

screen_cvmin.lasso

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

test.t2

HotellingsT2

Compute trace of matrix

twosample_single_regr

old single-split function for diffregr

w.kldist

Distance between comps based on symm. kl-distance

ggmgsa_singlesplit

Single-split GGMGSA

logratio

Log-likelihood-ratio statistics used in DiffNet

glasso.invcor

Graphical Lasso based on inverse covariance penalty

logratio.diffregr

Log-likelihood ratio statistics for Differential Regression.

NetHet-package

plot.diffnet

Plotting function for object of class 'diffnet'

perm.diffregr_pval

Computation "split-perm" p-value.

plot.diffregr

Plotting function for object of class 'diffregr'

mytrunc.method

Additional thresholding

Cross-validated glasso with additional thresholding

screen_cv1se.lasso

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

screen_mb

Node-wise Lasso-regressions for GGM estimation

sparse_conc

Generates sparse inverse covariance matrices

summary.diffnet

Summary function for object of class 'diffnet'

ww.mat2.diffregr

Computation M matrix and eigenvalues

ww.mat2

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

q.matrix4

screen_shrink

Shrinkage approach for estimating Gaussian graphical model

scatter_plot

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

ww.mat.diffregr

Computation M matrix and eigenvalues

t2diagcov.lr

Diagonal-restricted likelihood-ratio test

test.sd

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

ww.mat

Weight-matrix and eigenvalues

shapiro_screen

Filter "non-normal" genes

summary.nethetclustering

Summary function for object of class 'nethetclustering'

screen_mb2

Screen_mb2

sumoffdiag

Sum of non-diag elements of a matrix