run_glasso: Wrapper for glasso method

In dnapath: Differential Network Analysis using Gene Pathways

Wrapper for glasso method

Description

Conducts co-expression analysis using glasso \insertCitefriedman18dnapath. Uses the implementation from the huge package \insertCitehugednapath. Can be used for the network_inference argument in dnapath.

Usage

run_glasso(
  x,
  method = c("glasso", "mb", "ct"),
  criterion = c("ric", "stars"),
  verbose = FALSE,
  weights = NULL,
  ...
)

Arguments

x	A n by p matrix of gene expression data (n samples and p genes).
method	Argument is passed into huge.
criterion	Argument is passed into huge.select.
verbose	Argument is passed into huge and huge.select
weights	An optional vector of weights. This is used by dnapath() to apply the probabilistic group labels to each observation when estimating the group-specific network.
...	Additional arguments are ignored.

Value

A p by p matrix of association scores.

References

\insertRef

friedman18dnapath

\insertRef

hugednapath

See Also

run_aracne, run_bc3net, run_c3net, run_clr, run_corr, run_dwlasso, run_genie3, run_mrnet, run_pcor, and run_silencer

Examples

data(meso)
data(p53_pathways)

# To create a short example, we subset on one pathway from the p53 pathway list,
# and will only run 1 permutation for significance testing.
pathway_list <- p53_pathways[13]
n_perm <- 1

# Use this method to perform differential network analysis.
# The parameters in run_glasso() can be adjusted using the ... argument.
# For example, the 'criterion' parameter can be specified as shown here.
results <- dnapath(x = meso$gene_expression,
                   pathway_list = pathway_list,
                   group_labels = meso$groups,
                   n_perm = n_perm,
                   network_inference = run_glasso,
                   criterion = "ric")
summary(results)

# The group-specific association matrices can be extracted using get_networks().
nw_list <- get_networks(results) # Get networks for pathway 1.


# nw_list has length 2 and contains the inferred networks for the two groups.
# The gene names are the Entrezgene IDs from the original expression dataset.
# Renaming the genes in the dnapath results to rename those in the networks.
# NOTE: The temporary directory, tempdir(), is used in this example. In practice,
#       this argument can be removed or changed to an existing directory
results <- rename_genes(results, to = "symbol", species = "human",
                        dir_save = tempdir())
nw_list <- get_networks(results) # The genes (columns) will have new names.

# (Optional) Plot the network using SeqNet package (based on igraph plotting).
# First rename entrezgene IDs into gene symbols.
SeqNet::plot_network(nw_list[[1]])

dnapath documentation built on May 9, 2022, 9:05 a.m.