dnapath (version 0.6.4)

run_glasso: Wrapper for glasso method

Description

Conducts co-expression analysis using glasso friedman18dnapath. Uses the implementation from the huge package hugednapath. 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,
  ...
)

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

...

Additional arguments are ignored.

Value

A p by p matrix of association scores.

References

friedman18dnapath

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

# NOT RUN {
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$gene_expression,
                   pathway_list = pathway_list,
                   groups = meso$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.

# }
# NOT RUN {
# 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]])
# }