dnapath (version 0.7.6)

run_genie3: Wrapper for GENIE3 method

Description

Conducts co-expression analysis using GENIE3 huynhthu10dnapath. Uses the implementation from the GENIE3 package. Can be used for the network_inference argument in dnapath.

Usage

run_genie3(x, weights = NULL, nTrees = 200, ...)

Value

A p by p matrix of association scores.

Arguments

x

A n by p matrix of gene expression data (n samples and p genes).

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.

nTrees

Argument is passed into GENIE3.

...

Additional arguments are ignored.

References

huynhthu10dnapath

See Also

run_aracne, run_bc3net, run_c3net, run_clr, run_corr, run_glasso, run_mrnet, run_pcor, and run_silencer

Examples

if(!requireNamespace("GENIE3", quietly = TRUE)) {
data(meso)
data(p53_pathways)

# To create a short example, we subset on two pathways from the p53 pathway list,
# and will only run 5 permutations for significance testing.
pathway_list <- p53_pathways[c(8, 13)]
n_perm <- 5

# Use this method to perform differential network analysis.
# The parameters in run_genie3() can be adjusted using the ... argument.
# For example, the 'nTrees' 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_genie3,
                   nTrees = 100)
summary(results)

# The group-specific association matrices can be extracted using get_networks().
nw_list <- get_networks(results[[1]]) # 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[[1]]) # 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]])
}