data(meso)
data(p53_pathways)
# To create a short example, we subset on two pathways 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_silencer() can be adjusted using the ... argument.
# For example, the 'method' 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_silencer,
method = "spearman")
summary(results)
# The group-specific association matrices can be extracted using get_networks().
nw_list <- get_networks(results) # Get networks for the pathway
# \donttest{
# 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]])
# }
Run the code above in your browser using DataLab