run_corr | R Documentation |
Conducts co-expression analysis using correlation for association measure.
Can be used for the network_inference
argument in dnapath
.
run_corr(
x,
weights = NULL,
threshold = NULL,
method = c("pearson", "spearman"),
...
)
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 |
threshold |
Cutoff for significant associations. If NULL, all correlations are returned. Otherwise, correlations of magnitude at or below this threshold are set to zero. |
method |
Argument is passed into |
... |
Additional arguments are ignored. |
A p by p matrix of association scores.
run_aracne
,
run_bc3net
, run_c3net
,
run_clr
, run_genie3
,
run_glasso
, run_mrnet
,
run_pcor
, and run_silencer
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_corr() 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_corr,
method = "spearman")
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]])
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