run_aracne {dnapath} | R Documentation |
Wrapper for ARACNE method
Description
Conducts co-expression analysis using ARACNE (Margolin et al. 2006).
Uses the implementation from the minet
package (Meyer et al. 2008).
Can be used for the network_inference
argument in dnapath
.
Usage
run_aracne(
x,
weights = NULL,
estimator = "spearman",
disc = "none",
nbins = NULL,
eps = 0,
...
)
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 |
estimator |
Argument is passed into |
disc |
Argument is passed into |
nbins |
Argument is passed into |
eps |
Argument is passed into |
... |
Additional arguments are ignored. |
Value
A p by p matrix of association scores.
References
Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A (2006). “ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context.” In BMC Bioinformatics, volume 7(1), S7. BioMed Central.
Meyer PE, Lafitte F, Bontempi G (2008). “minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks using Mutual Information.” BMC Bioinformatics, 9(1), 461.
See Also
run_bc3net
, run_c3net
,
run_clr
, run_corr
,
run_dwlasso
, run_genie3
,
run_glasso
, run_mrnet
,
run_pcor
, and run_silencer
Examples
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_aracne() can be adjusted using the ... argument.
# For example, the 'estimator' 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_aracne,
estimator = "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]])