run_clr | R Documentation |
Conducts co-expression analysis using CLR \insertCitefaith07dnapath.
Uses the implementation from the minet
package \insertCitemeyer08dnapath.
Can be used for the network_inference
argument in dnapath
.
run_clr(x, weights = NULL, estimator = "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 |
estimator |
Argument is passed into |
... |
Additional arguments are ignored. |
A p by p matrix of association scores.
faith07dnapath
\insertRefmeyer08dnapath
run_aracne
,
run_bc3net
, run_c3net
, run_corr
,
run_dwlasso
, 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_clr() can be adjusted using the ... argument. # For example, the 'estimator' paramter 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_clr, 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]])
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