# run_bc3net: Wrapper for BC3Net method In dnapath: Differential Network Analysis using Gene Pathways

## Description

Conducts co-expression analysis using BC3Net \insertCitematos12dnapath. Uses the implementation from the `bc3net` package \insertCitebc3netdnapath. Can be used for the `network_inference` argument in `dnapath`.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```run_bc3net( x, boot = 100, estimator = "spearman", disc = "equalwidth", mtc1 = TRUE, adj1 = "bonferroni", alpha1 = 0.05, mtc2 = TRUE, adj2 = "bonferroni", alpha2 = 0.05, ... ) ```

## Arguments

 `x` A n by p matrix of gene expression data (n samples and p genes). `boot` Argument is passed into `bc3net`. `estimator` Argument is passed into `bc3net`. `disc` Argument is passed into `bc3net`. `mtc1` Argument is passed into `bc3net`. `adj1` Argument is passed into `bc3net`. `alpha1` Argument is passed into `bc3net`. `mtc2` Argument is passed into `bc3net`. `adj2` Argument is passed into `bc3net`. `alpha2` Argument is passed into `bc3net`. `...` Additional arguments are ignored.

## Value

A p by p matrix of association scores.

## References

\insertRef

matos12dnapath

\insertRef

bc3netdnapath

`run_aracne`, `run_c3net`, `run_clr`, `run_corr`, `run_dwlasso`, `run_genie3`, `run_glasso`, `run_mrnet`, `run_pcor`, and `run_silencer`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38``` ```data(meso) data(p53_pathways) # To create a short example, we subset on one pathway 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_bc3net() can be adjusted using the ... argument. # For example, the 'estimator' and 'boot' parameter can be specified as shown here. results <- dnapath(x = meso\$gene_expression, pathway_list = pathway_list, groups = meso\$groups, n_perm = n_perm, network_inference = run_bc3net, boot = 10, estimator = "pearson", mtc1 = FALSE, mtc2 = FALSE) summary(results) # The group-specific association matrices can be extracted using get_networks(). nw_list <- get_networks(results) # 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) # 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]]) ```