run_dwlasso {dnapath}

Wrapper for degree-weighted lasso method

Description

Conducts co-expression analysis using DWLasso (Sulaimanov et al. 2018). Uses the implementation from the DWLasso package (Sulaimanov et al. 2017). Can be used for the network_inference argument in dnapath.

Usage

run_dwlasso(x, weights = NULL, lambda1 = 0.4, lambda2 = 2, ...)

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 dnapath() to apply the probabilistic group labels to each observation when estimating the group-specific network.

lambda1

A penalty parameter that controls degree sparsity of the inferred network. See DWLasso for details.

lambda2

A penalty parameter that controls overall sparsity of the inferred network. See DWLasso for details.

...

Additional arguments are ignored.

Value

A p by p matrix of association scores.

References

Sulaimanov N, Kumar S, Burdet F, Ibberson M, Pagni M, Koeppl H (2018). “Inferring Gene Expression Networks with Hubs using a Degree Weighted Lasso Approach.” Bioinformatics, 35(6), 987–994.

Sulaimanov N, Kumar S, Koeppl H (2017). DWLasso: Degree Weighted Lasso. R package version 1.1, https://CRAN.R-project.org/package=DWLasso.

See Also

run_aracne, run_bc3net, run_c3net, run_clr, run_corr, 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 1 permutation for significance testing.
pathway_list <- p53_pathways[c(8, 13)]
n_perm <- 1

# Use this method to perform differential network analysis.
# The parameters in run_dwlasso() can be adjusted using the ... argument.
# For example, the 'lambda1' 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_dwlasso,
                   lambda1 = 0.5)
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]])