dnapath  R Documentation 
Integrates pathways into the differential network analysis of gene expression data \insertCitegrimes19dnapath.
dnapath( x, pathway_list, group_labels = NULL, network_inference = run_pcor, n_perm = 100, lp = 2, seed = NULL, verbose = FALSE, mc.cores = 1, ... )
x 
The gene expression data to be analyzed. This can be either (1) a
list of two matrices or data frames that contain the gene expression profile
from each of two populations (groups) – with rows corresponding to samples
and columns to genes – or (2) a single matrix or data frame
that contains the expression profiles for both groups. For case (2), the

pathway_list 
A single vector or list of vectors containing gene names
to indicate pathway membership. The vectors are used to subset the columns
of the matrices in 
group_labels 
If 
network_inference 
A function used to infer the pathway network. It
should take in an n by p matrix and return a p by p matrix of association
scores. (Builtin options include: 
n_perm 
The number of random permutations to perform during
permutation testing. If 
lp 
The lp value used to compute differential connectivity
scores. (Note: If a vector is provided, then the results are returned as
a list of 
seed 
(Optional) Used to set.seed prior to permutation test for each pathway. This allows results for individual pathways to be easily reproduced. 
verbose 
Set to TRUE to turn on messages. 
mc.cores 
Used in 
... 
Additional arguments are passed into the network inference function. 
A 'dnapath_list' or 'dnapath' object containing results for each
pathway in pathway_list
.
grimes19dnapath
filter_pathways
, summary.dnapath_list
subset.dnapath_list
, sort.dnapath_list
,
plot.dnapath
, rename_genes
data(meso) data(p53_pathways) set.seed(0) results < dnapath(x = meso$gene_expression, pathway_list = p53_pathways, group_labels = meso$groups, n_perm = 10) results summary(results) # Summary over all pathways in the pathway list. # Remove results for pathways with pvalues above 0.2. top_results < filter_pathways(results, 0.2) # Sort the top results by the pathway DC score. top_results < sort(top_results, by = "dc_score") top_results summary(top_results[[1]]) # Summary of pathway 1. plot(results[[1]]) # Plot of the differential network for pathway 1. # Use ... to adjust arguments in the network inference function. # For example, using run_corr() with method = "spearman": results < dnapath(x = meso$gene_expression, pathway_list = p53_pathways, group_labels = meso$groups, n_perm = 10, network_inference = run_corr, method = "spearman") results
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