dnapath {dnapath} | R Documentation |
Differential Network Analysis Using Gene Pathways
Description
Integrates pathways into the differential network analysis of gene expression data (Grimes et al. 2019).
Usage
dnapath(
x,
pathway_list,
group_labels = NULL,
network_inference = run_pcor,
n_perm = 100,
lp = 2,
seed = NULL,
verbose = FALSE,
mc.cores = 1,
...
)
Arguments
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. (Built-in 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. |
Value
A 'dnapath_list' or 'dnapath' object containing results for each
pathway in pathway_list
.
References
Grimes T, Potter SS, Datta S (2019). “Integrating Gene Regulatory Pathways into Differential Network Analysis of Gene Expression Data.” Scientific reports, 9(1), 5479.
See Also
filter_pathways
, summary.dnapath_list
subset.dnapath_list
, sort.dnapath_list
,
plot.dnapath
, rename_genes
Examples
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 p-values 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