View source: R/decoupleRnival.R
reduce_solution_network | R Documentation |
Reduces a solution network based on a decoupling analysis of upstream and downstream gene expression, by filtering out edges that do not meet a consistency threshold, and limiting the network to a certain number of steps from upstream input nodes.
reduce_solution_network(
decoupleRnival_res,
meta_network,
cutoff,
upstream_input,
RNA_input = NULL,
n_steps = 10
)
decoupleRnival_res |
A data frame resulting from the decoupleRnival function. |
meta_network |
A network data frame containing signed directed prior knowledge of molecular interactions. |
cutoff |
The consistency threshold for filtering edges from the solution network. |
upstream_input |
A named vector with up_stream nodes and their corresponding activity. |
RNA_input |
A named vector containing differential gene expression data. |
n_steps |
The maximum number of steps from upstream input nodes to include in the solution network. |
A list containing the solution network (SIF) and an attribute table (ATT) with gene expression data.
# Example input data
upstream_input <- c("A" = 1, "B" = -1, "C" = 0.5)
downstream_input <- c("D" = 2, "E" = -1.5)
meta_network <- data.frame(
source = c("A", "A", "B", "C", "C", "D", "E"),
target = c("B", "D", "D", "E", "D", "B", "A"),
interaction = c(-1, 1, -1, 1, -1, -1, 1)
)
RNA_input <- c("A" = 1, "B" = -1, "C" = 5, "D" = 0.7, "E" = -0.3)
# Run the decoupleRnival function to get the upstream influence scores
upstream_scores <- decoupleRnival(upstream_input, downstream_input, meta_network, n_layers = 2, n_perm = 100)
# Reduce the solution network based on the upstream influence scores
reduced_network <- reduce_solution_network(upstream_scores, meta_network, 0.4, upstream_input, RNA_input, 3)
# View the resulting solution network and attribute table
print(reduced_network$SIF)
print(reduced_network$ATT)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.