Iteratively propagate downstream input activity through a signed directed network using the weighted mean enrichment score from decoupleR package
Usage
moon(
upstream_input = NULL,
downstream_input,
meta_network,
n_layers,
n_perm = 1000,
downstream_cutoff = 0,
statistic = "ulm",
return_levels = F
)Arguments
- upstream_input
A named vector with up_stream nodes and their corresponding activity.
- downstream_input
A named vector with down_stream nodes and their corresponding activity.
- meta_network
A network data frame containing signed directed prior knowledge of molecular interactions.
- n_layers
The number of layers that will be propagated upstream.
- n_perm
The number of permutations to use in decoupleR's algorithm.
- downstream_cutoff
If downstream measurments should be included above a given threshold
- statistic
the decoupleR stat to consider: "wmean", "norm_wmean", or "ulm"
- return_levels
true or false, if true the layers that the protein belongs to will be returned alongside the scores
Value
A data frame containing the score of the nodes upstream of the downstream input based on the iterative propagation
Examples
# 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", "C", "D", "E", "D", "B", "A"),
sign = c(1, -1, -1, 1, -1, -1, 1)
)
# Run the function with the example input data
result <- moon(upstream_input, downstream_input, meta_network, n_layers = 2, statistic = "wmean")
#> [1] 2
# View the results
print(result)
#> source score level
#> 1 B -2.0 1
#> D D 2.0 0
#> E E -1.5 0