diffuse {perturbatr} | R Documentation |
Propagate the estimated gene effects from a previous analysis over a network using network diffusion. First the estimated effects are normalized and mapped to a given genetic network, for instance a PPI or co-expression network. Then the normalized effects are propagated across the edges of the network using a Markov random walk with restarts. By that the initial ranking of genes (as given by their absolute effect sizes) is re-evaluated and the genes are reordered. Thus network diffusion potentially reduced false negative hits.
diffuse(obj, graph = NULL, r = 0.5, delete.nodes.on.degree = 0, do.bootstrap = FALSE, take.largest.component = TRUE, correct.for.hubs = TRUE) ## S4 method for signature 'HMAnalysedPerturbationData' diffuse(obj, graph = NULL, r = 0.5, delete.nodes.on.degree = 0, do.bootstrap = FALSE, take.largest.component = TRUE, correct.for.hubs = TRUE)
obj |
|
graph |
a |
r |
restart probability of the random walk |
delete.nodes.on.degree |
delete nodes from the graph with a degree of
less or equal than |
do.bootstrap |
run a diffusion on every bootstrap sample in case bootstrap samples are available |
take.largest.component |
if |
correct.for.hubs |
if true corrects for the fact that the stationary distribution of the random walk is biased towards hubs. |
returns a NetworkAnalysedPerturbationData
object
data(rnaiscreen) hm.fit <- hm(rnaiscreen) graph <- readRDS(system.file( "extdata", "graph_small.rds", package = "perturbatr")) res <- diffuse(hm.fit, graph=graph, r=1)