R: Compute the heatrank using permutations

diffuse_mc {diffuStats}

R Documentation

Compute the heatrank using permutations

Description

Function diffuse_mc has an implemented parallelisation of the Monte Carlo trials for diffusion in a network. The input scores are assumed to be sparse and are internally sparsified, so very dense scores migth take time with current implementation.

Usage

diffuse_mc(graph, scores, n.perm = 10000, sample.prob = NULL, seed = 1,
    oneminusHeatRank = TRUE, K = NULL, ...)

Arguments

`graph`	igraph object
`scores`	Recursive list, can have either binary or quantitative scores
`n.perm`	Numeric, number of permutations
`sample.prob`	Numeric, probabilities (needn't be scaled) to permute the input. This is passed to `sample`'s `prob` argument. If `NULL`, sampling is uniform. It has to be in a list format, with the same names as `scores`, and each element of the list must be the sampling probability of each background.
`seed`	Numeric, seed for random number generator
`oneminusHeatRank`	Logical, should `1 - heatrank` be returned instead of `heatrank`?
`K`	Kernel matrix (if precomputed). If `K` is not supplied, the regularised Laplacian will be computed on the fly and used.
`...`	currently ignored arguments

Value

A list containing matrices of heatrank scores

Examples

# Using a list as input (needed)
data(graph_toy)
list_input <- list(myInput1 = graph_toy$input_mat)
diff_mc <- diffuse_mc(
    graph = graph_toy,
    scores = list_input)

[Package diffuStats version 1.6.0 Index]