localization_score: Ranks features by how localized they are in graph

In CamaraLab/AdjacencyScore: Computes a bivariate metric to rank pairs of features by how colocalized they are in a graph

Description

Given the adjacency matrix of a graph and a set of features on that graph, ranks those features (f) by the equation f((e^kA-I)/k)f, which measures how much those features are localized in the graph. Calculates the p-value of this score by permuting the columns of the feature matrix.

Usage

localization_score(adj_matrix, f, num_perms = 1000, num_cores = 1,
  perm_estimate = F)

Arguments

adj_matrix	a (preferrably sparse) binary matrix of adjacency between the columns of f
f	a numeric vector or matrix specifying one or more functions with support on the set of points whose significance will be assesed in the simplicial complex. Each column corresponds to a point and each row specifies a different function.
num_perms	number of permutations used to build the null distribution for each feature. By default is set to 1000.
num_cores	integer specifying the number of cores to be used in the computation. By default only one core is used.
perm_estimate	boolean indicating whether normal distribution parameters should be determined from num_perms permutations to estimate the p-value. By default is set to FALSE.

CamaraLab/AdjacencyScore documentation built on May 23, 2021, 12:57 p.m.