adjacency_score: Ranks pairs of features by how localized they are in graph

Description Usage Arguments

View source: R/adjacency_score.R


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


adjacency_score(adj_matrix, f, f_pairs, c, num_perms = 1000, num_cores = 1,
  perm_estimate = F, groupings = F, verbose = T)



a (preferrably sparse) binary matrix of adjacency between the columns of f


a numeric matrix specifying one or more features defined for each node of the graph. Each column is a node of the graph and each row is a feature over the nodes.


a 2 column matrix where each row specifies the indices or names of a pair of features on which the score will be computed


constant used to determine width of diffusion, must be 0 <= c


number of permutations used to build the null distribution for each feature. By default is set to 1000.


integer specifying the number of cores to be used in the computation. By default only one core is used.


boolean indicating whether normal distribution parameters should be determined from num_perms permutations to estimate the p-value. By default is set to FALSE.


boolean indicating whether features are binary and mutually exclusive indicated each point's inclusion in some group. Allows for p-value computation from a parameterized hypergeometric null distribution. By default is set to FALSE.


print time taken to create permutation matrices and compute adjacency score

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