View source: R/adjacency_score2.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 for this score by permuting the columns of the feature matrix separately for each feature.
1 2 | adjacency_score2(adj_matrix, f, f_pairs, c, num_perms = 1000, num_cores = 1,
perm_estimate = F, groupings = F, verbose = T)
|
adj_matrix |
a (preferrably sparse) binary matrix of adjacency between the columns of f |
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. |
f_pairs |
a 2 column matrix where each row specifies the indices or names of a pair of features on which the score will be computed |
c |
constant used to determine width of diffusion, must be 0 <= c |
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. |
groupings |
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. |
verbose |
print time taken to create permutation matrices and compute adjacency score |
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