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mND global function

framework_mND.Rd

It calculates permutations of X0 (if r parameter is used), applies network-diffusion on data, computes the mND score and the relative empirical p-value (if data are permuted). Outputs of function can be used to classify genes in each layer with the classification function.

Usage

framework_mND(
  X0 = NULL,
  W = NULL,
  alpha = 0.7,
  nMax = 10000,
  eps = 1e-06,
  k = 3,
  r = 0,
  seed_n = NULL,
  BPPARAM = NULL,
  ...
)

Arguments

X0

matrix; each column (layer) of the matrix X0 is a score vector over all vertices of G.

W

matrix; symmetrically normalized adjacency matrix W = D^-1 A D^-1, see 'normalize_adj_mat' function

alpha

numeric; the smothing factor

nMax

numeric; maximum number of iterations

eps

numeric; the iteration will stop when the maximum difference between matrix Xs between two consecutive iteraction is smaller than eps

k

numeric; number of top k first neighbors

r

numeric; number of permutations

seed_n

numeric; a given 'seed_n' will determine the same permutations of X0 and ensures the reproducibility of the analysis

BPPARAM

optional BiocParallelParam instance determining the parallel back-end to be used during evaluation. If NULL, parallel evaluation is disabled using SerialParam(). See ?bplapply.

...

additional parameter to NPATools::perm_vertices()

Value

list with:

  • mND: mND score (mND); if r parameter was used, the following columns will be added: corrisponding empirical p-value (p), product of mND score and -log10(p) (mNDp)

  • t: sum of top k first neighbours

  • pl: corrisponding empirical p-values of (t), only if r parameter was used

  • tp: product of the sum of top k neighbours (t) and -log10(pl), only if r parameter was used