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Differential connectivity nulls via permutation/bootstrap + GGM

Source: R/null_ggm.R
null_ggm.Rd

Generates a collection of null networks by repeatedly shuffling the data (via permutation or bootstrap) and fitting a sparse Gaussian graphical model (SILGGM) within each label-defined group. Each iteration returns a named list of two igraph objects (one per group). Edge attributes include:

  • qval — BH-adjusted p-values

  • pval — p-values from precision-matrix inference

  • pcor — partial correlations

  • sign — sign of partial correlation (+1 / -1)

Usage

null_ggm(
  dat,
  group_col = "phenotype",
  inference_method = c("D-S_NW_SL", "B_NW_SL"),
  shuffle_method = c("permutation", "bootstrap"),
  shuffle_iter = 100,
  balanced = FALSE,
  filter = c("pval", "fdr", "none"),
  threshold = 0.05,
  n_cores = NULL,
  seed = NULL
)

Arguments

dat

data.frame; p numeric feature columns + one label column.

group_col

character; name of the label column (default "phenotype").

inference_method

one of c("D-S_NW_SL", "B_NW_SL").

shuffle_method

character; one of c("permutation","bootstrap").

shuffle_iter

integer; number of null resamples (default 100).

balanced

logical; only used when shuffle_method = "permutation". If TRUE, downsample to equal group sizes before permutation.

filter

character; one of c("pval","fdr","none").

threshold

numeric; threshold for edge inclusion when filter != "none".

n_cores

integer; number of cores parallel computing

seed

integer; random seet

Value

A list of length shuffle_iter; each element is a named list (by group labels) of igraph objects