bootstrap_null: Bootstrap null distribution for RWR

In DavisWeaver/crosstalkr: Analysis of Graph-Structured Data with a Focus on Protein-Protein Interaction Networks

Bootstrap null distribution for RWR

Description

This function will generate a bootstrapped null distribution to identify signficant vertices in a PPI given a set of user-defined seed proteins. Bootstrapping is done by performing random walk with repeats repeatedly over "random" sets of seed proteins. Degree distribution of user-provided seeds is used to inform sampling.

Usage

bootstrap_null(
  seed_proteins,
  g,
  n = 1000,
  agg_int = 100,
  gamma = 0.6,
  eps = 1e-10,
  tmax = 1000,
  norm = TRUE,
  set_seed = NULL,
  cache = NULL,
  seed_name = NULL,
  ncores = 1
)

Arguments

seed_proteins	user defined seed proteins
g	igraph object
n	number of random walks with repeats to create null distribution
agg_int	number of runs before we need to aggregate the results - necessary to save memory. set at lower numbers to save even more memory.
gamma	restart probability
eps	maximum allowed difference between the computed probabilities at the steady state
tmax	the maximum number of iterations for the RWR
norm	if True, w is normalized by dividing each value by the column sum.
set_seed	integer to set random number seed - for reproducibility
cache	A filepath to a folder downloaded files should be stored
seed_name	Name to give the cached ngull distribution - must be a character string
ncores	Number of cores to use - defaults to 1. Significant speedup can be achieved by using multiple cores for computation.

Value

data frame containing mean/ standard deviation for null distribution

Examples

#g <- prep_biogrid()
#bootstrap_null(seed_proteins = c("EGFR", "KRAS"), g= g, ncores = 1, n = 10)

DavisWeaver/crosstalkr documentation built on May 6, 2024, 6:09 p.m.