bootstrap_null {crosstalkr}

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)