compute_null_dnp calculates a null distribution for the change in network potential for
for each node in a cell signaling network.
compute_null_dnp(
cache = NULL,
df,
ppi = "biogrid",
n,
n_genes = 50,
experiment_name,
ncores = 4,
min_score = NULL
)df, also saves to cache if specified
user-provided filepath for where to store data etc
output of compute_dnp()
should we use biogrid or stringdb for the PPI
number of permutations
integer describing number of genes per sample that we will compute the null distribution for
name of the experiment for saving output.
number of cores to use for calculations
if ppi is stringdb, which mininum score should we use to filter edges?
The input for this function will be the output of compute_dnp().
To compute the null distribution, the nodes in the provided cell signaling
network will be randomly permuted n times, with dnp computed or each new
cell signaling network. The mean and standard error of dnp for this set of random
networks will constitute the null model that we will use for comparison.
Be warned that this operation is extremely expensive computationally. It is
recommended to either use a high-performance cluster or limit the computation of the
null distribution to a small number of nodes.
To distribute the workload over multiple cores, just specify ncores.
compute_dnp() and compute_np()