domonster: MONSTER quick-start with pre-made regulatory networks

View source: R/MONSTER.R

domonsterR Documentation

MONSTER quick-start with pre-made regulatory networks

Description

This function is a wrapper to simplify usage of the monster function in the case where the pair of regulatory networks to be contrasted have already been estimated, either as panda objects, or represented as an adjacency matrix with regulators in rows and genes in columns.

Usage

domonster(exp_graph, control_graph, nullPerms = 1000, numMaxCores = 3, ...)

Arguments

exp_graph

matrix or PANDA object (generated by netzooR::panda); if matrix, should be the adjacency matrix for the network with regulators in rows and genes in columns, both named. This should be the network for the experimental (case) group.

control_graph

matrix or PANDA object (generated by netzooR::panda); if matrix, should be the adjacency matrix for the network with regulators in rows and genes in columns, both named. This should be the network for the control (reference) group.

nullPerms

numeric; defaults to 1000. Number of null permutations to perform. See monster for more details.

numMaxCores

numeric; defaults to 3. Maximum number of cores to use; will be the minimum of this number and the actual available cores. See monster for more details.

...

other arguments for monster may be passed.

Value

monster object

Examples




# Generating PANDA networks for demonstration:
# For the purposes of this example, first partition the pandaToyData samples, then perform panda:
pandaResult_exp <- panda(pandaToyData$motif, pandaToyData$expression[,1:25], pandaToyData$ppi)
pandaResult_control <- panda(pandaToyData$motif, pandaToyData$expression[,26:50], pandaToyData$ppi)

# function takes both panda objects and matrices, or a mixture
monster_res1 <- domonster(pandaResult_exp, pandaResult_control)
monster_res2 <- domonster(pandaResult_exp@regNet, pandaResult_control@regNet)
monster_res3 <- domonster(pandaResult_exp@regNet, pandaResult_control)


netZoo/netZooR documentation built on Sept. 23, 2024, 2:07 p.m.