randomizeCNOlist: add noise to the data contained in a CNOlist.

In cokelaer/CellNOptR: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data

Description

This function takes in a CNOlist and does the normalisation of the data between 0 and 1, according to two different procedures (see details).

Usage

randomizeCNOlist(cnolist, sd=0.1, minValue=0, maxValue=1, mode="gaussian")

Arguments

cnolist	a CNOlist
sd	standard deviation to be used when adding gaussian noise. Not used if mode is uniform.
minValue	When adding Gaussian noise, the result may be below the minValue(default 0). If so, the value is set to minValue.
maxValue	When adding Gaussian noise, the result may be above the maxValue(default 1). If so, the value is set to maxValue.
mode	The mode can be either 'gaussian', 'shuffle' or uniform'. In gaussian mode, a gaussian noise is added to the data. The mean parameter is the data and the standard deviation is defined by the sd parameter. In uniform mode, the data is simply replaced by values taken from a uniform distribution between 0 and 1. In 'shuffle' mode all rows and columns are shuffled.

Value

a noisy CNOlist

Author(s)

T. Cokelaer

Examples

data(CNOlistToyMMB, package="CellNOptR")
cnolist = CNOlistToyMMB
cnolist2 = randomizeCNOlist(cnolist, mode="uniform")

# a method called randomize is available in the CNOlist class so you could type:
cnolist2 = randomize(cnolist, mode="uniform")

cokelaer/CellNOptR documentation built on May 27, 2019, 8:44 a.m.