TDARACNE: TDARACNE
In TDARACNE: Network reverse engineering from time course data.

Description Usage Arguments Examples

Main function, see P. Zoppoli, S. Morganella, M. Ceccarelli. TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach. BMC Bioinformatics 2010, 11:154.

1	TDARACNE(eSet,N,delta=3,likehood=1.2,norm=2,logarithm=1,thresh=0,ksd=1,tolerance=0.15,plot=FALSE,dot=FALSE,name="youHaveForgottenIt",adj=FALSE)

`eSet`	eSet is the ExpressionSet object
`N`	N is respectively the number of bins in percentile normalization or in rank normalization
`delta`	delta is the maximum time delay allowed to infer connections
`likehood`	likehood is the fold change used as threshold to state the initial change expression (IcE)
`norm`	if you want column percentile normalization put norm == 1; if you want Rank normalization put norm == 2;
`logarithm`	if z is log put logarithm == 0;
`thresh`	the Influence threshold. if you have a threshold and a SD put them here in this format: c(thresh,SD) if you don't have threshold put 0 in thresh;
`ksd`	ksd is the standard deviation multiplier;
`tolerance`	tolerance is the DPI tolerance; 0 means no tolerance 1 means no DPI 0.15 is the default ARACNE tolerance as it is for TDARACNE
`plot`	plot must be TRUE to obtain automatically the graph
`dot`	dot must be TRUE to obtain a .dot file
`name`	name must be written with quotation marks(like this:'examplename') and is the name of the .dot file produced;
`adj`	adj must be TRUE to obtain an adjacent matrix

	## take paper data
	library(TDARACNE)
	data(dataIRMAon)
	data(threshIRMAon)
	## main function; in output gives to you and adj matrix and a .dot file
# eSet is the ExpressionSet object
# N is respectively the number of bins in percentile normalization or in rank normalization
# delta is the maximum time delay allowed to infer connections
# likehood is the fold change used as threshold to state the initial change expression (IcE) 
# if you want column percentile normalization put norm == 1;
# if you want Rank normalization put norm == 2;
# if z is log put logarithm == 0;
# if you don't have threshold put 0 in thresh;
# ksd is the standard deviation multiplier;
# tolerance is the DPI tolerance;
# plot must be TRUE to obtain automatically the graph
# dot must be TRUE to obtain a .dot file 
# name must be written with quotation marks(like this:'examplename') and is the name of the .dot file produced;
# adj must be TRUE to obtain an adjacent matrix

TDARACNE(dataIRMAon,11,"netIRMAon",delta=3,likehood=1.2,norm=2,logarithm=1,thresh=threshIRMAon,ksd=0,tolerance=0.15);