relNet2TGF {maigesPack} | R Documentation |
This function receive an object of class maigesRelNetB
or maigesRelNetM
and write TGF files with the relevance
networks produced.
relNet2TGF(...) ## S3 method for class 'maigesRelNetB': relNet2TGF(data, dir = "./", filename="group.tgf", corC=NULL, pValue=0.05, ...) ## S3 method for class 'maigesRelNetM': relNet2TGF(data, dir = "./", filenames=c("group1.tgf", "group2.tgf", "difPvalue.tgf"), pValue=0.05, ...)
data |
object of class maigesRelNetB or maigesRelNetM . |
dir |
character string specifying the folder to save the TGF files. |
filename |
character string specifying the file name, for objects
of class maigesRelNetB . |
filenames |
character vector of length 3 with the file names to
be saved, for objects of class maigesRelNetM . |
corC |
numeric in [0,1] specifying the cutoff for selecting absolute correlation. May also be 'max' to select the maximum correlation values in a permutation bootstrap strategy, as proposed by Butte et al. (2000). |
pValue |
numeric in [0,1] specifying the cutoff for selecting correlation values by p-values. |
... |
additional parameters. |
This function only picks the result of the relNetworkB
or relNetworkM
and display write TGF files. This files
are interesting to be used with Yed graph visualisation and
editing tool, wrote in Java (http://www.yworks.com/en/products_yed_about.htm).
This function don't return any object.
Gustavo H. Esteves <gesteves@vision.ime.usp.br>
Butte, A.J.; Tamayo, P.; Slonim, D.; Golub, T.R. and Kohane, I.S. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks, PNAS, 97, 12182-12186, 2000 (http://www.pnas.org/cgi/content/full/97/22/12182)
relNetworkB
, relNetworkM
,
maigesRelNetB
, maigesRelNetM
.
## Loading the dataset data(gastro) ## Constructing the relevance network for sample ## 'Tissue' comparing 'Neso' and 'Aeso' for the 1st gene group ## The same is also true for objects of class maigesRelNetB gastro.net = relNetworkM(gastro.summ, sLabelID="Tissue", samples = list(Neso="Neso", Aeso="Aeso"), geneGrp=11, type="Rpearson") relNet2TGF(gastro.net)