Description Usage Arguments Details Value Author(s) References Examples
The function builds a graph for the inferred PAN so that it can be visualize
in igraph
or RedeR
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object |
an object of S4 class |
engine |
a character value specifying which graphics engine to use: ‘igraph’ or ‘RedeR’. |
para |
a list of parameters specifying graph attributes (see details) |
verbose |
a logical value to switch on (if |
... |
all the other arguments accepted by the function |
Here are the introductions for the detailed graph attributes that the user can specify:
'nodeColor' and 'nodeSize' - a vector of node colors or sizes. Please note
that the order of color or size must be concordant with the gene ids in
PANR:the argument pheno
of the object of BetaMixture
.
'nodeSumCols' and 'nodeSumMethod' - these two arguments are used to scale
the colors of nodes by phenotypes. The former argument is a numeric vector
specifying the columns in slot pheno
of the object of class BetaMixture
;
while the latter one is a character value giving the method to summarize
these columns of phenotypes: either 'mean' or 'median'.
'edgeColor' and 'edgeWidth' - a vector of edge colors or width.
'pValCutoff' - the argument is only used when what='module'
and
for module searching based on pvclust
. Only
significant modules will be displayed.
'minSize' and 'maxSize' - two arguments controlling the size of modules which will be used to filtered out modules that are too small or too large.
'hideNeg' - a logical value specifying whether or not to hide edges with negative associations
This function will return an object of class PAN
with inferred
gene modules (modules$clusters
) and corresponding p-values (modules$pval
)
updated in slot 'modules'.
Xin Wang xw264@cam.ac.uk
Xin Wang, Mauro Castro, Klaas W. Mulder and Florian Markowetz, Posterior association networks and enriched functional gene modules inferred from rich phenotypic perturbation screens, in preparation.
R. Suzuki and H. Shimodaira. Pvclust: an r package for assessing the uncertainty in hierarchical clustering. Bioinformatics, 22(12):1540, 2006.
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