computeSubnetwork: Compute a subnetwork
In kwanjeeraw/metabox: metabox

Description Usage Arguments Details Value Author(s) References See Also Examples

compute a subnetwork from p-values of the nodes of a network.

1	computeSubnetwork(edgelist, nodelist, pval, fc, fdr, pcol, internalid, method, hasatt, returnas)

`edgelist`	a data frame of edges contains at least a source column (1st column) and a target column (2nd column).
`nodelist`	a data frame of nodes contains node attributes e.g. node id, node name, node xref. Default is NULL.
`pval`	a numeric vector or a two-column data frame of statistical values e.g. p-values. If `pval` is a vector, the name attributes must be identical to the names of network nodes. If `pval` is a data frame, 1st column contains the network nodes and 2nd column contains statistical values.
`fc`	a numeric vector of fold changes with name attributes identical to the names of network nodes. Default is NULL.
`fdr`	a numeric value specifying false discovery rate. Default is 0.05.
`pcol`	a string specifying columnname containing p-values. This parameter is only for accepting the input from GUI.
`internalid`	a logical value indicating whether the network nodes are neo4j ids, if TRUE (default). It has no effect if there is no database installed.
`method`	a string specifying the method used to compute the subnetwork. Default is bionet.
`hasatt`	a logical value indicating whether node attributes are kept already. Used by GUI.
`returnas`	a string specifying output type. It can be one of dataframe, list, json. Default is dataframe.

For the method bionet, the function wraps around the main steps of BioNet including fitBumModel, scoreNodes, runFastHeinz to compute a subnetwork.

list of network information with the following components:

nodes:

nodeAttributes = node attributes provided

score = node score if the method is bionet: positive values = signal content and negative values = background noise

edges:

source, target

edgeAttributes = edge attributes provided

Return empty list if error or found nothing.

Kwanjeera W kwanich@ucdavis.edu

Beisser D., Klau GW., Dandekar T., Müller T. and Dittrich MT. (2010) BioNet: an R-Package for the functional analysis of biological networks. Bioinformatics, 26(8):1129-30

Dittrich MT., Klau GW., Rosenwald A., Dandekar T., Müller T. (2008) Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics, 24(13):i223-31

fitBumModel, scoreNodes, runFastHeinz

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#simnw <- computeSimilarity(c(1110,10413,196,51,311,43,764,790)) #compute similarity network for given pubchem compounds
#pval <- data.frame(pubchem=c(1110,10413,196,51,311,43,764,790), stat=runif(8, 0, 0.06)) #statistical values of pubchem compounds
#result <- computeSubnetwork(simnw$edges, simnw$nodes, pval=pval, internalid = F)

kwanjeeraw/metabox documentation built on May 20, 2019, 7:07 p.m.