dnet (version 1.0.0)

dDAGannotate: Function to generate a subgraph of a direct acyclic graph (DAG) induced by the input annotation data

Description

dDAGannotate is supposed to produce a subgraph induced by the input annotation data, given a direct acyclic graph (DAG; an ontology). The input is a graph of "igraph" or "graphNET" object, a list of the vertices containing annotation data, and the mode defining the paths to the root of DAG. The induced subgraph contains vertices (with annotation data) and their ancestors along with the defined paths to the root of DAG. The annotations at these vertices (including their ancestors) are also updated according to the true-path rule: a gene annotated to a term should also be annotated by its all ancestor terms.

Usage

dDAGannotate(g, annotations, path.mode = c("all_paths",
"shortest_paths",
"all_shortest_paths"), verbose = TRUE)

Arguments

g

an object of class "igraph" or "graphNEL"

annotations

the vertices/nodes for which annotation data are provided

path.mode

the mode of paths induced by vertices/nodes with input annotation data. It can be "all_paths" for all possible paths to the root, "shortest_paths" for only one path to the root (for each node in query), "all_shortest_paths" for all shortest paths to the r

verbose

logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display

Value

• • subg: an induced subgraph, an object of class "igraph". In addition to the original attributes to nodes and edges, the return subgraph is also appended a new node attribute called "annotations", which contains a list of genes either as original annotations or inherited annotations

See Also

dDAGinduce, dDAGlevel

Examples

# 1) load GO Molelular Function as igraph object
load(url("http://dnet.r-forge.r-project.org/data/Obo/ig.GOMF.RData"))
g <- ig.GOMF

# 2) load human genes annotated by GO Molelular Function terms
load(url("http://dnet.r-forge.r-project.org/data/Hs/org.Hs.egGOMF.RData"))
GS <- org.Hs.egGOMF # as 'GS' object

# 3) prepare for annotation data
# randomly select vertices with annotation data
annotations <- GS$gs[sample(1:length(GS$gs),5)]

# 4) obtain the induced subgraph
# 4a) based on all possible paths (i.e. the complete subgraph induced)
dDAGannotate(g, annotations, path.mode="all_paths", verbose=TRUE)
# 4b) based on shortest paths (i.e. the most concise subgraph induced)
dag <- dDAGannotate(g, annotations, path.mode="shortest_paths",
verbose=TRUE)

# 5) color-code nodes/terms according to the number of annotations
data <- sapply(V(dag)$annotations, length)
names(data) <- V(dag)$name
visDAG(g=dag, data=data, node.info="both")