dcGOR (version 1.0.6)

visEnrichment: Function to visualise enrichment analysis outputs in the context of the ontology hierarchy

Description

visEnrichment is supposed to visualise enrichment analysis outputs (represented as an 'Eoutput' object) in the context of the ontology hierarchy (direct acyclic graph; DAG). Only part of DAG induced by those nodes/terms specified in query nodes (and the mode defining the paths to the root of DAG) will be visualised. Nodes in query are framed in black (by default), and all nodes (in query plus induced) will be color-coded according to a given data.type ('zscore'; otherwise taking the form of 10-based negative logarithm for 'adjp' or 'pvalue'). If no nodes in query, the top 5 significant terms (in terms of adjusted p-value) will be used for visualisation

Usage

visEnrichment(e, nodes_query = NULL, num_top_nodes = 5,
path.mode = c("all_shortest_paths", "shortest_paths", "all_paths"),
data.type = c("adjp", "pvalue", "zscore"), height = 7, width = 7,
margin = rep(0.1, 4), colormap = c("yr", "bwr", "jet", "gbr", "wyr",
"br",
"rainbow", "wb", "lightyellow-orange"), ncolors = 40, zlim = NULL,
colorbar = T, colorbar.fraction = 0.1, newpage = T,
layout.orientation = c("left_right", "top_bottom", "bottom_top",
"right_left"), node.info = c("both", "none", "term_id", "term_name",
"full_term_name"), graph.node.attrs = NULL, graph.edge.attrs = NULL,
node.attrs = NULL)

Arguments

e

an object of S4 class Eoutput

nodes_query

a verctor containing a list of nodes/terms in query. These nodes are used to produce a subgraph of the ontology DAG induced by them. If NULL, the top significant terms (in terms of p-value) will be determined by the next 'num_top_nodes'

num_top_nodes

a numeric value specifying the number of the top significant terms (in terms of p-value) will be used. This parameter does not work if the previous 'nodes_query' has been specified

path.mode

the mode of paths induced by nodes in query. 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 root (i.e. for each node, find all shortest paths with the equal lengths)

data.type

a character telling which data type for nodes in query is used to color-code nodes. It can be one of 'adjp' for adjusted p-values (by default), 'pvalue' for p-values and 'zscore' for z-scores. When 'adjp' or 'pvalue' is used, 10-based negative logarithm is taken. For the style of how to color-code, please see the next arguments: colormap, ncolors, zlim and colorbar

height

a numeric value specifying the height of device

width

a numeric value specifying the width of device

margin

margins as units of length 4 or 1

colormap

short name for the colormap. It can be one of "yr" (yellow-red colormap; by default), "jet" (jet colormap), "bwr" (blue-white-red colormap), "gbr" (green-black-red colormap), "wyr" (white-yellow-red colormap), "br" (black-red colormap), "wb" (white-black colormap), and "rainbow" (rainbow colormap, that is, red-yellow-green-cyan-blue-magenta). Alternatively, any hyphen-separated HTML color names, e.g. "lightyellow-orange" (by default), "blue-black-yellow", "royalblue-white-sandybrown", "darkgreen-white-darkviolet". A list of standard color names can be found in http://html-color-codes.info/color-names

ncolors

the number of colors specified over the colormap

zlim

the minimum and maximum z/data values for which colors should be plotted, defaulting to the range of the finite values of z. Each of the given colors will be used to color an equispaced interval of this range. The midpoints of the intervals cover the range, so that values just outside the range will be plotted

colorbar

logical to indicate whether to append a colorbar. If data is null, it always sets to false

colorbar.fraction

the relative fraction of colorbar block against the device size

newpage

logical to indicate whether to open a new page. By default, it sets to true for opening a new page

layout.orientation

the orientation of the DAG layout. It can be one of "left_right" for the left-right layout (viewed from the DAG root point; by default), "top_bottom" for the top-bottom layout, "bottom_top" for the bottom-top layout, and "right_left" for the right-left layout

node.info

tells the ontology term information used to label nodes. It can be one of "both" for using both of Term ID and Name (the first 15 characters; by default), "none" for no node labeling, "term_id" for using Term ID, "term_name" for using Term Name (the first 15 characters), and "full_term_name" for using the full Term Name

graph.node.attrs

a list of global node attributes. These node attributes will be changed globally. See 'Note' below for details on the attributes

graph.edge.attrs

a list of global edge attributes. These edge attributes will be changed globally. See 'Note' below for details on the attributes

node.attrs

a list of local edge attributes. These node attributes will be changed locally; as such, for each attribute, the input value must be a named vector (i.e. using Term ID as names). See 'Note' below for details on the attributes

Value

An object of class 'Ragraph'

See Also

dcEnrichment, dcRDataLoader, dcConverter

Examples

## Not run: 
# # 1) load SCOP.sf (as 'InfoDataFrame' object)
# SCOP.sf <- dcRDataLoader('SCOP.sf')
# # randomly select 20 domains
# data <- sample(rowNames(SCOP.sf), 20)
# 
# # 2) perform enrichment analysis, producing an object of S4 class 'Eoutput'
# eoutput <- dcEnrichment(data, domain="SCOP.sf", ontology="GOMF")
# eoutput
# 
# # 3) visualise the top 10 significant terms
# # color-coded according to 10-based negative logarithm of p-values
# visEnrichment(eoutput)
# # color-coded according to zscore
# visEnrichment(eoutput, data.type='zscore')
# 
# # 4) visualise the top 5 significant terms in the ontology hierarchy
# nodes_query <- names(sort(adjp(eoutput))[1:5])
# visEnrichment(eoutput, nodes_query=nodes_query)
# # change the frame color: highlight (framed in blue) nodes/terms in query
# nodes.highlight <- rep("blue", length(nodes_query))
# names(nodes.highlight) <- nodes_query
# visEnrichment(eoutput, nodes_query=nodes_query,
# node.attrs=list(color=nodes.highlight))
# ## End(Not run)