Nothing
R/xA2NetCode.r
In A2: Atlas and Analysis of Systems-Biology-Led Pathways
Defines functions
xA2NetCode
Documented in
xA2NetCode
#' Function to create codes annotating nodes in an igraph object
#'
#' \code{xA2NetCode} is supposed to create codes annotating nodes in an igraph object. It returns two ggplot2 objects, one for visualing the network with nodes lablelled by codes, the other for listing code meaning in a table
#'
#' @param g an object of class "igraph"
#' @param node.level a character specifying which node attribute defining the node level. By default, it is 'term_distance'
#' @param node.level.value a positive integer specifying the level value as major branches. By default, it is 2
#' @param node.label.size a character specifying which node attribute used for node label size
#' @param node.label.color a character specifying which node attribute used for the node label color
#' @param node.label.alpha the 0-1 value specifying transparency of node labelling
#' @param node.label.padding the padding around the labeled node
#' @param node.label.arrow the arrow pointing to the labeled node
#' @param node.label.force the repelling force between overlapping labels
#' @param node.shape an integer specifying node shape
#' @param node.xcoord a vector specifying x coordinates. If NULL, it will be created using igraph::layout_with_kk
#' @param node.ycoord a vector specifying y coordinates. If NULL, it will be created using igraph::layout_with_kk
#' @param node.color a character specifying which node attribute used for node coloring
#' @param node.color.title a character specifying the title for node coloring
#' @param colormap short name for the colormap. It can be one of "jet" (jet colormap), "bwr" (blue-white-red colormap), "gbr" (green-black-red colormap), "wyr" (white-yellow-red colormap), "br" (black-red colormap), "yr" (yellow-red colormap), "wb" (white-black colormap), "rainbow" (rainbow colormap, that is, red-yellow-green-cyan-blue-magenta), and "ggplot2" (emulating ggplot2 default color palette). 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 \url{http://html-color-codes.info/color-names}
#' @param ncolors the number of colors specified over the colormap
#' @param zlim the minimum and maximum values for which colors should be plotted
#' @param node.size.range the range of actual node size
#' @param title a character specifying the title for the plot
#' @param edge.size a numeric value specifying the edge size. By default, it is 0.5
#' @param edge.color a character specifying which edge attribute defining the the edge colors
#' @param edge.color.alpha the 0-1 value specifying transparency of edge colors
#' @param edge.curve a numeric value specifying the edge curve. 0 for the straight line
#' @param edge.arrow a numeric value specifying the edge arrow. By default, it is 2
#' @param edge.arrow.gap a gap between the arrow and the node
#' @param node.table a character specifying which node attribute for coding. By default, it is 'term_name'
#' @param node.table.wrap a positive integer specifying wrap width of coded node labelling
#' @param table.base.size a positive integer specifying font size in the table
#' @param table.row.space a positive numeric value specifying amplying horizental space for a row with wrapped text
#' @param table.nrow a positive integer specifying the number of rows in the table
#' @param table.ncol NULL or a positive integer specifying the number of columns per page. If NULL, it will be 3 or less
#' @param root.code a character specifying the root code. By default, it is 'RT'
#' @return
#' a list with 3 components, two ggplot objects (code and table) and an igraph object (ig appended with node attributes 'node.code' and 'node.table')
#' @note none
#' @export
#' @seealso \code{\link{xA2NetCode}}
#' @include xA2NetCode.r
#' @examples
#' # Load the library
#' library(A2)
#' RData.location <- "http://galahad.well.ox.ac.uk/bigdata_dev/"
#'
#' \dontrun{
#' # load REACTOME
#' # 1a) restricted to Immune System ('R-HSA-168256') or Signal Transduction ('R-HSA-162582')
#' g <- xRDataLoader(RData.customised='ig.REACTOME', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="R-HSA-168256", mode="out")
#' vids <- V(g)[unique(unlist(neighs.out))]$name
#' ig <- igraph::induced.subgraph(g, vids=vids)
#'
#' # 1b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=2, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#'
#' # 1c) visualise the graph with nodes coded and colored by information content (IC)
#' V(ig)$IC <- -1*log10(V(ig)$nAnno/max(V(ig)$nAnno))
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=2, node.shape=19, node.size.range=4, node.color='IC', node.color.title='IC', colormap='white-cyan-darkcyan')
#'
#' V(ig)$term_anno <- log10(V(ig)$nAnno)
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=2, node.shape=19, node.size.range=4, node.color='term_anno', node.color.title='# genes\n(log10)', colormap='white-cyan-darkcyan', zlim=c(1,4))
#'
#'
#' # load EF (annotating GWAS reported genes)
#' # 2a) restricted to disease ('EFO:0000408') and annotation (>=10)
#' # 2a) restricted to immune system disease ('EFO:0000540') and annotation (>=10)
#' g <- xRDataLoader(RData.customised='ig.EF', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="EFO:0000540", mode="out")
#' nodeClan <- V(g)[unique(unlist(neighs.out))]$name
#' anno <- xRDataLoader(RData.customised='org.Hs.egEF', RData.location=RData.location)
#' vec <- sapply(anno$gs, length)
#' nodeAnno <- names(vec[vec>=10])
#' neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno, mode="in")
#' nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
#' vids <- intersect(nodeClan, nodeAnno)
#' ig <- igraph::induced.subgraph(g, vids=vids)
#' V(ig)$anno <- anno$gs[V(ig)$name]
#' # 2b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=4, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#' # 2c) ## GWAS genes for immune system disease ('EFO:0000540')
#' anno <- xRDataLoader(RData.customised='org.Hs.egEF', RData.location=RData.location)
#' genes <- anno$gs[['EFO:0000540']]
#' # 2d) ## GWAS SNPs for immune system disease ('EFO:0000540')
#' annotation <- xRDataLoader(RData.customised='GWAS2EF', RData.location=RData.location)
#' dag <- xDAGpropagate(g, annotation, path.mode="all_paths", propagation="min")
#' snps <- unlist(V(dag)[V(dag)$name=='EFO:0000540']$anno)
#' # 2e) ## ChEMBL targets for immune system disease ('EFO:0000540')
#' annotation <- xRDataLoader(RData.customised='Target2EF', RData.location=RData.location)
#' dag <- xDAGpropagate(g, annotation, path.mode="all_paths", propagation="max")
#' targets <- unlist(V(dag)[V(dag)$name=='EFO:0000540']$anno)
#'
#'
#' # load GOBP
#' # 3a) restricted to immune system process ('GO:0002376') and annotation (>=10)
#' g <- xRDataLoader(RData.customised='ig.GOBP', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="GO:0002376", mode="out")
#' nodeClan <- V(g)[unique(unlist(neighs.out))]$name
#' anno <- xRDataLoader(RData.customised='org.Hs.egGOBP', RData.location=RData.location)
#' vec <- sapply(anno$gs, length)
#' nodeAnno <- names(vec[vec>=10])
#' neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno, mode="in")
#' nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
#' vids <- intersect(nodeClan, nodeAnno)
#' ig <- igraph::induced.subgraph(g, vids=vids)
#' V(ig)$anno <- anno$gs[V(ig)$name]
#' # 3b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=1, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#'
#'
#' # load GOMF
#' # 4a) restricted to molecular function ('GO:0003674') and annotation (>=50)
#' g <- xRDataLoader(RData.customised='ig.GOMF', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="GO:0003674", mode="out")
#' nodeClan <- V(g)[unique(unlist(neighs.out))]$name
#' anno <- xRDataLoader(RData.customised='org.Hs.egGOMF', RData.location=RData.location)
#' vec <- sapply(anno$gs, length)
#' nodeAnno <- names(vec[vec>=50])
#' neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno, mode="in")
#' nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
#' vids <- intersect(nodeClan, nodeAnno)
#' ig <- igraph::induced.subgraph(g, vids=vids)
#' V(ig)$anno <- anno$gs[V(ig)$name]
#' # 4b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=1, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#'
#'
#' # load HPPA
#' # 5a) restricted to Abnormality of the immune system ('HP:0002715') and annotation (>=50)
#' g <- xRDataLoader(RData.customised='ig.HPPA', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="HP:0002715", mode="out")
#' nodeClan <- V(g)[unique(unlist(neighs.out))]$name
#' anno <- xRDataLoader(RData.customised='org.Hs.egHPPA', RData.location=RData.location)
#' vec <- sapply(anno$gs, length)
#' nodeAnno <- names(vec[vec>=50])
#' neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno, mode="in")
#' nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
#' vids <- intersect(nodeClan, nodeAnno)
#' ig <- igraph::induced.subgraph(g, vids=vids)
#' V(ig)$anno <- anno$gs[V(ig)$name]
#' # 5b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=1, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#'
#'
#' # load DO
#' # 6a) restricted to immune system disease ('DOID:2914') and annotation (>=10)
#' g <- xRDataLoader(RData.customised='ig.DO', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="DOID:2914", mode="out")
#' nodeClan <- V(g)[unique(unlist(neighs.out))]$name
#' anno <- xRDataLoader(RData.customised='org.Hs.egDO', RData.location=RData.location)
#' vec <- sapply(anno$gs, length)
#' nodeAnno <- names(vec[vec>=10])
#' neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno, mode="in")
#' nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
#' vids <- intersect(nodeClan, nodeAnno)
#' ig <- igraph::induced.subgraph(g, vids=vids)
#' V(ig)$anno <- anno$gs[V(ig)$name]
#' # 6b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=2, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#'
#'
#' # load MP
#' # 7a) restricted to immune system phenotype ('MP:0005387') and annotation (>=50)
#' # 7a) restricted to abnormal immune system physiology ('MP:0001790') and annotation (>=50)
#' g <- xRDataLoader(RData.customised='ig.MP', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="MP:0001790", mode="out")
#' nodeClan <- V(g)[unique(unlist(neighs.out))]$name
#' anno <- xRDataLoader(RData.customised='org.Hs.egMP', RData.location=RData.location)
#' vec <- sapply(anno$gs, length)
#' nodeAnno <- names(vec[vec>=50])
#' neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno, mode="in")
#' nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
#' vids <- intersect(nodeClan, nodeAnno)
#' ig <- igraph::induced.subgraph(g, vids=vids)
#' V(ig)$anno <- anno$gs[V(ig)$name]
#' # 7b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=3, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#' }
xA2NetCode <- function(g, node.level='term_distance', node.level.value=2, node.label.size=2, node.label.color='darkblue', node.label.alpha=0.8, node.label.padding=0, node.label.arrow=0.01, node.label.force=0, node.shape=19, node.xcoord=NULL, node.ycoord=NULL, node.color=NULL, node.color.title=NULL, colormap='grey-grey', ncolors=64, zlim=NULL, node.size.range=4, title='', edge.size=0.5, edge.color="black", edge.color.alpha=0.4, edge.curve=0.1, edge.arrow=2, edge.arrow.gap=0.02, node.table='term_name', node.table.wrap=50, table.base.size=7, table.row.space=2, table.nrow=55, table.ncol=NULL, root.code='RT')
{
if(any(class(g) %in% c("igraph"))){
ig <- g
}else{
stop("The function must apply to a 'igraph' object.\n")
}
#################
ls_gp <- XGR::xOBOcode(g=ig, node.level=node.level, node.level.value=node.level.value, node.label.size=node.label.size, node.label.color=node.label.color, node.label.alpha=node.label.alpha, node.label.padding=node.label.padding, node.label.arrow=node.label.arrow, node.label.force=node.label.force, node.shape=node.shape, node.xcoord=node.xcoord, node.ycoord=node.ycoord, node.color=node.color, node.color.title=node.color.title, colormap=colormap, ncolors=ncolors, zlim=zlim, node.size.range=node.size.range, title=title, edge.size=edge.size, edge.color=edge.color, edge.color.alpha=edge.color.alpha, edge.curve=edge.curve, edge.arrow=edge.arrow, edge.arrow.gap=edge.arrow.gap, node.table=node.table, node.table.wrap=node.table.wrap, table.base.size=table.base.size, table.row.space=table.row.space, table.nrow=table.nrow, table.ncol=table.ncol, root.code=root.code)
#################
invisible(ls_gp)
}
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Defines functions xA2NetCode
Documented in xA2NetCode
#' Function to create codes annotating nodes in an igraph object
#'
#' \code{xA2NetCode} is supposed to create codes annotating nodes in an igraph object. It returns two ggplot2 objects, one for visualing the network with nodes lablelled by codes, the other for listing code meaning in a table
#'
#' @param g an object of class "igraph"
#' @param node.level a character specifying which node attribute defining the node level. By default, it is 'term_distance'
#' @param node.level.value a positive integer specifying the level value as major branches. By default, it is 2
#' @param node.label.size a character specifying which node attribute used for node label size
#' @param node.label.color a character specifying which node attribute used for the node label color
#' @param node.label.alpha the 0-1 value specifying transparency of node labelling
#' @param node.label.padding the padding around the labeled node
#' @param node.label.arrow the arrow pointing to the labeled node
#' @param node.label.force the repelling force between overlapping labels
#' @param node.shape an integer specifying node shape
#' @param node.xcoord a vector specifying x coordinates. If NULL, it will be created using igraph::layout_with_kk
#' @param node.ycoord a vector specifying y coordinates. If NULL, it will be created using igraph::layout_with_kk
#' @param node.color a character specifying which node attribute used for node coloring
#' @param node.color.title a character specifying the title for node coloring
#' @param colormap short name for the colormap. It can be one of "jet" (jet colormap), "bwr" (blue-white-red colormap), "gbr" (green-black-red colormap), "wyr" (white-yellow-red colormap), "br" (black-red colormap), "yr" (yellow-red colormap), "wb" (white-black colormap), "rainbow" (rainbow colormap, that is, red-yellow-green-cyan-blue-magenta), and "ggplot2" (emulating ggplot2 default color palette). 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 \url{http://html-color-codes.info/color-names}
#' @param ncolors the number of colors specified over the colormap
#' @param zlim the minimum and maximum values for which colors should be plotted
#' @param node.size.range the range of actual node size
#' @param title a character specifying the title for the plot
#' @param edge.size a numeric value specifying the edge size. By default, it is 0.5
#' @param edge.color a character specifying which edge attribute defining the the edge colors
#' @param edge.color.alpha the 0-1 value specifying transparency of edge colors
#' @param edge.curve a numeric value specifying the edge curve. 0 for the straight line
#' @param edge.arrow a numeric value specifying the edge arrow. By default, it is 2
#' @param edge.arrow.gap a gap between the arrow and the node
#' @param node.table a character specifying which node attribute for coding. By default, it is 'term_name'
#' @param node.table.wrap a positive integer specifying wrap width of coded node labelling
#' @param table.base.size a positive integer specifying font size in the table
#' @param table.row.space a positive numeric value specifying amplying horizental space for a row with wrapped text
#' @param table.nrow a positive integer specifying the number of rows in the table
#' @param table.ncol NULL or a positive integer specifying the number of columns per page. If NULL, it will be 3 or less
#' @param root.code a character specifying the root code. By default, it is 'RT'
#' @return
#' a list with 3 components, two ggplot objects (code and table) and an igraph object (ig appended with node attributes 'node.code' and 'node.table')
#' @note none
#' @export
#' @seealso \code{\link{xA2NetCode}}
#' @include xA2NetCode.r
#' @examples
#' # Load the library
#' library(A2)
#' RData.location <- "http://galahad.well.ox.ac.uk/bigdata_dev/"
#'
#' \dontrun{
#' # load REACTOME
#' # 1a) restricted to Immune System ('R-HSA-168256') or Signal Transduction ('R-HSA-162582')
#' g <- xRDataLoader(RData.customised='ig.REACTOME', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="R-HSA-168256", mode="out")
#' vids <- V(g)[unique(unlist(neighs.out))]$name
#' ig <- igraph::induced.subgraph(g, vids=vids)
#'
#' # 1b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=2, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#'
#' # 1c) visualise the graph with nodes coded and colored by information content (IC)
#' V(ig)$IC <- -1*log10(V(ig)$nAnno/max(V(ig)$nAnno))
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=2, node.shape=19, node.size.range=4, node.color='IC', node.color.title='IC', colormap='white-cyan-darkcyan')
#'
#' V(ig)$term_anno <- log10(V(ig)$nAnno)
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=2, node.shape=19, node.size.range=4, node.color='term_anno', node.color.title='# genes\n(log10)', colormap='white-cyan-darkcyan', zlim=c(1,4))
#'
#'
#' # load EF (annotating GWAS reported genes)
#' # 2a) restricted to disease ('EFO:0000408') and annotation (>=10)
#' # 2a) restricted to immune system disease ('EFO:0000540') and annotation (>=10)
#' g <- xRDataLoader(RData.customised='ig.EF', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="EFO:0000540", mode="out")
#' nodeClan <- V(g)[unique(unlist(neighs.out))]$name
#' anno <- xRDataLoader(RData.customised='org.Hs.egEF', RData.location=RData.location)
#' vec <- sapply(anno$gs, length)
#' nodeAnno <- names(vec[vec>=10])
#' neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno, mode="in")
#' nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
#' vids <- intersect(nodeClan, nodeAnno)
#' ig <- igraph::induced.subgraph(g, vids=vids)
#' V(ig)$anno <- anno$gs[V(ig)$name]
#' # 2b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=4, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#' # 2c) ## GWAS genes for immune system disease ('EFO:0000540')
#' anno <- xRDataLoader(RData.customised='org.Hs.egEF', RData.location=RData.location)
#' genes <- anno$gs[['EFO:0000540']]
#' # 2d) ## GWAS SNPs for immune system disease ('EFO:0000540')
#' annotation <- xRDataLoader(RData.customised='GWAS2EF', RData.location=RData.location)
#' dag <- xDAGpropagate(g, annotation, path.mode="all_paths", propagation="min")
#' snps <- unlist(V(dag)[V(dag)$name=='EFO:0000540']$anno)
#' # 2e) ## ChEMBL targets for immune system disease ('EFO:0000540')
#' annotation <- xRDataLoader(RData.customised='Target2EF', RData.location=RData.location)
#' dag <- xDAGpropagate(g, annotation, path.mode="all_paths", propagation="max")
#' targets <- unlist(V(dag)[V(dag)$name=='EFO:0000540']$anno)
#'
#'
#' # load GOBP
#' # 3a) restricted to immune system process ('GO:0002376') and annotation (>=10)
#' g <- xRDataLoader(RData.customised='ig.GOBP', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="GO:0002376", mode="out")
#' nodeClan <- V(g)[unique(unlist(neighs.out))]$name
#' anno <- xRDataLoader(RData.customised='org.Hs.egGOBP', RData.location=RData.location)
#' vec <- sapply(anno$gs, length)
#' nodeAnno <- names(vec[vec>=10])
#' neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno, mode="in")
#' nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
#' vids <- intersect(nodeClan, nodeAnno)
#' ig <- igraph::induced.subgraph(g, vids=vids)
#' V(ig)$anno <- anno$gs[V(ig)$name]
#' # 3b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=1, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#'
#'
#' # load GOMF
#' # 4a) restricted to molecular function ('GO:0003674') and annotation (>=50)
#' g <- xRDataLoader(RData.customised='ig.GOMF', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="GO:0003674", mode="out")
#' nodeClan <- V(g)[unique(unlist(neighs.out))]$name
#' anno <- xRDataLoader(RData.customised='org.Hs.egGOMF', RData.location=RData.location)
#' vec <- sapply(anno$gs, length)
#' nodeAnno <- names(vec[vec>=50])
#' neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno, mode="in")
#' nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
#' vids <- intersect(nodeClan, nodeAnno)
#' ig <- igraph::induced.subgraph(g, vids=vids)
#' V(ig)$anno <- anno$gs[V(ig)$name]
#' # 4b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=1, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#'
#'
#' # load HPPA
#' # 5a) restricted to Abnormality of the immune system ('HP:0002715') and annotation (>=50)
#' g <- xRDataLoader(RData.customised='ig.HPPA', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="HP:0002715", mode="out")
#' nodeClan <- V(g)[unique(unlist(neighs.out))]$name
#' anno <- xRDataLoader(RData.customised='org.Hs.egHPPA', RData.location=RData.location)
#' vec <- sapply(anno$gs, length)
#' nodeAnno <- names(vec[vec>=50])
#' neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno, mode="in")
#' nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
#' vids <- intersect(nodeClan, nodeAnno)
#' ig <- igraph::induced.subgraph(g, vids=vids)
#' V(ig)$anno <- anno$gs[V(ig)$name]
#' # 5b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=1, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#'
#'
#' # load DO
#' # 6a) restricted to immune system disease ('DOID:2914') and annotation (>=10)
#' g <- xRDataLoader(RData.customised='ig.DO', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="DOID:2914", mode="out")
#' nodeClan <- V(g)[unique(unlist(neighs.out))]$name
#' anno <- xRDataLoader(RData.customised='org.Hs.egDO', RData.location=RData.location)
#' vec <- sapply(anno$gs, length)
#' nodeAnno <- names(vec[vec>=10])
#' neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno, mode="in")
#' nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
#' vids <- intersect(nodeClan, nodeAnno)
#' ig <- igraph::induced.subgraph(g, vids=vids)
#' V(ig)$anno <- anno$gs[V(ig)$name]
#' # 6b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=2, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#'
#'
#' # load MP
#' # 7a) restricted to immune system phenotype ('MP:0005387') and annotation (>=50)
#' # 7a) restricted to abnormal immune system physiology ('MP:0001790') and annotation (>=50)
#' g <- xRDataLoader(RData.customised='ig.MP', RData.location=RData.location)
#' neighs.out <- igraph::neighborhood(g, order=vcount(g), nodes="MP:0001790", mode="out")
#' nodeClan <- V(g)[unique(unlist(neighs.out))]$name
#' anno <- xRDataLoader(RData.customised='org.Hs.egMP', RData.location=RData.location)
#' vec <- sapply(anno$gs, length)
#' nodeAnno <- names(vec[vec>=50])
#' neighs.in <- igraph::neighborhood(g, order=vcount(g), nodes=nodeAnno, mode="in")
#' nodeAnno <- V(g)[unique(unlist(neighs.in))]$name
#' vids <- intersect(nodeClan, nodeAnno)
#' ig <- igraph::induced.subgraph(g, vids=vids)
#' V(ig)$anno <- anno$gs[V(ig)$name]
#' # 7b) visualise the graph with nodes coded
#' ls_gp <- xA2NetCode(g=ig, node.level='term_distance', node.level.value=3, node.shape=19, node.size.range=4, edge.color.alpha=0.2)
#' pdf('xA2NetCode.pdf', useDingbats=FALSE, width=8, height=8)
#' print(ls_gp$code + coord_equal(ratio=1))
#' print(ls_gp$table)
#' dev.off()
#' }
xA2NetCode <- function(g, node.level='term_distance', node.level.value=2, node.label.size=2, node.label.color='darkblue', node.label.alpha=0.8, node.label.padding=0, node.label.arrow=0.01, node.label.force=0, node.shape=19, node.xcoord=NULL, node.ycoord=NULL, node.color=NULL, node.color.title=NULL, colormap='grey-grey', ncolors=64, zlim=NULL, node.size.range=4, title='', edge.size=0.5, edge.color="black", edge.color.alpha=0.4, edge.curve=0.1, edge.arrow=2, edge.arrow.gap=0.02, node.table='term_name', node.table.wrap=50, table.base.size=7, table.row.space=2, table.nrow=55, table.ncol=NULL, root.code='RT')
{
if(any(class(g) %in% c("igraph"))){
ig <- g
}else{
stop("The function must apply to a 'igraph' object.\n")
}
#################
ls_gp <- XGR::xOBOcode(g=ig, node.level=node.level, node.level.value=node.level.value, node.label.size=node.label.size, node.label.color=node.label.color, node.label.alpha=node.label.alpha, node.label.padding=node.label.padding, node.label.arrow=node.label.arrow, node.label.force=node.label.force, node.shape=node.shape, node.xcoord=node.xcoord, node.ycoord=node.ycoord, node.color=node.color, node.color.title=node.color.title, colormap=colormap, ncolors=ncolors, zlim=zlim, node.size.range=node.size.range, title=title, edge.size=edge.size, edge.color=edge.color, edge.color.alpha=edge.color.alpha, edge.curve=edge.curve, edge.arrow=edge.arrow, edge.arrow.gap=edge.arrow.gap, node.table=node.table, node.table.wrap=node.table.wrap, table.base.size=table.base.size, table.row.space=table.row.space, table.nrow=table.nrow, table.ncol=table.ncol, root.code=root.code)
#################
invisible(ls_gp)
}
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