xEnrichCtree | R Documentation

## Function to visualise enrichment results using a tree-like circular plot

### Description

`xEnrichCtree` is supposed to visualise enrichment results using a tree-like circular plot.

### Usage

    xEnrichCtree(
    eTerm,
    ig,
    FDR.cutoff = NULL,
    node.color = c("zscore", "adjp", "or", "nOverlap"),
    colormap = "brewer.Reds",
    zlim = NULL,
    node.size = c("adjp", "zscore", "or", "nOverlap"),
    slim = NULL,
    node.size.range = c(0.5, 4.5),
    group.gap = 0.08,
    group.color = "lightblue",
    group.size = 0.2,
    group.label.size = 2,
    group.label.color = "black",
    legend.direction = c("auto", "horizontal", "vertical"),
    leave.label.orientation = c("inwards", "outwards"),
    ...
    )

### Arguments

`eTerm` | an object of class "eTerm" or "ls_eTerm". Alterntively, it can be a data frame having all these columns ('name','adjp','or','zscore','nOverlap'; 'group' optionally)
`ig` | an object of class "igraph" with node attribute 'name'. It could be a 'phylo' object converted to. Note: the leave labels would be the node attribute 'name' unless the node attribute 'label' is explicitely provided
`FDR.cutoff` | FDR cutoff used to show the significant terms only. By default, it is set to NULL; useful when nodes sized by FDR
`node.color` | which statistics will be used for node coloring. It can be "or" for the odds ratio, "adjp" for adjusted p value (FDR) and "zscore" for enrichment z-score

`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 http://html-color-codes.info/color-names

`zlim` | the minimum and maximum values for which colors should be plotted
`node.size` | which statistics will be used for node size. It can be "or" for the odds ratio, "adjp" for adjusted p value (FDR) and "zscore" for enrichment z-score
`slim` | the minimum and maximum values for which sizes should be plotted
`node.size.range` | the range of actual node size
`group.gap` | the gap between group circles. Only works when multiple groups provided
`group.color` | the color of group circles. Only works when multiple groups provided
`group.size` | the line width of group circles. Only works when multiple groups provided
`group.label.size` | the size of group circle labelling. Always a sequential integer located at the top middle. Only works when multiple groups provided
`group.label.color` | the color of group circle labelling. Only works when multiple groups provided
`legend.direction` | the legend guide direction. It can be "horizontal" (useful for many groups with lengthy labelling), "vertical" and "auto" ("vertical" when multiple groups provided; otherwise "horizontal")
`leave.label.orientation` | the leave label orientation. It can be "outwards" and "inwards"
`...` | additional graphic parameters used in xCtree

### Value

a ggplot2 object appended with 'ig', 'data' which should contain columns 'x','y', 'leaf' (T/F), 'name' (the same as V(ig)$name), 'tipid' (tip id), 'label' (if not given in ig, a 'name' varient), 'angle' and 'hjust' (assist in leave label orientation), and 'data_enrichment' (enrichment results for tips)

### Note

none

### See Also

`xCtree`

### Examples

    ## Not run:
    # Load the XGR package and specify the location of built-in data
    library(XGR)
    RData.location <- "http://galahad.well.ox.ac.uk/bigdata"

    # load the atlas of AA pathways
    AA.template <- xRDataLoader("AA.template",
    RData.location=RData.location)
    # consensus tree
    ig <- AA.template$consensus$ig

    # enrichment analysis using AA pathways
    input <- xRDataLoader('Haploid_regulators_all',
    RData.location=RData.location)
    data <- subset(input, Phenotype=="AKT")
    genes <- data$Gene[data$FDR<0.05]
    background <- data$Gene
    eTerm <- xEnricherGenes(genes, background=background, ontology="AA",
    min.overlap=5, test="fisher", RData.location=RData.location)

    # circular visualisation of enriched AA pathways
    gp <- xEnrichCtree(eTerm, ig)

    ###############################
    # advanced use: multiple groups
    # enrichment analysis using AA pathways
    Haploid <- subset(input, FDR<0.05)
    ls_group <- split(x=Haploid$Gene, f=Haploid$Phenotype)
    background <- unique(input$Gene)
    ls_eTerm <- xEnricherGenesAdv(ls_group, background=background,
    ontologies="AA", test="fisher", min.overlap=5,
    RData.location=RData.location)

    # circular visualisation of enriched AA pathways
    gp <- xEnrichCtree(ls_eTerm, ig)

    ## End(Not run)