xEnrichLadder | R Documentation

## Function to visualise enrichment results using ladder-like plot

### Description

`xEnrichLadder` is supposed to visualise enrichment results using ladder-like plot in which rows for terms and columns for its members. The members are sorted first by sharings and then by individual terms. It returns an object of class "ggplot".

### Usage

    xEnrichLadder(eTerm, sortBy = c("fdr", "or", "adjp", "pvalue",
    "zscore",
    "fc", "nAnno", "nOverlap", "none"), top_num = 5, FDR.cutoff = 0.05,
    CI.one = T, colormap = "lightgrey-grey-black", x.rotate = 90,
    x.text.size = 6, y.text.size = 6, shape = 22, size = 2,
    label = c("concise", "full"), verbose = T, ...)

### Arguments

`eTerm` | an object of class "eTerm"
`sortBy` | which statistics will be used for sorting and viewing gene sets (terms). It can be "adjp" or "fdr" for adjusted p value (FDR), "pvalue" for p value, "zscore" for enrichment z-score, "fc" for enrichment fold change, "nAnno" for the number of sets (terms), "nOverlap" for the number in overlaps, "or" for the odds ratio, and "none" for ordering according to ID of terms
`top_num` | the number of the top terms (sorted according to FDR or adjusted p-values). If it is 'auto', only the significant terms (see below FDR.cutoff) will be displayed
`FDR.cutoff` | FDR cutoff used to declare the significant terms. By default, it is set to 0.05
`CI.one` | logical to indicate whether to allow the inclusion of one in CI. By default, it is TURE (allowed)
`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), and "rainbow" (rainbow colormap, that is, red-yellow-green-cyan-blue-magenta). Alternatively, any hyphen-separated HTML color names, e.g. "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
`x.rotate` | the angle to rotate the x tick labelings. By default, it is 60
`x.text.size` | the text size of the x tick labelings. By default, it is 6
`y.text.size` | the text size of the y tick labelings. By default, it is 6
`shape` | the number specifying the shape. By default, it is 19
`size` | the number specifying the shape size. By default, it is 2
`label` | how to label gene sets (terms). It can be "concise" or "full"
`verbose` | logical to indicate whether the messages will be displayed in the screen
`...` | additional graphic parameters for xHeatmap

### Value

an object of class "ggplot"

### Note

none

### See Also

`xEnrichViewer`, `xHeatmap`

### 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/"

    data(Haploid_regulators)
    ## only IRF1 positive regulators
    data <- subset(Haploid_regulators, Phenotype=='IRF1' &
    MI<0)[,c('Gene')]

    # 1) KEGGenvironmental
    eTerm <- xEnricherGenes(data, ontology="KEGGenvironmental",
    size.range=c(10,2000), min.overlap=5, RData.location=RData.location)
    gp_ladder <- xEnrichLadder(eTerm)

    # 2) PSG
    eTerm <- xEnricherGenes(data,
    ontology=c("PSG","Approved","GWAS","CGL")[1], size.range=c(1,20000),
    min.overlap=0, RData.location=RData.location)
    gp_ladder <- xEnrichLadder(eTerm, sortBy="none", top_num="auto",
    FDR.cutoff=1)
    gp_ladder+ coord_flip()

    # 3) save into the file "xEnrichLadder.pdf"
    mat <- xSparseMatrix(gp_ladder$data)
    pdf("xEnrichLadder.pdf", width=2+ncol(mat)*0.075,
    height=2+nrow(mat)*0.1, compress=T)
    print(gp_ladder)
    dev.off()

    # 4) SIFTS2GOMF
    ## df_fpocket
    SIFTS_fpocket <-
    xRDataLoader(RData='SIFTS_fpocket',RData.location=RData.location)
    df_fpocket <- as.data.frame(SIFTS_fpocket %>%
    dplyr::filter(druggable=='Y') %>% dplyr::group_by(Symbol,PDB_code)
    %>% dplyr::summarise(num_pockets=n()) %>%
    dplyr::arrange(Symbol,desc(num_pockets),PDB_code))
    df_fpocket <- df_fpocket[!duplicated(df_fpocket$Symbol), ]
    ## mat_fpocket
    mat_fpocket <- df_fpocket %>% tidyr::spread(Symbol, num_pockets)
    rownames(mat_fpocket) <- mat_fpocket[,1]
    mat_fpocket <- mat_fpocket[,-1]
    ## gp_ladder
    set.seed(825)
    data <- as.character(sample(unique(df_fpocket$Symbol), 100))
    eTerm <- xEnricherGenes(data=data, ontology="SIFTS2GOMF",
    RData.location=RData.location)
    gp_ladder <- xEnrichLadder(eTerm, sortBy="none", top_num=5,
    FDR.cutoff=0.01, x.rotate=90)
    #gp_ladder + coord_flip()
    ## data_matrix
    ind <- match(colnames(gp_ladder$matrix), colnames(mat_fpocket))
    data_matrix <- mat_fpocket[,ind[!is.na(ind)]]
    ind <- which(apply(!is.na(data_matrix), 1, sum)!=0)
    data_matrix <- data_matrix[ind,]
    ind <- match(data, colnames(data_matrix))
    data_matrix <- data_matrix[,ind[!is.na(ind)]]
    ## gp_pdb
    gp_pdb <- xHeatmap(t(data_matrix), reorder="row", colormap="jet.top",
    x.rotate=90, shape=19, size=1, x.text.size=6,y.text.size=5,
    na.color='transparent', legend.title='# pockets')
    #gp_pdb + coord_flip()
    ## plot_combined
    #plot_combined <- cowplot::plot_grid(gp_ladder, gp_pdb, align="h", ncol=1, rel_heights=c(2,3))

    ## enrichment analysis
    SIFTS_fpocket <-
    xRDataLoader(RData='SIFTS_fpocket',RData.location=RData.location)
    annotation.file <- SIFTS_fpocket[!duplicated(SIFTS_fpocket$Symbol),
    c('Symbol','druggable')]
    ### 100 randomly chosen human genes
    org.Hs.eg <- xRDataLoader(RData='org.Hs.eg',
    RData.location=RData.location)
    set.seed(825)
    data <- as.character(sample(org.Hs.eg$gene_info$Symbol, 100))
    ### optionally, provide the test background (if not provided, all human genes)
    background <- as.character(org.Hs.eg$gene_info$Symbol)
    ### perform enrichment analysis
    eTerm <- xEnricherYours(data.file=data,
    annotation.file=annotation.file, background.file=background,
    size.range=c(10,20000))

    ## End(Not run)