plotConCub {geecc} | R Documentation

## Generate a heatmap showing log2 odds ratios and P-values.

### Description

The function generates a heatmap by calling the `heatmap.2`-function from the `gplots`-package. Each cell shows the log2 odds ratio of the test for the corresponding variables. In addition, stars indicate the P-value for this test.

### Usage

    plotConCub(obj, filter, fix.cat = 1, show=list(), dontshow=list(),
    	args_heatmap.2 = list(), col = list(range = NULL),
    	alt.names = list(), t = FALSE)

### Arguments

`obj` | An object with class concub
`filter` | An object with class concubfilter
`fix.cat` | The heatmap can only visualize a two-dimensional table. In case of three-dimensions, one dimension (category) must be fixed.
`show` | A named list. The names are the names of the categories. Each item is a character vector of variables that should be shown in the plot.
`dontshow` | A named list. The names are the names of the categories. Each item is a character vector of variables that should not be shown in the plot.
`args_heatmap.2` | Arguments passed to ‘`heatmap.2`’. Can be used to change size of fonts etc.
`col` | A vector of colors, for instance from `heat.colors`
`alt.names` | Substitute variables by alternative terms. For instance, if variables are artificial ids, they can be substituted by descriptive text for the heatmap.
`t` | logical; transpose matrix for heatmap. Default `FALSE`.

### Value

An object with class concub.

### Examples

    ##
    ## a completely artificial example run
    ## through the routines of the package
    ##
    R <- 500
    #generate R random gene-ids
    ID <- sapply(1:R, function(r){paste( sample(LETTERS, 10), collapse="" ) } )
    ID <- unique(ID)

    #assign artificial differentially expressed genes randomly
    category1 <- list( deg.smallFC=sample(ID, 100, rep=FALSE),
    	deg.hughFC=sample(ID, 100, rep=FALSE) )
    #assign artificial GO terms of genes randomly
    category2 <- list( go1=sample(ID, 50, replace=FALSE),
    	go2=sample(ID, 166, replace=FALSE),
    	go3=sample(ID, 74, replace=FALSE),
    	go4=sample(ID, 68, replace=FALSE) )
    #assign artificial sequence length of genes randomly
    LEN <- setNames(sample(seq(100, 1000, 100), length(ID), replace=TRUE), ID)
    category3 <- split( ID, f=factor(LEN, levels=seq(100, 1000, 100)) )
    CatList <- list(deg=category1, go=category2, len=category3)

    ConCubFilter.obj <- new("concubfilter", names=names(CatList))
    ConCub.obj <- new("concub", categories=CatList)
    ConCub.obj.2 <- runConCub( obj=ConCub.obj, filter=ConCubFilter.obj, nthreads=1 )
    ConCub.obj.3 <- filterConCub( obj=ConCub.obj.2, filter=ConCubFilter.obj )
    plotConCub( obj=ConCub.obj.3, filter=ConCubFilter.obj )

[Package geecc version 1.14.0 Index]