plotConCub {geecc} | R Documentation |
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.
plotConCub(obj, filter, fix.cat = 1, show=list(), dontshow=list(), args_heatmap.2 = list(), col = list(range = NULL), alt.names = list(), t = FALSE)
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 ‘ |
col |
A vector of colors, for instance from |
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 |
An object with class concub.
## ## 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 )