KANT (version 2.0)

plot_up: Function to plot the results of expression_up

Description

Generates a graphic of the 50 best probesets of the algorithm. Every probeset is plotted in function of the number of samples in the subpopulation that overexpressed it and delta (difference of expression between max of normal samples and median of the subpopulation). They are represented by a circle, with a size proportional to 1/(standard deviation of expression in the subpopulation)

Usage

plot_up(N, eset_up, cells_type, fontsize)

Arguments

N

Number of probesets to plot

eset_up

Result of expression_up

cells_type

Name of the type of cells tested (just for title of the graph)

fontsize

For legends

See Also

See Also link{expression_up}

Examples

## The function is currently defined as
function (N, eset_up, cells_type, fontsize) 
{
    probes <- 1:N
    plot(as.numeric(as.character(pData(featureData(eset_up))[probes, 
        "Numbre_up"])), as.numeric(as.character(pData(featureData(eset_up))[probes, 
        "Delta_median_up"])), 
        cex = (1/(0.1 + as.numeric(as.character(pData(featureData(eset_up))[probes, 
        "IQR_up"])))), pch = 21, main = c("Over-expression in  :", 
        cells_type), xlab = "Number of samples in the sub-population over-expressing the gene", 
        ylab = "Delta")
    text(as.numeric(as.character(pData(featureData(eset_up))[probes, 
        "Numbre_up"])), as.numeric(as.character(pData(featureData(eset_up))[probes, 
        "Delta_median_up"])), as.character(pData(featureData(eset_up))[probes, 
        "Gene.Symbol"]), pos = 2, cex = fontsize)
  }