densityPlot: Quantify Genes and Corresponding ED ratios in Each Category

In Sharonxiaoyuan/eegc: Engineering Evaluation by Gene Categorization (eegc)

Quantify Genes and Corresponding ED ratios in Each Category

Description

Quantify genes in each gene category and their expression difference (ED) ratios in a density plot.

Usage

densityPlot(ratio, color = NULL, main = NA, xlab = NA, ylab = "Density",
legend.labels = NULL, shade = TRUE, transparency = TRUE, proportion = TRUE,
out.file = NULL, ...)

Arguments

ratio	a list of ED ratios for five gene categories, alternatively output by categorizeGene.
color	vector of colors for the five gene categories.
main, xlab, ylab	the overall title, tile for x axis, and title for y axis, see title.
legend.labels	vector of labels for the legend.
shade	logical to determine if the five categories are filled with shades.
transparency	logical to determine if the density plot is transparent.
proportion	logical to determine whether the proportion of each category genes over the all genes is drawn on the density plot.
out.file	a character string naming the output file with density plot.
...	parameters in plot.

Value

a density plot

Examples

data(cate.ratio)
names(cate.ratio)
# make the extreme ED ratios in Reversed and Over categories to the median values
reverse = cate.ratio[[1]]
over = cate.ratio[[5]]
reverse[reverse[,1] <= median(reverse[,1]), 1]  = median(reverse[,1])
over[over[,1] >= median(over[,1]),1] = median(over[,1])
cate.ratio[[1]] = reverse
cate.ratio[[5]] = over

# densityPlot(cate.ratio, xlab = "ED ratio", ylab = "Density", proportion = TRUE)

Sharonxiaoyuan/eegc documentation built on April 17, 2022, 2:10 a.m.