plotFeatures: plotFeatures

Description

Visualize the relations between predictors and response variable ('tssOverlap').

Usage

plotFeatures(object, plot.type = c("box", "density"), feature, ncol, xlab,
  ylab, color = c("#E41A1C", "#377EB8"), alpha = 1)
## S3 method for class 'ChipDataSet':
plotFeatures(object, plot.type = c("box", "density"),
  feature, ncol, xlab, ylab, color = c("#E41A1C", "#377EB8"), alpha = 1)

Arguments

object

A ChipDataSet object.

plot.type

One of ["box", "density"]. Default: "box"

feature

Feature to plot. By default, all the features are plotted.

ncol

Numeric. Arrange individual plots in columns. By default, the number of columns correspond to the number of features used for plotting.

xlab

Character. Title of the x-axis

ylab

Character. Title of the y-axis.

color

A character vector of length two. Default: ["#E41A1C","#377EB8"].

alpha

Color transparency. In a range [0, 1]. Default: 1.

Value

ggplot2 object.

Details

In order to discriminate between functional or gene associated peaks and non-functional or background peaks, each peak in the data set is characterized by several features. Moreover, the user might supply her/his own list of features with the addFeature. Prior to fitting the logistic model, the relations between predictors and response variable (tssOverlap) can be explored with plotFeatures. Based on the plots, poor predictors can be excluded from the analysis to improve the model fit.

Examples

Run this code

### Load ChipDataSet object
data(cds)

### The data can be plotted in two ways
### As a boxplot
plotFeatures(object = cds, plot.type = "box")

### Or as a density plot
plotFeatures(object = cds, plot.type = "density")

### Additionally, only the subset of features can be shown
plotFeatures(object = cds, plot.type = "box", feature = c("pileup", "length"))

### The position of the graphs on the plot, can be adjusted by 'ncol' argument
plotFeatures(object = cds, plot.type = "box", ncol = 2)