CoNI (version 0.1.0)

getVertexsPerEdgeFeature_and_Grid: Vertex-Class profile per edge feature Side-by-Side (two treatments)

Description

This function creates a grid of barplots. The barplot of one side depicts the number of class vertex features per edge feature for treatment 1 and the other side the same barplot for treatment 2. Results of both Treatments are side by side for better comparison.

Usage

getVertexsPerEdgeFeature_and_Grid(
  CompTreatTable,
  Treat1,
  Treat2,
  Annotation,
  chunks = 3,
  ggrep = TRUE,
  xlb = "Edge Feature",
  onlyT = FALSE,
  small = FALSE,
  ...
)

Arguments

CompTreatTable

Output of Compare_VertexClasses_sharedEdgeFeatures

Treat1

Name treatment 1 as in table CompTreatTable

Treat2

Name treatment 2 as in table CompTreatTable

Annotation

Data frame that includes the rgb colors for every class. The column 'class' (or 'Class') has to be present and also the column 'ColorRgb'

chunks

To avoid a non readable dense plot the results can be spitted in multiple plots

ggrep

logical. If TRUE includes ggrepel labels for every bar

xlb

Change the x-axis label

onlyT

logical. If TRUE a table is returned instead of a grid of plots

small

logical. If only a few edge features are in the input set as TRUE. A single plot will be created

...

Other parameters for inner functions, mainly ggplot2 visual parameters

Value

A gtable containing side-by-side barplots, one for each treatment, showing the number of vertex features per class for every shared edge feature

Examples

# NOT RUN {
data(VertexClassesSharedGenes_HFDvsChow)
VCSGs<-VertexClassesSharedGenes_HFDvsChow
data(MetColorTable)
HFD_vs_Chow_LCP_Gene<-getVertexsPerEdgeFeature_and_Grid(VCSGs,
                                                        "HFD","Chow",
                                                        Annotation=MetColorTable,
                                                        ggrep=FALSE,
                                                        small = FALSE,
                                                        chunks = 3,
                                                        szLegendKey=0.2)
plot(HFD_vs_Chow_LCP_Gene)
# }