Upset.Binary.Dataframe: Compute a usable matrix to make an Upset plot

In jtcasemajor/GENEXPRESSO: Comparison of DEG and coexpression tools

Description

Fill the dataframe with binary values. 0 for non differentially expressed genes and 1 for the other.

Usage

Upset.Binary.Dataframe(data.to.comp, threshold)

Arguments

data.to.comp	Dataframe of DEG pvalues with genes in columns, and methods in rows.
threshold	Threshold value to fill the dataframe with binary values. By default threshold = 0.05

Value

dataframe filled with binary values

Examples

# Import the dataset
Data = matrix(runif(5000, 10, 100), ncol=20)
group = paste0(rep(c("control", "case"), each = 10),rep(c(1:10),each = 1))
genes <- paste0(rep(LETTERS[1:25], each=10), rep(c(1:10),each = 1))
colnames(Data) = group
row.names(Data) = genes 

# Compute Pvalues for all the methods 
# res.DEG = tools.DEG.RNAseq.merge(Data)
# Make a binary matrix to construct an Upset plot
# Upset = Upset.Binary.Dataframe(res.DEG)
#upset(Upset, sets = names(Upset), 
#          sets.bar.color = "#56B4E9", 
#          order.by = "freq", 
#          empty.intersections = NULL )

jtcasemajor/GENEXPRESSO documentation built on Dec. 21, 2021, 4:11 a.m.