plot.associations: Plot of `associations` objects

Description

Plot showing the associated trajectories with or without estimated time shift.

Usage

# S3 method for associations
plot(x, data1, data2, time, feature1, feature2, cutoff,
  fdr = T, absCor = T, withShift = F, ...)

Arguments

an object of class associations

data1

an object of class matrix or data.frame.

data2

an object of class matrix or data.frame.

time

a vector of class numeric presenting the measured time points.

feature1

the reference feature to visualise, either the index or the name.

feature2

the associated feature to visualise, either the index or the name.

cutoff

for the associated feature. If fdr=TRUE the false discovery rate (fdr) corrected p-value (default cutoff=0.05). If fdr=FALSE the absolute Pearson Correlation cutoff (default cutoff=0.9).

fdr

(default TRUE) indicating if the false discovery rate of the corrected p-values from the associations object should be used as cutoff to visualize associated profiles. If FALSE the absolute Peason correlation is used as cutoff.

absCor

(default FALSE) if fdr=FALSE you can choose to visualise associations invariant for positive or negative correlation.

withShift

(default FALSE) indicating if the associated feature should be plotted with the time shift.

…

ignored

Value

plot showing the associated data as calculated by associateData()

Details

The function allows to visualise features with and without realignement (or shift) of the time profiles according to the estimated delays using associateData() function from the dynOmics package. Features to be visualised can be filtered either using FDR corrected p-values or a correlation threshold.

Examples

Run this code

# NOT RUN {
data(Metabolites)
data(Transcripts)
associations <- associateData(Metabolites[,1:2],Transcripts[,c(1:100)])
#if you only define feature1 or feature2 if will plot all associations
plot(associations,Metabolites,Transcripts,feature1=1,withShift = TRUE)
#if you define feature1 and feature2 it will only plot these two profiles
plot(associations,Metabolites,Transcripts,feature1="Metabolite 1",feature2="Transcript 2")
# }