For a DESeq2 analysis, the function which retrieves the variance stabililizing
transformation via the vst() of deseq2.
For a limma analysis, the function will calculate the PCs using plotMDS
If no DE_type is 'all', the function will plot the PCA for the normalized data.
It then plots a labelled PCA plot in png format
plot_PCA_TS(
time_object,
exp_name = NULL,
DE_type = NULL,
show_names = TRUE,
return_plot = TRUE,
pcsToUse = 1:2
)A timeseries object
The name of the experiment for which the PCA is plotted
Either conditional or temporal for the type of differential experiment being plotted
boolean indicating if sample names should be put on the pca or not
boolean indicating if the plot should be returned. If FALSE, it will return the vst and pca_data instead.
vector of two integers indicating which PCs to plot. The first value will be the xaxis while the second will be the yaxis.
the pca_plot
TS_object<-create_example_object_for_R()
TS_object <- normalize_timeSeries_with_deseq2(time_object=TS_object)
#> converting counts to integer mode
#Perform conditional differential gene expression analysis
TS_object<-conditional_DE_wrapper(TS_object,vignette_run=TRUE)
TS_object<-temporal_DE_wrapper(TS_object,do_all_combinations=FALSE,vignette_run=TRUE)
TS_pca<-plot_PCA_TS(TS_object,DE_type='all')
#> using ntop=500 top features by variance