Format data for PART — prep_counts_for_PART • TimeSeriesAnalysis

The function retrieves the counts from the time series object and filters it to only contain relevant genes and samples. The data can also be scaled if the parameter 'scale' is set to TRUE

prep_counts_for_PART(object, target_genes, scale, target_samples)

Arguments

object: A time series object
target_genes: Vector of genes to keep/use
scale: Boolean indicating if the data should be scaled
target_samples: Vector of samples to keep/use

Value

The timeseries object with the added PART count matrix

Examples

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)
#Extract genes for PART clustering based on defined log(2)foldChange threshold
signi_genes<-select_genes_with_l2fc(TS_object)

#Use all samples, but implement a custom order. In this case it is reversed
sample_data<-exp_sample_data(TS_object)
TS_groups<-slot(TS_object,'group_names')
samps_2<-sample_data$sample[sample_data$group==TS_groups[2]]
samps_1<-sample_data$sample[sample_data$group==TS_groups[1]]

#Create the matrix that will be used for PART clustering
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))