The heatmap shows selected genes as rows and replicates of the time series experiment as columns. Gene clusters are identified by colored row annotation. Replicates are split by their grouping (conditional) and ordered (within each other) via time points The legend for groupings, clusters, timepoints, and heatmap values is given on the right hand side of the heatmap.
the heatmap is saved twice, once in png format, and the other in svg format
PART_heat_map(object, heat_name = "custom_heat_map")
none or PART heatmap if heat_name is set to NULL
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=TRUE,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))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
#Heatmap will be saved to main directory
PART_heat<-PART_heat_map(TS_object,NULL) #Create a summary heatmap