Calcualte trajectory of clusters — calculate_cluster_traj

Function which calculates the trajectory data for each cluster Trajectory data is defined as the impact a gene has on time This is obtained by performing a scale feature sum, where each gene is 'equalized' by dividing the value of the gene (at each sample) by the rowSum of the gene (the addition of the gene's values across all samples)

calculate_cluster_traj_data(object, custom_cmap = NULL, scale_feat = TRUE)

Arguments

object: A timeseries object
custom_cmap: A custom cluster map to be used instead of the cmap contained within the object
scale_feat: If the genes should be scaled/transformed using scale feature sum

Value

A dataframe containing the transformed or non-transformed gene values for each cluster

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)
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)
ts_data<-calculate_cluster_traj_data(TS_object,scale_feat=TRUE) #Calculate scaled gene values for genes of clusters