Format clustered GO data for plotting — create_clustered_module

Function which counts the number of GOs in each cluster and determines how many GOs of each module appears in each cluster

create_clustered_module_dataframe(cluster_df)

Arguments

cluster_df: A dataframe containing the GO terms, their cluster, and module(s) as created by find_clusters_from_termdist

Value

cluster_nb_df A dataframe containing GO terms for the clusters, their associated cluster, the number of GO terms in each cluster as well as the module dispertion of the GOs within the 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_object<-run_gprofiler_PART_clusters(TS_object,vignette_run=TRUE) #Run the gprofiler analysis
#> running Gprofiler on PART clusters
#Results saved to created directory
gpro_res<-gprofiler_cluster_analysis(TS_object,'GO:BP',save_path=NULL)
GO_clusters<-gpro_res[['GO_df']]
sem_dta<-slot(TS_object,'sem_list')
found_clusters<-find_clusters_from_termdist(GO_clusters,sem_dta)
clustered_module_df<-create_clustered_module_dataframe(found_clusters)