Create dotplot for ancestor query results — dotplot

Function which creates a dotplot of all the GO terms found to be related to selected ancestors. The function expects the dataframe from find_relation_to_ancestors

The plot shows enirchment (up or down), to which ancestor a GO has been associated as well as the size of the term. Each term is shown on the axis of the cluster for wich it was found.

dotplot_ancestors(ancestor_df, use_names = FALSE, enrichment_dta = TRUE)

Arguments

ancestor_df: The dataframe obtained from find_relation_to_ancestors
use_names: Boolean indicating if the ancestor names or IDs should be used
enrichment_dta: Boolean indicating if the plot has enrichment data

Value

The ggplot2 object for the created dotplot

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
#Save path set to NULL to not save results
gpro_res<-gprofiler_cluster_analysis(TS_object,'GO:BP',save_path=NULL)
GO_clusters<-gpro_res[['GO_df']]
#Immune related ancestors
target_ancestors<-c('GO:0002253','GO:0019882','GO:0002404','GO:0002339','GO:0042386',
                    'GO:0035172','GO:0002252','GO:0006955','GO:0002520','GO:0090713',
                    'GO:0045321','GO:0001776','GO:0050900','GO:0031294','GO:0002262',
                    'GO:0002683','GO:0002684','GO:0002440','GO:0002682','GO:0002200',
                    'GO:0045058','GO:0002507')
ancestor_ontology<-'BP'
GOs_ancestors_clust<-find_relation_to_ancestors(target_ancestors,GO_clusters,ontology = ancestor_ontology)
#> Warning: non-vector elements will be ignored
my_dotplot<-dotplot_ancestors(GOs_ancestors_clust,use_names=TRUE,enrichment_dta=FALSE)