Plot nearest ancestor MDS (clustered GOs)

Source: R/MDS_GO_results_functions.R

Function which plots the MDS for clusters/nearest ancestor of the ancestor curation dataframe

plot_ancestor_clust_MDS(the_data, ggplot_version = FALSE)

Arguments

the_data

The dataframe as created by calculate_and_format_MDS

ggplot_version

Boolean indicating if a ggplot (static) version of the plot should be created instead of a plotly version. By default is FALSE.

Value

p The plotly plot to enable either visualization or saving of the plot

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
#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']]
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')
GOs_ancestors_clust<-find_relation_to_ancestors(target_ancestors,GO_clusters,ontology = 'BP')
#> Warning: non-vector elements will be ignored
sem_dta<-slot(TS_object,'sem_list')
plot_data<-calculate_and_format_MDS(GOs_ancestors_clust,sem_dta)
plot_data<-merge_duplicate_modules(plot_data)
for_merger<-GOs_ancestors_clust[,c('term_id','ancestor_name','-log10(padj)')]
colnames(for_merger)=c('GO.ID','Ancestor','-log10(padj)')
plot_data<-merge(plot_data,for_merger,by='GO.ID')

my_MDS<-plot_ancestor_clust_MDS(plot_data)