Wrapper function which makes the necessary calls to plots ancestor curation plots
These plots find all terms which are children of the provided ancestors within the specified ontology.
The function will plot and save a dotplot as well as an interactive MDS plot
wrapper_ancestor_curation_plots(
GO_df,
sem_data,
use_names = TRUE,
target_dir = "TS_results/",
return_plot = FALSE,
term_type_gg = FALSE
)The dataframe of GOs as returned by find_relation_to_ancestors
semantic similarity data as created by the godata function
boolean indicating if names or IDs should be used
string indicating the save location of the plots
boolean indicating if the plot should be returned
Boolean indicating if the MDS for the terms should be a ggplot static figure or a interactive plotly figure. by default is FALSE - will use the plotly version
if specified, will return a list containing the ggplot2 object for the dotplot and the plotly object for the MDS plot.
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'
sem_dta<-slot(TS_object,'sem_list')
GOs_ancestors_clust<-find_relation_to_ancestors(target_ancestors,GO_clusters,ontology = ancestor_ontology)
#> Warning: non-vector elements will be ignored
ancestor_plots<-wrapper_ancestor_curation_plots(GOs_ancestors_clust,sem_dta,return_plot=TRUE,target_dir=NULL)