Ancestor querying for GOs

Function which finds if a term is associated with a target ancestor

The function will retrieve ancestry information from the annotationDbi of the loaded library as well as the inputted ontology (BP by default). It will then isolate and preserve the GO terms found to be related to one or several of the target ancestors. If the GO is related to several ancestors, only the one highest in the tree is linked to that GO.

The function also associates a color to each ancestor, due to this, there is a maximum of 9 ancestors that can be selected

find_relation_to_ancestors(target_ancestors, GOs_to_check, ontology = "BP")

Arguments

target_ancestors

A vector of GO IDs to use as ancestors

GOs_to_check

A dataframe created by either gprofiler_for_conditional_DE or gprofiler_cluster_analysis or within the 'Gpro_org' slot of a time_object

ontology

The ontology to be used to search for ancestry

Value

A updated dataframe where all non-ancestor related GOs were removed GOs with a found ancestor are associated to it via a new column called 'ancestor'

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