Plot gprofiler results

Creates a dataframe of GO's based on the gprofiler results stored in a time series object. The dataframe is specific to the requested ontology

If save_path is not null, the gprofiler results will be saved to the designated location in both csv and html (interactive plot) format.

gprofiler_cluster_analysis(
  object,
  ontology,
  save_path = NULL,
  return_specific_cluster = NULL,
  return_interactive = TRUE
)

Arguments

object

A time series object

ontology

the ontology that will be returned in the dataframe (ex: GO:BP)

save_path

The folder path to save results if gprofiler results are to be saved

return_specific_cluster

String to return the gost plot of a specific cluster

return_interactive

Boolean indicating if the returned plot (if needed) is to be interactive or static.

Value

GO_df A dataframe containing all the GOs (ID) found, their cluster, and the term name

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)