Read gprofiler data from results folder — read_gprofiler

Function which reads the grprofiler data results from the time series object

The function reads the information for each cluster and filters for the required columns, ontology, and number of GOs. It then adds a -log10 FDR

read_gprofiler_results(object, ont = "REAC", top_n = NULL)

Arguments

object: A time series object
ont: The ontology to be filtered for
top_n: The number of top GOs to be filtered for

Value

A dataframe with the requested information

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)
GO_top_cluster<-read_gprofiler_results(TS_object,ont='GO:BP',top_n=10)