Wrapper function for the creation of a dotplot which summarises the gprofiler findings for a specified ontology.
The function reads the results for a location. It will automatically search for the 'gprofiler_results/data_files' folder in the procided location. This is should be located in the TS_results folder, but can change based on the user.
The function will attempt to create the plot with proper width and height, but these can be over-written by providing them to the function.
The function will save the plot in the location provided through the gpro_file_location parameter.
GO_dotplot_wrapper(
object,
file_loc,
target_ontology,
top_n,
sig_only = TRUE,
custom_width = NULL,
custom_height = NULL,
return_plot = FALSE
)A time series object
The location where the files are to be saved
The targeted ontology ex: 'REAC' or 'GO:BP'
The number of top GOs to plot per cluster
If only significant GOs should be included.
A custom value for the width of the plot
A custom value for the height of the plot
Boolean indicating if the plot should be returned
if specified, the function will return the ggplot2 object for the dotplot
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)
gpro_dotplot<-GO_dotplot_wrapper(TS_object,file_loc=NULL,target_ontology='GO:BP',top_n=10,return_plot=TRUE)