R/MDS_GO_results_functions.R
wrapper_MDS_and_MDS_clusters.RdWrapper function which makes the necessary calls to plot a multi dimensional scaling (MDS) plot for all of the terms found in each cluster (for a specified ontology). The function calculates and plots a nearest ancestor version of the MDS using the BMA and WANG approach. Each term is brought up to it's nearest ancestor.
Each plot is saved as an interactive plot in TS_results
wrapper_MDS_and_MDS_clusters(
GO_clusters,
sem_data,
sem_ontology,
target_dir = "TS_results/gprofiler_results/",
return_plot = FALSE,
term_type_gg = FALSE
)The dataframe of GOs contained in the 'Gpro_org' slot of a time_object
semantic similarity data as created by the godata function
The ontology that was used to calculate the semantic data and that will be plotted
string indicating the location where the results will be saved
boolean indicating if the plots 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 two plotly objects for both the GO term MDS and clustered GO term MDS.
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_clusters<-gpro_res[['GO_df']]
sem_dta<-slot(TS_object,'sem_list')
#Plot MDS and clustered MDS
MDS_plots<-wrapper_MDS_and_MDS_clusters(GO_clusters,sem_dta,sem_ontology='BP',target_dir=NULL,return_plot=TRUE)
#> Warning: non-vector elements will be ignored
#> 'select()' returned many:1 mapping between keys and columns
#> Could not cluster found MDS terms