The function set's a seed for reproducibility of results, it then uses the part function from the clusterGenomics package to establish which genes belong to what clusters. The clusters are then ordered using hierarchical clustering. This may result in some PART cluster being split, therefore the order is adjusted to put them together. We first order using hierarchical clustering for visual purposes.
compute_PART(
object,
part_recursion = 100,
part_min_clust = 10,
dist_param = "euclidean",
hclust_param = "average",
custom_seed = NULL,
custom_matrix = NULL,
return_as_object = TRUE,
vignette_run = FALSE
)A timeseries object
The number of recursions for PART calculation
The minimum number of genes per cluster
The distance parameter for clustering
The hierarchical clustering method/parameter to be used
The seed inputed (if any)
Allows the input of a custom matrix instead of taking it from the object
Boolean indicating if the results should be returned within the submitted object or as a list
Boolean indicating if this is for Vignettes, if so the function will load the appropriate example data instead of performing the computation.
The timeseries object with the PART results added
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)