Function requires the path to the individual count files and the samples to be included in the matrix. Files will be read based on the sample they represent the values from the different files are merged into a matrix and returned
prep_RNAseq_matrix(path_to_counts, selected_samples)final_counts The formatted count matrix
path <- tempfile()
bfc <- BiocFileCache(path, ask = FALSE)
bfc_cache<-slot(bfc,'cache')
write_example_data_to_dir(target_dir=bfc_cache)
my_path_data<-paste0(bfc_cache,'/data/PBMC/raw_counts_TS')
my_path_sample_dta<-paste0(bfc_cache,'/data/PBMC/sample_file.csv')
graph_vect<-c("#e31a1c","#1f78b4")
TS_object <- new('TimeSeries_Object',
group_names=c('IgM','LPS'),group_colors=graph_vect,DE_method='DESeq2',
DE_p_filter='padj',DE_p_thresh=0.05,DE_l2fc_thresh=1,
PART_l2fc_thresh=4,sem_sim_org='org.Hs.eg.db',Gpro_org='hsapiens')
TS_object <- add_experiment_data(TS_object,sample_dta_path=my_path_sample_dta,count_dta_path=my_path_data)
groups<-slot(TS_object,'group_names')
#Ensures that the order will follow the grouping order
sample_data<-exp_sample_data(TS_object)
selected_samples_1<-sample_data$sample[sample_data$group %in% groups[1]]
selected_samples_2<-sample_data$sample[sample_data$group %in% groups[2]]
selected_samples<-c(selected_samples_1,selected_samples_2)
#Prepare the matrix according to the differential expression method (affects input)
final_counts<-prep_RNAseq_matrix(my_path_data,selected_samples)