data-raw/sysdata.R

In GAPGOM: GAPGOM (novel Gene Annotation Prediction and other GO Metrics)

# sysdata.rda generation file (included params for tests)

# variable requests from package for resaving/updating
# Part of actual data generation are commented
freq_go_pairs <- GAPGOM:::freq_go_pairs # see freq_go_pairs.R
gapgom_tests <- GAPGOM:::gapgom_tests # documented examples are used / code in tests
benchmarks <- GAPGOM:::benchmarks # see below

#####
#' Benchmark prep, this is ran on multiple machine of choice and concatted into a list at the en

#' prepare

#' library(GAPGOM)
#' library(profvis)
#' library(GO.db)
#' library(graph)
#' 
#' # prepare the godata for mouse and some other calculations later needed in benchmarking
#' organism <- "human"
#' ontology <- "BP"
#' go_data <- GAPGOM::set_go_data(organism, ontology)
#' 
#' #' term
#' # grab 15 random GOs (for term algorithm)
#' ## sample(unique(go_data@geneAnno$GO), 15)
#' random_gos <- c("GO:0030177", "GO:0001771", "GO:0045715", "GO:0044330", "GO:0098780",
#'                 "GO:1901006", "GO:0061143", "GO:0060025", "GO:0015695", "GO:0090074",
#'                 "GO:0035445", "GO:0008595", "GO:1903634", "GO:1903826", "GO:0048389"
#' )
#' # print them for reproducability
#' ## dput(random_gos)
#' # now compare all unique random GO pairs. (105 uniques).
#' unique_pairs <- GAPGOM:::.unique_combos(random_gos, random_gos)
#' 
#' times <- c()
#' mem_usages <- c()
#' for (i in seq_len(nrow(unique_pairs))) {
#'   prof_toptitj <- profvis({
#'     pair <- unique_pairs[i]
#'     go1 <- pair[[1]]
#'     go2 <- pair[[2]]
#'     GAPGOM::topo_ic_sim_term(ontology, organism, go1, go2, go_data = go_data)
#'   })
#'   time <- max(prof_toptitj$x$message$prof$time)*10
#'   mem <- max(prof_toptitj$x$message$prof$memalloc)
#'   mem_usages <- c(mem_usages, mem)
#'   times <- c(times, time)
#'   gc()
#' }
#' times_term <- times
#' mems_term <- mem_usages
#' 
#' #' gene
#' 
#' ## dput(sample(unique(go_data@geneAnno$ENTREZID), 5))
#' random_genes <- c("3848", "2824", "65108", "3988", "10800")
#' 
#' unique_pairs <- GAPGOM:::.unique_combos(random_genes, random_genes)
#' 
#' times <- c()
#' mem_usages <- c()
#' for (i in seq_len(nrow(unique_pairs))) {
#'   prof_topg1g2 <- profvis({
#'     pair <- unique_pairs[i]
#'     gene1 <- pair[[1]]
#'     gene2 <- pair[[2]]
#'     GAPGOM::topo_ic_sim_genes(ontology, organism, gene1, gene2, go_data=go_data)
#'   })
#'   time <- max(prof_topg1g2$x$message$prof$time)*10
#'   mem <- max(prof_topg1g2$x$message$prof$memalloc)
#'   mem_usages <- c(mem_usages, mem)
#'   times <- c(times, time)
#'   gc()
#' }
#' times
#' mem_usages
#' times_gen <- times
#' mems_gen <- mem_usages
#' 
#' #' geneset
#' 
#' list1=c("126133","221","218","216","8854","220","219","160428","224","222","8659","501","64577","223","217","4329","10840","7915", "5832")
#' times <- c()
#' mem_usages <- c()
#' for (i in seq(length(list1)-1)) {
#'   sampled_list <- list1[1:(i+1)]
#'   print(sampled_list)
#'   p <- profvis({
#'     GAPGOM::topo_ic_sim_genes(ontology, organism, sampled_list, sampled_list, drop=NULL, go_data=go_data)
#'   })
#'   time <- max(p$x$message$prof$time)*10
#'   mem <- max(p$x$message$prof$memalloc)
#'   mem_usages <- c(mem_usages, mem)
#'   times <- c(times, time)
#'   gc()
#' }
#' times
#' mem_usages
#' times_genset <- times
#' mems_genset <- mem_usages
#' 
#' #' combine to list (seperateley done per machine)
#' 
#' benchmarks <- list()
#' 
#' benchmarks$server_times_term <- times_term
#' benchmarks$server_times_gen <- times_gen
#' benchmarks$server_times_genset <- times_genset
#' benchmarks$server_mems_term <- mems_term
#' benchmarks$server_mems_gen <- mems_gen
#' benchmarks$server_mems_genset <- mems_genset
#' ##
#' benchmarks$laptop_times_term <- times_term
#' benchmarks$laptop_times_gen <- times_gen
#' benchmarks$laptop_times_genset <- times_genset
#' benchmarks$laptop_mems_term <- mems_term
#' benchmarks$laptop_mems_gen <- mems_gen
#' benchmarks$laptop_mems_genset <- mems_genset
#' 
#' c(a, b) #... combines all machine results
#' 
#' # LNCRNAPRED BENCHMARK NOW INCLUDED, SEE VIGNETTE INSTEAD

#####

save(freq_go_pairs, gapgom_tests, benchmarks, file = "./sysdata.rda", compress = "xz", compression_level = 9)