fix kmer_merge to kmer_plot connection

src/bin/kmerclust_merge.R

@@ -3,10 +3,11 @@ library(kmerclust)
 args <- commandArgs(trailingOnly = TRUE)
 print(args)
 res_files <- list.files(pattern = "Rdata$")
+print(res_files)
 res_total <- NULL
 for (res_file in res_files) {
   load(res_file)
   res_total <- rbind(res_total, res)
 }
 res <- res_total
-save(res, file = paste0(args[1], "_boot", ".Rdata"))
+save(res, file = paste0(args[1], "_boot.Rdata"))

src/bin/kmerclust_plot.R

@@ -6,17 +6,18 @@ print(args)
 load(file = paste0(args[1], ".Rdata"))
 load(file = paste0(args[1], "_boot.Rdata"))
 
+print(res)
 res %>%
   ggplot(aes(x = name, y = BIC)) +
-  geom_violin() %>%
+  geom_violin() +
   theme_bw()
 ggsave(paste0(args[1], "_BIC.pdf"))
 res %>%
   ggplot(aes(x = name, y = WSS_f / BSS)) +
-  geom_violin() %>%
+  geom_violin() +
   theme_bw()
 ggsave(paste0(args[1], "_WSS_BSS.pdf"))
 count %>%
-  ggplot(aes(x = count_m, y = count_f)) %>%
+  ggplot(aes(x = count_m, y = count_f)) +
   theme_bw()
 ggsave(paste0(args[1], "_scatter.pdf"))

src/main.nf

@@ -33,6 +33,6 @@ workflow {
   COLLATEKMER(MERGEKMER.out.csv.map{it -> [it[0].specie, it[1]] }.groupTuple())
   KMERCLUST_LOAD(COLLATEKMER.out.csv, params_csv.collect())
   KMERCLUST(Channel.of(1..params.bootstrap).combine(KMERCLUST_LOAD.out.rdata))
-  KMERCLUST_MERGE(KMERCLUST.out.rdata.map{it -> [it[1], it[2]]}.groupTuple())
-  KMERCLUST_PLOT(KMERCLUST_MERGE.out.rdata)
+  KMERCLUST_MERGE(KMERCLUST.out.rdata.groupTuple())
+  KMERCLUST_PLOT(KMERCLUST_MERGE.out.rdata.mix(KMERCLUST_LOAD.out.rdata).groupTuple())
 }