add an argument (e) to Msd

lib/convergence_detection.ml

@@ -72,7 +72,7 @@ let merge_results ?fna_infos ~res_by_tools () : text_file workflow =
         | `Topological_WAG _ -> string "--topological_WAG"
         | `Tdg09 _ -> string "--tdg09"
         | `Multinomial _ -> string "--multinomial"
-        | `Msd _ -> string "--msd"
+        | `Msd (w, e) -> string (sprintf "--msd %f" e)
       in
       seq ~sep:" " [opt; dep w]
     )

lib/pipeline.ml

@@ -254,13 +254,12 @@ let derive_from_det_meth ~det_meth ~(dataset : Dataset.t) ~preview =
 
 let derive_from_dataset ~dataset ~preview ~fast_mode=
   let det_meths = [
-    [
+    ([
       `Identical_LG;
       `Topological_LG;
       `Multinomial;
       `Pcoc;
-
-    ] ;
+    ] @ List.map [0.05;1.] (fun x -> `Msd x)) ;
     if preview then
       []
     else
@@ -273,7 +272,7 @@ let derive_from_dataset ~dataset ~preview ~fast_mode=
     if fast_mode then
       []
     else
-      ([`Diffsel; `Pcoc_C60] @ List.map [0.05;0.1;0.5;1.] (fun x -> `Msd x)) ;
+      [`Diffsel; `Pcoc_C60]  ;
   ]
     |> List.concat in
   let res_by_tools = List.map det_meths ~f:(fun det_meth ->

lib/scripts/calc_t_per_meth.R

@@ -293,6 +293,7 @@ plot_out = function(df_out, df_d , df_recall_sup09_per_meth, meths = NULL, suffi
 
     nb_c = length(unique(df_out$couple))
     colors = c(c("#984EA3","#4AA947","#377EB8","#E41A1C","#F5BE5B","#90EE90","#8B6914")[1:nb_c], c("#7F7F7F","#ADD8E6"))
+    colors2 = c("#984EA3","#4AA947","#377EB8","#E41A1C","#F5BE5B","#90EE90","#8B6914","#7F7F7F","#ADD8E6")
 
     if (! is.null(meths)) {
 
@@ -356,7 +357,7 @@ plot_out = function(df_out, df_d , df_recall_sup09_per_meth, meths = NULL, suffi
     plot = plot + theme(legend.position="top")
     plot = plot + ylim(c(0,1)) + xlim(c(0,1))
     plot = plot + guides(fill=FALSE)
-    plot = plot + scale_color_manual(values=colors)
+    plot = plot + scale_color_manual(values=colors2)
     #plot = plot + geom_point(size=1, alpha=alpha)
     plot = plot + geom_step(direction="vh", size=0.5, alpha=alpha)
     plot = plot + geom_hline( aes(yintercept = 0.9), col="black" ,  size = 0.5, show.legend = NA,linetype="dashed")
@@ -400,6 +401,7 @@ plot_out = function(df_out, df_d , df_recall_sup09_per_meth, meths = NULL, suffi
     df_out_melt2 = subset(df_out_melt2, variable == "specificity")
     df_out_melt2$FPR = 1 - df_out_melt2$value
     
+    if (length(df_out_melt2$methode) > 0){
     df_recall_sup09_per_meth2 = subset(df_recall_sup09_per_meth, couple %in%c("H0/HaPC NeG5", "H0/HaPC NeG5_NeC_div2" , "H0/HaPC NeG5_NeC_x2"))
     df_recall_sup09_per_meth2$couple = gsub("/HaPC ","",df_recall_sup09_per_meth2$couple)
 
@@ -426,7 +428,9 @@ plot_out = function(df_out, df_d , df_recall_sup09_per_meth, meths = NULL, suffi
               base_aspect_ratio = 1.5,
               limitsize = FALSE
               )
-
+    } else {
+    plot_FPR = NULL
+    }
 
 
     print("plot per indicator")
@@ -566,7 +570,7 @@ plot_out = function(df_out, df_d , df_recall_sup09_per_meth, meths = NULL, suffi
 
 
 plot_out(df_out, df_d, df_recall_sup09_per_meth, suffix="")
-condensed_meths = c("PCOC","Diffsel_mean","Identical_LG08","Mutinomial_1MinusLRT","Tdg09_1MinusFDR","Msd_1MinusP","Topological_LG08")
+condensed_meths = c("PCOC","Diffsel_mean","Identical_LG08","Mutinomial_1MinusLRT","Tdg09_1MinusFDR","Msd_1MinusP","Msd_0.01_1MinusP","Msd_0.05_1MinusP","Msd_0.10_1MinusP","Msd_0.50_1MinusP","Msd_1.00_1MinusP","Topological_LG08")
 plot_out(df_out, df_d, df_recall_sup09_per_meth, meths=condensed_meths,  suffix="_condensed")
 
 

lib/scripts/merge_det_results.py

@@ -72,7 +72,7 @@ availableOptions.add_argument('--tdg09', type=str,
                    help="tdg09 output name", default = None)
 availableOptions.add_argument('--multinomial', type=str,
                    help="multinomial output name", default = None)
-availableOptions.add_argument('--msd', type=str,
+availableOptions.add_argument('--msd', type=str, action="append", nargs=2,
                    help="msd output name", default = None)
 availableOptions.add_argument('--fna_infos', type=str,
                    help="fna_infos output name", default = None)
@@ -144,8 +144,14 @@ if args.tdg09 :
   df_tdg09 = pd.read_csv(args.tdg09, sep="\t")
 
 if args.msd :
-  df_msd = pd.read_csv(args.msd, sep="\t")
-  df_msd.rename(columns={'1MinusP': 'Msd_1MinusP'}, inplace=True)
+  print(args.msd)
+  df_msd_l = []
+  for (e, f) in args.msd:
+    df_msd_tmp = pd.read_csv(f, sep="\t")
+    df_msd_tmp.rename(columns={'1MinusP': 'Msd_%s_1MinusP' %(e[0:4])}, inplace=True)
+    df_msd_l.append(df_msd_tmp)
+  
+  df_msd = reduce(lambda x, y: pd.merge(x, y, on = 'Sites', how='outer'), df_msd_l)
 
 if args.multinomial :
   df_multinomial = pd.read_csv(args.multinomial, sep="\t")

lib/scripts/plot_sens_spe_all_trees.R

@@ -46,7 +46,7 @@ files_df_ok = data.frame(files= paste0(input_dir,"/",files), tree = gsub(".tsv",
 } else {
     stop("ERROR no input files")
 }
-condensed_meths = c("PCOC","Diffsel_mean","Identical_LG08","Mutinomial_1MinusLRT","Tdg09_1MinusFDR","Msd_1MinusP","Topological_LG08")
+condensed_meths = c("PCOC","Diffsel_mean","Identical_LG08","Mutinomial_1MinusLRT","Tdg09_1MinusFDR","Msd_1MinusP","Msd_0.01_1MinusP","Msd_0.05_1MinusP","Msd_0.10_1MinusP","Msd_0.50_1MinusP","Msd_1.00_1MinusP","Topological_LG08")
 
 read_dir = function(x) {
   file = x["files"]