Commit f23e90fe authored by Carine Rey's avatar Carine Rey
Browse files

add an argument (e) to Msd

parent c82b037a
......@@ -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]
)
......
......@@ -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 ->
......
......@@ -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")
......
......@@ -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")
......
......@@ -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"]
......
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