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Commit 5d95464a authored by Carine Rey's avatar Carine Rey
Browse files

add 2 new post_analyse plots

parent 5649b154
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Showing with 390 additions and 121 deletions
etc/docker/r_basics/Dockerfile
View file @ 5d95464a
......@@ -8,3 +8,6 @@ RUN echo 'install.packages(c("optparse", "ggplot2", "reshape2", "cowplot", "coda
RUN apt-get update && \
apt-get install --no-install-recommends -qy \
pandoc
RUN echo 'source("https://bioconductor.org/biocLite.R"); biocLite("ggtree")' > /tmp/packages.R \
&& Rscript /tmp/packages.R
etc/docker/r_basics/build_docker.sh
View file @ 5d95464a
......@@ -4,7 +4,7 @@ set -e
IMAGE_NAME=r_basics
DOCKERFILE_DIR=.
TAG=07232018
TAG=08012018
REPO=carinerey/$IMAGE_NAME:$TAG
docker build -t $REPO -f ./Dockerfile $DOCKERFILE_DIR
......
lib/convergence_detection.ml
View file @ 5d95464a
......@@ -41,8 +41,12 @@ type dataset_res = {
plot_merged_results : svg workflow ;
}
let merge_results ~res_by_tools : text_file workflow =
let merge_results ?fna_infos ~res_by_tools () : text_file workflow =
let env = docker_image ~account:"carinerey" ~name:"python_basics" ~tag:"07252018" () in
let fna_infos = match fna_infos with
| Some (sw) -> sw
| _ -> None
in
let command = List.map res_by_tools ~f:(fun res ->
let w = match res with
| `Pcoc d -> Pcoc.results d
......@@ -78,6 +82,7 @@ let merge_results ~res_by_tools : text_file workflow =
file_dump (string Scripts.merge_det_results) ;
opt "-o" ident dest ;
seq ~sep:" " command ;
option( opt "--fna_infos" dep ) fna_infos;
] ;
]
......
lib/convergence_detection.mli
View file @ 5d95464a
......@@ -31,7 +31,9 @@ type dataset_res = {
}
val merge_results :
?fna_infos : text_file workflow option ->
res_by_tools : result list ->
unit ->
text_file workflow
val plot_merge_results :
......
lib/file_formats.ml
View file @ 5d95464a
......@@ -6,6 +6,11 @@ class type nhx = object
method format : [`nhx]
end
class type nw = object
inherit text_file
method format : [`nw]
end
class type diffsel_tree = object
inherit text_file
method format : [`diffsel_tree]
......
lib/phyml.ml
View file @ 5d95464a
......@@ -2,6 +2,7 @@ open Core
open Bistro.Std
open Bistro.EDSL
open Bistro_bioinfo.Std
open File_formats
type phyml
......@@ -27,5 +28,5 @@ let phyml ~tree phy : phyml directory workflow =
])
]
let phyml_tree ~tree phy =
let phyml_tree ~tree phy : nw workflow =
(phyml ~tree phy) / selector ["tree.phy_phyml_tree.txt"]
lib/pipeline.ml
View file @ 5d95464a
......@@ -253,7 +253,8 @@ let derive_from_dataset ~dataset ~preview ~fast_mode=
let res_by_tools = List.map det_meths ~f:(fun det_meth ->
derive_from_det_meth ~det_meth ~dataset ~preview
) in
let merged_results = merge_results ~res_by_tools in
let fna_infos = dataset.dataset.fna_infos in
let merged_results = merge_results ~fna_infos ~res_by_tools () in
let tsv = merged_results in
let faa = dataset.dataset.faa in
let tree = Tree_dataset.tree dataset.dataset.tree_dataset `Detection in
......
lib/post_analyses.ml
View file @ 5d95464a
......@@ -6,13 +6,16 @@ open Bistro_utils
open File_formats
open Convergence_detection
type plot_trees
type post_analyses_dir
type sens_spe_t_choices_plot
type t_choices = {
t_choices_complete: text_file workflow ;
t_choices_max: text_file workflow ;
t_choices_recall09: text_file workflow ;
t_choices_plot: text_file workflow ;
t_choices_condensed_plot: text_file workflow ;
tree_prefix: string;
}
......@@ -35,12 +38,20 @@ type simu_infos = {
tree_prefix: string ;
}
type reinfered_tree = {
reinfered_tree : nw workflow ;
input_tree : nhx workflow ;
tree_prefix : string ;
model_prefix : string ;
}
type post_analyses_simu = {
simu_infos_l : simu_infos list;
simu_infos_plot : text_file workflow ;
trees_plot : text_file workflow ;
}
let r_env = docker_image ~account:"carinerey" ~name:"r_basics" ~tag:"07232018" ()
let r_env = docker_image ~account:"carinerey" ~name:"r_basics" ~tag:"08012018" ()
let is_hyp ~hyp (dataset_results :dataset_res) =
let model_prefix = dataset_results.model_prefix in
......@@ -51,7 +62,7 @@ let build_cmd_t_choices (opt_name : string) mr_option =
| Some x -> [opt opt_name dep x]
| None -> []
let make_t_choices ~h0_mr ~h0_NeBig_mr ~h0_NeSmall_mr ~haPCOC_mr ~haPC_mr ~haPC_NeBig_mr ~haPC_NeSmall_mr ~h0_NeBigInSmall_mr
let make_t_choices ~h0_mr ~h0_NeBig_mr ~h0_NeSmall_mr ~haPCOC_mr ~haPC_mr ~haPC_NeBig_mr ~haPC_NeSmall_mr ~h0_NeBigInSmall_mr
~h0_NeSmallInBig_mr ~haPC_NeBigInSmall_mr ~haPC_NeSmallInBig_mr () : post_analyses_dir directory workflow =
let env = r_env in
let out = dest // "out" in
......@@ -75,7 +86,8 @@ let make_t_choices ~h0_mr ~h0_NeBig_mr ~h0_NeSmall_mr ~haPCOC_mr ~haPC_mr ~haPC
mkdir_p dest ;
cmd "Rscript" ([
[file_dump (string Scripts.calc_t_per_meth) ;
opt "--out " ident out; ] ;
opt "--out " ident out;
] ;
cmd_mr ;
] |> List.concat) ;
])
......@@ -118,6 +130,29 @@ let group_simu_infos ~simu_infos_l : simu_infos directory workflow =
)
]
let plot_trees ~reinfered_tree_l : plot_trees directory workflow =
let env = r_env in
let cmd_cp_l = List.map reinfered_tree_l ~f:(fun rt -> [
cmd "cp" [dep rt.reinfered_tree ; tmp // (rt.tree_prefix ^"@"^ rt.model_prefix ^ ".nw")];
cmd "cp" [dep rt.input_tree ; tmp // (rt.tree_prefix ^"@input_tree.nw")]
]) |> List.concat
in
let out = dest // "out" in
workflow ~descr:"post_analyses.plot_trees" [
docker env (
and_list ([
[mkdir_p dest];
[mkdir_p tmp ];
cmd_cp_l;
[cmd "Rscript" [
file_dump (string Scripts.plot_trees) ;
opt "--input_dir" ident tmp;
opt "--out " ident out;
];]
] |> List.concat)
)
]
let get_merged_results_opt hx = match hx with
| Some w -> Some w.merged_results
| None -> None
......@@ -176,11 +211,13 @@ let get_t_choices ~(dataset_results_l: dataset_res list) : t_choices option =
| (Some h0, Some _) ->
let t_choices_dir = make_t_choices_per_couple {h0_res; h0_NeBig_res; h0_NeSmall_res; ha_PC_res; ha_PCOC_res; ha_PC_NeBig_res ; ha_PC_NeSmall_res;
h0_NeBigInSmall_res; h0_NeSmallInBig_res; ha_PC_NeBigInSmall_res; ha_PC_NeSmallInBig_res} in
let t_choices_max = t_choices_dir / selector ["out.recall09_per_meth.tsv"] in
let t_choices_max = t_choices_dir / selector ["out.max_MCC_per_meth.tsv"] in
let t_choices_recall09 = t_choices_dir / selector ["out.recall09_per_meth.tsv"] in
let t_choices_complete = t_choices_dir / selector ["out.complete.tsv"] in
let t_choices_plot = t_choices_dir / selector ["out.pdf"] in
let t_choices_condensed_plot = t_choices_dir / selector ["out_condensed.pdf"] in
let tree_prefix = h0.tree_prefix in
Some {t_choices_max; t_choices_complete ; t_choices_plot; tree_prefix}
Some {t_choices_max; t_choices_recall09; t_choices_complete ; t_choices_plot; t_choices_condensed_plot; tree_prefix}
| _ -> None
......@@ -227,8 +264,19 @@ let post_analyses_simu_of_simu_dataset_l ~simu_dataset_l =
model_prefix = dataset.model_prefix
}
) in
let reinfered_tree_l = List.map simu_dataset_l ~f:(fun dataset ->
let rd = dataset.dataset in
let phy = Bppsuite.fa2phy rd.fna in
let input_tree = rd.input_tree in
let reinfered_tree = Phyml.phyml_tree ~tree:input_tree phy in
{reinfered_tree; input_tree;
tree_prefix = dataset.tree_prefix ;
model_prefix = dataset.model_prefix
}
) in
let simu_infos_plot = group_simu_infos ~simu_infos_l / selector ["out.pdf"] in
{simu_infos_l; simu_infos_plot}
let trees_plot = plot_trees ~reinfered_tree_l / selector ["out.pdf"] in
{simu_infos_l; simu_infos_plot; trees_plot}
......@@ -287,7 +335,8 @@ let repo_post_analyses_all_trees_of_all_post_analyses_per_tree ~profile_prefix ~
let repo_of_post_analyses_simu ~post_analyses_simu =
[
Repo.[
item ["hypothesis_validation.pdf"] post_analyses_simu.simu_infos_plot
item ["hypothesis_validation.pdf"] post_analyses_simu.simu_infos_plot ;
item ["trees_validation.pdf"] post_analyses_simu.trees_plot;
] |> Repo.shift "simu_infos"
;
(List.map post_analyses_simu.simu_infos_l ~f:(fun simu_infos ->
......@@ -308,11 +357,13 @@ let repo_of_post_analyses_res ~prefix ~post_analyses_res =
| Some w ->
Repo.[
item [prefix ^ ".t_choices.max_mcc_per_meth.tsv"] w.t_choices_max ;
item [prefix ^ ".t_choices.recall09_per_meth.tsv"] w.t_choices_recall09 ;
item [prefix ^ ".t_choices.complete.tsv"] w.t_choices_complete ;
item [prefix ^ ".t_choices.pdf"] w.t_choices_plot ;
item [prefix ^ ".t_choices.condensed.pdf"] w.t_choices_condensed_plot ;
] |> Repo.shift "t_choices"
);
(
(*(
match post_analyses_res.auto_t_plot_l with
| None -> []
| Some w_l ->
......@@ -323,6 +374,6 @@ let repo_of_post_analyses_res ~prefix ~post_analyses_res =
]
)|> List.concat
|> Repo.shift "auto_t_pdf"
);
);*)
] |> List.concat
lib/scripts/calc_t_per_meth.R
View file @ 5d95464a
......@@ -37,6 +37,71 @@ if (is.null(opt$HaPCOC)){
stop("At least one argument must be supplied (HaPCOC input file)", call.=FALSE)
}
########################################################################
print("prep input files")
read_hyp = function(opt_name) {
if (! is.null(opt_name)){
df = read.table(opt_name, header=TRUE, sep = '\t',na.strings = "NA")
df = df[, ! colnames(df) %in% c("Indel_prop", "Indel_prop.ConvLeaves.")]
id_vars = if ("P_distance" %in% colnames(df)) {c("Sites", "P_distance")} else {c("Sites")}
df_melt = melt(df, id.vars = id_vars, variable.name = "methode")
return(df_melt)
} else {
return(NULL)
}
}
df_H0_melt = read_hyp(opt$H0)
df_H0NeBig_melt = read_hyp(opt$H0NeBig)
df_H0NeSmall_melt = read_hyp(opt$H0NeSmall)
df_H0NeBigInSmall_melt = read_hyp(opt$H0NeBigInSmall)
df_H0NeSmallInBig_melt = read_hyp(opt$H0NeSmallInBig)
df_HaPCOC_melt = read_hyp(opt$HaPCOC)
df_HaPC_melt = read_hyp(opt$HaPC)
df_HaPCNeBig_melt = read_hyp(opt$HaPCNeBig)
df_HaPCNeSmall_melt = read_hyp(opt$HaPCNeSmall)
df_HaPCNeBigInSmall_melt = read_hyp(opt$HaPCNeBigInSmall)
df_HaPCNeSmallInBig_melt = read_hyp(opt$HaPCNeSmallInBig)
########################################################################
print("prep df_d")
build_df_dist_couple = function (df_h0,df_ha,name) {
if ((! is.null(df_h0)) & (! is.null(df_ha))) {
df = merge(df_h0, df_ha, by = c("Sites","P_distance", "methode"), suffix = c("_H0","_Ha"))
df_melt = melt(df, id.vars = c("Sites","P_distance", "methode"), variable.name = "val_H0Ha")
df_melt$couple = name
} else {
df_melt = NULL
}
return(df_melt)
}
df_d_H0HaPC = build_df_dist_couple(df_H0_melt, df_HaPC_melt, "H0/HaPC")
df_d_H0HaPC_NeBig = build_df_dist_couple(df_H0NeBig_melt, df_HaPCNeBig_melt, "H0/HaPC NeBig")
df_d_H0HaPC_NeSmall = build_df_dist_couple(df_H0NeSmall_melt, df_HaPCNeSmall_melt, "H0/HaPC NeSmall")
df_d_H0HaPC_NeBigInSmall = build_df_dist_couple(df_H0NeBigInSmall_melt, df_HaPCNeBigInSmall_melt, "H0/HaPC NeBigInSmall")
df_d_H0HaPC_NeSmallInBig = build_df_dist_couple(df_H0NeSmallInBig_melt, df_HaPCNeSmallInBig_melt, "H0/HaPC NeSmallInBig")
df_d_H0HaPCOC = build_df_dist_couple(df_H0_melt, df_HaPCOC_melt, "H0/HaPCOC")
df_d = rbind.data.frame(df_d_H0HaPC, df_d_H0HaPCOC,df_d_H0HaPC_NeBig,df_d_H0HaPC_NeSmall,
df_d_H0HaPC_NeBigInSmall,df_d_H0HaPC_NeSmallInBig)
df_d = df_d[order(df_d$methode),]
print(head(df_d))
print(tail(df_d))
########################################################################
print("prep df")
## fun...
calc_TN_FP = function(vals, t){
......@@ -62,45 +127,18 @@ calc_TP_FN = function(vals, t){
}
calc_TN_FP_TP_FN = function(t, df_H0_melt, df_Ha_melt){
df_TN_FP = do.call(rbind.data.frame,tapply(df_H0_melt$value, df_H0_melt$variable, calc_TN_FP, t=t))
df_TP_FN = do.call(rbind.data.frame,tapply(df_Ha_melt$value, df_H0_melt$variable, calc_TP_FN, t=t))
df_TN_FP = do.call(rbind.data.frame,tapply(df_H0_melt$value, df_H0_melt$methode, calc_TN_FP, t=t))
df_TP_FN = do.call(rbind.data.frame,tapply(df_Ha_melt$value, df_H0_melt$methode, calc_TP_FN, t=t))
df_TN_FP_TP_FN = merge(df_TN_FP, df_TP_FN, by=0)
df_TN_FP_TP_FN$t = t
return(df_TN_FP_TP_FN)
}
## program...
read_hyp = function(opt_name) {
if (! is.null(opt_name)){
df = read.table(opt_name, header=TRUE, sep = '\t',na.strings = "NA")
df = df[, ! colnames(df) %in% c("Indel_prop", "Indel_prop.ConvLeaves.")]
df_melt = melt(df, id.vars = c("Sites"))
return(df_melt)
} else {
return(NULL)
}
}
df_H0_melt = read_hyp(opt$H0)
df_H0NeBig_melt = read_hyp(opt$H0NeBig)
df_H0NeSmall_melt = read_hyp(opt$H0NeSmall)
df_H0NeBigInSmall_melt = read_hyp(opt$H0NeBigInSmall)
df_H0NeSmallInBig_melt = read_hyp(opt$H0NeSmallInBig)
df_HaPCOC_melt = read_hyp(opt$HaPCOC)
df_HaPC_melt = read_hyp(opt$HaPC)
df_HaPCNeBig_melt = read_hyp(opt$HaPCNeBig)
df_HaPCNeSmall_melt = read_hyp(opt$HaPCNeSmall)
df_HaPCNeBigInSmall_melt = read_hyp(opt$HaPCNeBigInSmall)
df_HaPCNeSmallInBig_melt = read_hyp(opt$HaPCNeSmallInBig)
build_df_couple = function (df_h0,df_ha,name) {
if ((! is.null(df_h0)) & (! is.null(df_ha))) {
df = do.call(rbind.data.frame,lapply(seq(0,0.999,0.001), calc_TN_FP_TP_FN, df_H0_melt = df_h0, df_Ha_melt = df_ha))
df = do.call(rbind.data.frame,lapply(seq(0,1,0.01), calc_TN_FP_TP_FN, df_H0_melt = df_h0, df_Ha_melt = df_ha))
df$couple = name
} else {
df = NULL
......@@ -121,7 +159,9 @@ df = rbind.data.frame(df_H0HaPC, df_H0HaPCOC,df_H0HaPC_NeBig,df_H0HaPC_NeSmall,
print(head(df))
print(tail(df))
## Sensitivity (= recall)
## Sensitivity (= recall)
df$sens = df$TP / (df$TP+df$FN)
df$sens[is.na(df$sens)] = 0
......@@ -133,9 +173,9 @@ df$spe[is.na(df$spe)] = 0
n_sites = sum(df[1,c("TP","FN")])
p = 140 / n_sites
n = (6000 -140)/ n_sites
df$FP_2 = df$FP * n
df$FP_2 = df$FP * n
df$TP_2 = df$TP * p
df$FN_2 = df$FN * p
df$FN_2 = df$FN * p
df$TN_2 = df$TN * n
df$mcc = (df$TP_2 * df$TN_2 - df$FP_2 * df$FN_2) / sqrt((df$TP_2+df$FP_2)*(df$TP_2+df$FN_2)*(df$TN_2+df$FP_2)*(df$TN_2+df$FN_2))
......@@ -156,9 +196,7 @@ df_out = df_out[order(df_out$methode),]
print(summary(df_out))
df_out_melt = melt(df_out, id.vars = c("couple", "methode","threshold"))
########################################################################
print("prep plot max mcc")
df_max_mcc_per_method = do.call(rbind, lapply(split(df_out,paste0(df_out$methode,df_out$couple)),
......@@ -170,91 +208,155 @@ print(df_max_mcc_per_method)
df_max_mcc_per_method_2 = df_max_mcc_per_method
df_max_mcc_per_method_2$variable="MCC"
########################################################################
print("prep plot recall_precision_per_meth")
df_recall_sup09_per_meth = do.call(rbind, lapply(split(df_out,paste0(df_out$methode,df_out$couple)),
function(x) {
print(x)
x = x[x$precision > 0.9,]
if (nrow(x) > 0) {
print(x)
return(x[which.max(x$sensitivity),c("couple","methode", "threshold", "MCC","sensitivity","specificity","precision")])
x2 = x[x$precision > 0.9,]
if (nrow(x2) > 0) {
return(x2[which.max(x2$sensitivity),c("couple","methode", "threshold", "MCC","sensitivity","specificity","precision")])
} else {
return()
x = x[1,c("couple","methode", "threshold", "MCC","sensitivity","specificity","precision")]
print(x)
x[,c("threshold", "MCC","sensitivity","specificity","precision")] = NA
return(x)
}
}))
print(df_recall_sup09_per_meth)
########################################################################
print("plot recall_precision_per_meth")
alpha = 0.5
plot = ggplot(df_out, aes(x=sensitivity, y=precision, col = couple))
plot = plot + theme_bw()
plot = plot + labs(x="Sensitivity ( = Recall)", y="Precision")
plot = plot + ylim(c(0,1)) + xlim(c(0,1))
plot = plot + guides(fill=FALSE) + guides(col=FALSE)
plot = plot + scale_color_brewer(palette="Set1")
plot = plot + geom_point(size=0.5)
plot = plot + geom_line()
plot = plot + geom_hline( aes(yintercept = 0.9), col="black" , alpha=alpha, show.legend = NA,linetype="dotted")
plot = plot + facet_grid(couple ~ methode)
plot_recall_precision = plot
plot_out = function(df_out, df_d , df_recall_sup09_per_meth, meths = NULL, suffix="") {
nb_c = length(unique(df_out$couple))
colors = c(c("#984EA3","#4AA947","#377EB8","#E41A1C","#F5BE5B","#90EE90")[1:nb_c], c("#7F7F7F","#ADD8E6"))
save_plot(paste0(opt$out,".recall_precision_per_meth.pdf"),
plot_recall_precision,
ncol = 0.4* length(unique(df_out_melt$methode)),
nrow = 0.4,
base_aspect_ratio = 1,
limitsize = FALSE
)
if (! is.null(meths)) {
print("plot per indicator")
x_labs = "Threshold"
y_labs = ""
plot = ggplot(df_out_melt, aes(x=threshold, y=value, col=couple))
plot = plot + theme_bw()
plot = plot + theme(legend.position="top")
plot = plot + guides(fill=FALSE) + theme(legend.position="top")
plot = plot + labs(x=x_labs, y=y_labs) #+ ylim(y_lim)
plot = plot + geom_point(size=0.5)
plot = plot + scale_color_brewer(palette="Set1")
#plot = plot + geom_hline( data = df_max_mcc_per_method_2, aes(yintercept = MCC), alpha=alpha, show.legend = NA)
plot = plot + geom_vline( data = df_recall_sup09_per_meth, aes(xintercept = threshold, col=couple), alpha=alpha, show.legend = NA,linetype="dotted")
plot = plot + facet_grid(variable ~ methode, scales="free")
plot_max_MCC = plot
save_plot(paste0(opt$out,".max_MCC_per_meth.pdf"),
plot_max_MCC,
ncol = 0.4 * length(unique(df_out_melt$methode)),
nrow = 1.7,
base_aspect_ratio = 1.5,
limitsize = FALSE
)
plot = plot_grid(plot_recall_precision,plot_max_MCC,
labels = c("A", "B"),
rel_heights = c(length(unique(df_out$couple))*0.8,3),
nrow=2)
df_out = df_out[df_out$methode %in%meths,]
df_d = df_d[df_d$methode %in%meths,]
df_recall_sup09_per_meth = df_recall_sup09_per_meth[df_recall_sup09_per_meth$methode %in%meths,]
} else {
meths = sort(unique(df_out$methode))
}
save_plot(paste0(opt$out,".pdf"),
plot,
ncol = 0.4* length(unique(df_out_melt$methode)),
nrow = length(unique(df_out$couple)) * 0.5 + 1,
base_aspect_ratio = 1,
limitsize = FALSE
)
alpha = 0.5
df_out$methode = factor(df_out$methode, levels = meths)
df_recall_sup09_per_meth$methode = factor(df_recall_sup09_per_meth$methode, levels = meths)
df_d$methode = factor(df_d$methode, levels = meths)
couple_levels = unique(df_out$couple)
couple_levels = c(couple_levels[couple_levels != "H0/HaPCOC"] , "H0/HaPCOC")
df_out$couple = factor(df_out$couple, levels = couple_levels)
df_d$couple = factor(df_d$couple, levels = couple_levels)
df_recall_sup09_per_meth$couple = factor(df_recall_sup09_per_meth$couple, levels = couple_levels )
df_out_melt = melt(df_out, id.vars = c("couple", "methode","threshold"))
df_out_melt$methode = factor(df_out_melt$methode, levels = meths)
df_out_melt$variable = factor(df_out_melt$variable, levels = c("sensitivity", "specificity", "precision", "MCC"))
df_out_melt$couple = factor(df_out_melt$couple, levels = couple_levels)
print("plot recall_precision_per_meth")
plot = ggplot(df_out, aes(x=sensitivity, y=precision, col = couple))
plot = plot + theme_bw()
plot = plot + labs(x="Sensitivity (= Recall)", y="Precision")
plot = plot + theme(legend.position="top")
plot = plot + ylim(c(0,1)) + xlim(c(0,1))
plot = plot + guides(fill=FALSE) + guides(col=FALSE)
plot = plot + scale_color_manual(values=colors)
plot = plot + geom_point(size=0.5, alpha=alpha)
plot = plot + geom_line()
plot = plot + geom_hline( aes(yintercept = 0.9), col="black" , size = 1, show.legend = NA,linetype="dashed")
plot = plot + facet_grid(couple ~ methode)
plot = plot + theme(axis.text.x = element_text(angle = 45, hjust = 1))
plot_recall_precision = plot
save_plot(paste0(opt$out,suffix,".recall_precision_per_meth.pdf"),
plot_recall_precision,
ncol = 0.4* length(unique(df_out_melt$methode)),
nrow = 0.4* length(unique(df_out_melt$couple)),
base_aspect_ratio = 1,
limitsize = FALSE
)
print("plot per indicator")
x_labs = "Threshold"
y_labs = ""
plot = ggplot(df_out_melt, aes(x=threshold, y=value, col=couple))
plot = plot + theme_bw()
plot = plot + theme(legend.position="top")
plot = plot + guides(fill=FALSE) + theme(legend.position="top")
plot = plot + labs(x=x_labs, y=y_labs) #+ ylim(y_lim)
plot = plot + geom_point(size=0.5, alpha=alpha)
plot = plot + scale_color_manual(values=colors)
#plot = plot + geom_hline( data = df_max_mcc_per_method_2, aes(yintercept = MCC), alpha=alpha, show.legend = NA)
plot = plot + geom_vline( data = df_recall_sup09_per_meth, aes(xintercept = threshold, col=couple), size = 1, show.legend = NA,linetype="dashed")
plot = plot + facet_grid(variable ~ methode, scales="free")
plot = plot + theme(axis.text.x = element_text(angle = 45, hjust = 1))
plot_max_MCC = plot
save_plot(paste0(opt$out,suffix,".max_MCC_per_meth.pdf"),
plot_max_MCC,
ncol = 0.4 * length(unique(df_out_melt$methode)),
nrow = 1.7,
base_aspect_ratio = 1.5,
limitsize = FALSE
)
print("plot value/distance")
plot = ggplot(df_d, aes(y=P_distance, x=value, shape = val_H0Ha, col = val_H0Ha))
plot = plot + theme_bw()
plot = plot + theme(legend.position="top")
plot = plot + labs(y="P distance", x="Value")
plot = plot + xlim(c(0,1)) + ylim(c(0, max(max(df_d$P_distance), 1)))
plot = plot + guides(col=FALSE)
plot = plot + scale_color_manual(values=colors)
plot = plot