cleanup

Figures_manuscript/scripts/Fig_SUP17_BMI_pred_new_loci.R

@@ -2,8 +2,8 @@ library(data.table)
 library(plotROC)
 library(cowplot)
 
-Berisa = fread("Y:\\5._ARTICLE_DISCOVERABILITY\\Figures\\inputs\\berisa_region.bed")
-JASS_BMI = fread("Y:\\5._ARTICLE_DISCOVERABILITY\\Figures\\inputs\\BMIanalysis\\Evaluation_new_associations_BMI_with_allSNPs.tsv")
+Berisa = fread("../inputs/berisa_region.bed")
+JASS_BMI = fread("../inputs/BMIanalysis/Evaluation_new_associations_BMI_with_allSNPs.tsv")
 
 JASS_BMI = cbind(JASS_BMI,Berisa)
 JASS_BMI = JASS_BMI[!apply(is.na(JASS_BMI), 1, any),]
@@ -41,7 +41,7 @@ p_ROC = p_ROC + annotate("text", y=rev(seq(0.45,0.6,0.05)) ,x=0.6, label=c("AUC"
 
 
 
-png("Y:\\5._ARTICLE_DISCOVERABILITY\\Figures\\outputs\\Fig_SUP_BMI_pred_new_loci.png", 
+png("../outputs/Fig_SUP_BMI_pred_new_loci.png", 
     width = 8, height = 8, units     = "in",
     res       = 300,
     pointsize = 4)

Figures_manuscript/scripts/Fig_SUP2_residual_covariance.R

@@ -3,20 +3,20 @@ library(ggplot2)
 library(cowplot)
 library("stringr")
 
-trait_72 = fread("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\inputs\\72trait_data_2023-07-07.csv")
+trait_72 = fread("../inputs/72trait_data_2023-07-07.csv")
 #trait_72 = fread("V:\\5._ARTICLE_DISCOVERABILITY\\Tables\\Supp\\Output\\72trait_data_2023-07-07.csv")
 head(trait_72)
 trait_72 = trait_72[!is.na(trait_72[,`log10_mes_semilogadjust`]),]
 setkey(trait_72, "ID")
 
-Rho = fread("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\inputs\\COV_H0.csv")
+Rho = fread("../inputs/COV_H0.csv")
 setkey(Rho, "V1")
 Rho = Rho[trait_72$ID , c("V1",trait_72$ID), with=FALSE]
 res_dist = as.dist(1- Rho[trait_72$ID , trait_72$ID, with=FALSE])
 res_dist[is.na(res_dist)] = 1
 
 
-Rho_gen = fread("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\inputs\\Correlation_matrix_genetic.csv")
+Rho_gen = fread("../inputs/Correlation_matrix_genetic.csv")
 setkey(Rho_gen, "V1")
 Rho_gen = Rho_gen[trait_72$ID , c("V1",trait_72$ID), with=FALSE]
 gen_dist = as.dist(1- abs(Rho_gen[trait_72$ID, trait_72$ID, with=FALSE]))
@@ -50,7 +50,7 @@ for(tr1 in 1:72){
   }
 }
 
-png("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\outputs\\Fig_sup_residual_covariance.png",width = 20, height    = 20,
+png("../outputs/Fig_sup_residual_covariance.png",width = 20, height    = 20,
   units     = "in",
   res       = 300,
   pointsize = 4)

Figures_manuscript/scripts/Fig_SUPnote3_stratification_illustration.R

@@ -31,6 +31,6 @@ p_distrib = p_distrib + geom_density(data=xuf, linewidth=1.1,aes(x=X)) +theme_mi
 p_distrib = p_distrib + scale_color_manual(name= "", label = c("Feature", "mean of the feature (random sets)", "mean of the feature (stratified sets)"),values=c("#E94F37", "#C0A04E", "#111D4A")) 
 
 
-png("Y:\\5._ARTICLE_DISCOVERABILITY\\Figures\\outputs\\Fig_SUPP_illustration_stratification.png", width=500, height=500)
+png("../outputs/Fig_SUPP_illustration_stratification.png", width=500, height=500)
 p_distrib
 dev.off()

Figures_manuscript/scripts/Fig_SUPnote4_simulation_test_boundaries.R

@@ -102,7 +102,7 @@ for(hypothesis in names(BG_mat)){
         rho_ov= cor(Y1, Y2) * overlap # cor(Y1,Y2)=rho (rho=pe+pg); overlap=NS/N (fraction of overlapping samples out of all samples)
 
         D = data.frame(matrix(c(Y1, Y2), ncol = 2))
-        png(paste0("/pasteur/zeus/projets/p02/GGS_JASS/5._ARTICLE_DISCOVERABILITY/Figures/outputs/simulations/Phenotype_", hypothesis,"_ov_",overlap,".png"))
+        png(paste0("../outputs/simulations/Phenotype_", hypothesis,"_ov_",overlap,".png"))
           p = ggplot(D, aes(x=Y1, y=Y2)) + geom_point()
           print(p)
         dev.off()
@@ -112,7 +112,7 @@ for(hypothesis in names(BG_mat)){
 
         Beta_est = data.frame(B1 = Beta_estimate1, B2=Beta_estimate2)
 
-        png(paste0("/pasteur/zeus/projets/p02/GGS_JASS/5._ARTICLE_DISCOVERABILITY/Figures/outputs/simulations/Beta_", hypothesis,"_ov_",overlap,".png"))
+        png(paste0("../outputs/simulations/Beta_", hypothesis,"_ov_",overlap,".png"))
           print(ggplot(Beta_est, aes(x=B1, y=B2)) + geom_point())
         dev.off()
         Z = Beta_est
@@ -122,7 +122,7 @@ for(hypothesis in names(BG_mat)){
         Z = as.data.frame(Z)
         names(Z) = c("z1", "z2")
 
-        png(paste0("/pasteur/zeus/projets/p02/GGS_JASS/5._ARTICLE_DISCOVERABILITY/Figures/outputs/simulations/Z_", hypothesis,"_ov_",overlap,".png"))
+        png(paste0("../outputs/simulations/Z_", hypothesis,"_ov_",overlap,".png"))
           print(ggplot(Z, aes(x=z1, y = z2)) + geom_point(alpha=0.5, size=1, color="blue"))
         dev.off()
         #
@@ -151,15 +151,15 @@ for(hypothesis in names(BG_mat)){
 
 	range_z <- max(abs(Z$z1),abs(Z$z2))
 
-        write.table(Z, paste0("/pasteur/zeus/projets/p02/GGS_JASS/5._ARTICLE_DISCOVERABILITY/Figures/outputs/simulations/Z_scores_", hypothesis, "_ov_", overlap,"_N_", N,"ce_",ce, ".csv"), sep="\t", row.names=TRUE)
+        write.table(Z, paste0("../outputs/simulations/Z_scores_", hypothesis, "_ov_", overlap,"_N_", N,"ce_",ce, ".csv"), sep="\t", row.names=TRUE)
 
-        png(paste0("/pasteur/zeus/projets/p02/GGS_JASS/5._ARTICLE_DISCOVERABILITY/Figures/outputs/simulations/", hypothesis, "_ov_", overlap,"_N_", N,"ce_",ce, ".png"), width=1500, height=1500, res=300)
+        png(paste0("../outputs/simulations/", hypothesis, "_ov_", overlap,"_N_", N,"ce_",ce, ".png"), width=1500, height=1500, res=300)
           p = ggplot(Z , id.var=c("z1", "z2"), aes(x=z1, y=z2, color=`Significance status`)) + geom_point(size=1.5) + scale_colour_manual(values = c("#3ba3ec","grey", "#f77189","#50b131")) + xlim(-range_z,range_z) + ylim(-range_z,range_z)
            print(p + theme_minimal()+ theme(legend.position = "top", panel.spacing = unit(1.5, "lines"), text=element_text(size=16)) + labs(color="Significance Status"))
         dev.off()
           simu = simu+1
       }
     }
-      write.table(D_pval, paste0("/pasteur/zeus/projets/p02/GGS_JASS/5._ARTICLE_DISCOVERABILITY/Figures/outputs/simulations/", hypothesis, "_ov_", overlap,".csv"), sep="\t", row.names=TRUE)
+      write.table(D_pval, paste0("../outputs/simulations/", hypothesis, "_ov_", overlap,".csv"), sep="\t", row.names=TRUE)
   }
 }

Figures_manuscript/scripts/Figure_2_feature_description_without_duplicates.R

@@ -5,8 +5,7 @@ library(psych)
 library(latex2exp)
 library(scales)
 
-trait_features = fread("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\inputs\\72trait_data_2023-07-07.csv")
-#trait_features = fread("V:\\5._ARTICLE_DISCOVERABILITY\\Tables\\Supp\\Output\\72trait_data_2023-07-07.csv")
+trait_features = fread("../inputs/72trait_data_2023-07-07.csv")
 head(trait_features)
 
 lm_pol_neff <- lm(log10(`polygenicity (unadjusted)`) ~ Neff,data=trait_features)
@@ -78,7 +77,7 @@ pG = pG  + theme(plot.margin = margin(0,0,0.1,0.1, "cm"))
 # set level features
 ###################
 
-set_features = fread("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\inputs\\JASS_5CVdata-2023-08-01\\traitset_jass_5CVcombined_without_duplicates.tsv")
+set_features = fread("../inputs/JASS_5CVdata-2023-08-01/traitset_jass_5CVcombined_without_duplicates.tsv")
 
 
 names(set_features)
@@ -175,7 +174,7 @@ p_corr = p_corr + scale_x_discrete("Var1", labels=label_parse()) + scale_y_discr
 p_corr = p_corr + theme(plot.margin = margin(0,0,0.1,0.1, "cm"),axis.title.x=element_blank(), axis.title.y=element_blank())
 
 
-png("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\outputs\\fig2_feature_description.png",width = 13.5, height    = 16,
+png("../outputs/fig2_feature_description.png",width = 13.5, height    = 16,
   units     = "in",
   res       = 600,
   pointsize = 4)

Figures_manuscript/scripts/Figure_3_jass_gain_vs_features_descriptive_without_duplicates.R

@@ -6,7 +6,7 @@ library(stringr)
 library(latex2exp)
 library(scales)
 
-set_features = fread("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\inputs\\JASS_5CVdata-2023-08-01\\traitset_jass_5CVcombined_without_duplicates.tsv")
+set_features = fread("../inputs/JASS_5CVdata-2023-08-01/traitset_jass_5CVcombined_without_duplicates.tsv")
 
 #../inputs/JASS_5CVdata-2023-08-01/traitset_jass_5CVcombined_without_duplicates.tsv")
 set_features[, percent_increase := 100*((Both+Univariate+Joint) / (Univariate+Both))]
@@ -100,7 +100,7 @@ p_corr = p_corr + scale_x_discrete("Var1", labels=label_parse()) + scale_y_discr
 
 
 
-png("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\outputs\\Fig_3_jass_association_gain_without_duplicates_2.png", width = 9, height = 4,
+png("../outputs/Fig_3_jass_association_gain_without_duplicates_2.png", width = 9, height = 4,
   units     = "in",
   res       = 300,
   pointsize = 4)

Figures_manuscript/scripts/Figure_5_SUP14_JASS_vs_MTAG_without_duplicates.R

@@ -17,8 +17,8 @@ library(cowplot)
 #JASS_res = rbindlist(CV_fold_JASS)
 #MTAG_res = rbindlist(CV_fold_MTAG)
 
-JASS_res = fread("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\inputs\\JASS_5CVdata-2023-08-01\\traitset_jass_5CVcombined_without_duplicates.tsv") 
-MTAG_res = fread("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\inputs\\MTAG_5CVdata-2023-08-01\\traitset_mtag_5CVcombined_without_duplicates.tsv")
+JASS_res = fread("../inputs/JASS_5CVdata-2023-08-01/traitset_jass_5CVcombined_without_duplicates.tsv") 
+MTAG_res = fread("../inputs/MTAG_5CVdata-2023-08-01/traitset_mtag_5CVcombined_without_duplicates.tsv")
 
 setkey(JASS_res, "Id")
 setkey(MTAG_res, "Id")
@@ -73,11 +73,11 @@ p3 = ggplot(frac_JASS_better, aes(x= `# of traits`, y=`Fraction of set where #JA
 p3 = p3 + ylab("Fraction of set where #JASS >\n uncorrected #MTAG") + coord_flip() +theme_minimal() 
 
 
-png("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\outputs\\Figure_5_JASS_vs_MTAG_without_duplicates_HJ.png", width=10, height=5, unit="in", res=300)
+png("../outputs/Figure_5_JASS_vs_MTAG_without_duplicates_HJ.png", width=10, height=5, unit="in", res=300)
     plot_grid(p1,NULL,p2,labels = c('A', "",'B'), rel_widths = c(6,0.1,4), nrow=1, ncol=3)
 dev.off()
 
-png("V:\\5._ARTICLE_DISCOVERABILITY\\Figures\\outputs\\Figure_SUP_JASS_vs_uncorrected_MTAG_without_duplicates_HJ.png", width=10, height=5, unit="in", res=300)
+png("../outputs/Figure_SUP_JASS_vs_uncorrected_MTAG_without_duplicates_HJ.png", width=10, height=5, unit="in", res=300)
     plot_grid(p1c,p3,labels = c('A', 'B'), rel_widths = c(6,4), nrow=1, ncol=2)
 dev.off()