Change the figure size.

Figure2.R

@@ -198,22 +198,22 @@ breast_cancer_num_pro_miss_plot = ggplot(breast_cancer_num_pro_miss, aes(x=metho
   ylab("# Proteins (x1000)") +
   xlab("Method") +
   theme_light() +
-  labs(fill= "Non-missing\nvalue filter") +
+  labs(fill= "Completeness") +
   theme(axis.text.x = element_text(angle = 0, vjust = 1, hjust=0.5, size = 5),
         axis.text.y = element_text(size = 5),
         axis.title = element_text(size = 5),
         panel.border = element_blank(), panel.grid.major = element_blank(),
         panel.grid.minor = element_blank(), axis.line = element_line(colour = "black", size = 0.05),
-        legend.position = "right",
+        legend.position = "bottom",
         legend.title = element_text(size=5, face="bold"),
         legend.text = element_text(size = 5),
-        legend.key.size = unit(0.2, "cm"),
+        legend.key.size = unit(0.1, "cm"),
         strip.background =element_rect(fill="white"),
         strip.text = element_text(size=6, colour = 'black')) +
   facet_grid(~type, scales="free", space = "free")
 breast_cancer_num_pro_miss_plot
 
 breast_cancer_plot_all= ggarrange(breast_cancer_pg_dis_plot, breast_cancer_num_pro_miss_plot,
                        ncol = 1, nrow = 2, align="v", labels = c("a", "b"), font.label = list(size = 10))
-ggsave("./figures/Figure2.pdf", breast_cancer_plot_all, width=4, height = 3, units = c("in"), dpi=400)
+ggsave("./figures/Figure2.pdf", breast_cancer_plot_all, width=88, height = 100, units = c("mm"), dpi=400)
 

Figure4.R

@@ -198,7 +198,8 @@ plasma_feature_plot = ggplot(feature_data_all, aes(x=condition, y=quant_log, col
   geom_point(position=position_jitterdodge(0.1), size=0.1, alpha=0.6)+
   scale_x_discrete(labels=c('NSCLC', 'Control')) + 
   scale_color_manual(values = c("#E54D37", "#5CBED3")) +
-  stat_compare_means(label = "p.signif", method = "t.test", label.x = 1.5 , label.y = 13, size=2)+
+  stat_compare_means(aes(label = paste0('p = ', after_stat(p.format), '\n',
+                                    after_stat(p.signif))), method = "t.test", label.x = 1.5 , label.y = 13, size=1.8)+
   ylab("Protein Abundance (log2)") +
   xlab("Group") +
   theme_light() +

Figure7.R

@@ -1,10 +1,17 @@
+#install.packages('reticulate')
+#reticulate::install_miniconda()
+#reticulate::conda_install('r-reticulate', 'python-kaleido')
+#reticulate::conda_install('r-reticulate', 'plotly', channel = 'plotly')
+#reticulate::use_miniconda('r-reticulate')
+
 library(tidyverse)
 library(data.table)
 library(ComplexHeatmap)
 library(psych)
 library(plotly)
 library(eulerr)
 library(ggrepel)
+library(reticulate)
 
 read_maxlfq = function(file_path){
   out_data = fread(file_path) %>%
@@ -281,14 +288,16 @@ plot_pca = function(imputed_data, annotation_data, padding){
                         yaxis = list(title = list(text=paste('PC2=', summary(pc_comp)$importance[2,][2][[1]]*100, "%", sep=""), font=list(size=15)), 
                                      tickfont = list(size = 6)),
                         zaxis = list(title = list(text=paste('PC3=', summary(pc_comp)$importance[2,][3][[1]]*100, "%", sep=""), font=list(size=15)), 
-                                     tickfont = list(size = 6))))
+                                     tickfont = list(size = 6)),
+                        camera = list(eye = list(x = -1, y = -2.4, z = 1.5))))
 
   return(fig)
 }
 fig4_lib_exp_over_anno = fread("./lowInputData/Direct_lib_result/experiment_annotation_over.tsv")
 
 fig3_lib_imputed_table = fread("./lowInputData/Tonsil_lib_result/fragpipe_analyst/Imputed_matrix.csv")
 fig3_lib_pca = plot_pca(fig3_lib_imputed_table, fig4_lib_exp_over_anno, "30")
+fig3_lib_pca
 
 ### Imputed_matrix.csv download from FragPipe-analysist.
 fig4_lib_imputed_table = fread("./lowInputData/Direct_lib_result/fragpipe_analyst/Imputed_matrix.csv")
@@ -299,6 +308,10 @@ fig4_lib_imputed_table_passed = fig4_lib_imputed_table %>%
 
 fig4_lib_pca = plot_pca(fig4_lib_imputed_table_passed, fig4_lib_exp_over_anno, "")
 fig4_lib_pca # PCA plot save manually since need to modify 3D position.
+reticulate::import("sys")
+reticulate::import("plotly")
+save_image(fig4_lib_pca, "./figures/pca.pdf", scale=2)
+
 
 # gene expression non_imputed
 get_expre_one_gene = function(report_data, class_file, gene_name_list){