discriminate parents in PCA plot

1-clustering.R

@@ -70,16 +70,16 @@
   pr.out <- prcomp(x = (t(trait.matrix)),  center = T, scale. = F)
   pc.df <- data.frame(pc1 = pr.out$x[, 1], 
                           pc2 = pr.out$x[, 2], 
-                          stage = sample.stage, 
-                          population = c(rep('parent', 16), rep('RIL', 164)))
+                          stage = as.character(sample.stage),
+                          population = substring(colnames(trait.matrix), 1, 3))
   pc.sum <- summary(pr.out)
   pc1.var <- round(pc.sum$importance[2, 'PC1'] * 100, 2)
   pc2.var <- round(pc.sum$importance[2, 'PC2'] * 100, 2)
   
-  pca.all <- ggplot(pc.df, aes(x = pc1, y = pc2, color = factor(stage, level = c('pd', 'ar', 'im', 'rp')))) + 
+  pca.all <- ggplot(pc.df, aes(x = pc1, y = pc2, color = factor(stage, level = c('parent', 'pd', 'ar', 'im', 'rp')))) + 
                             geom_point(aes(shape = population)) +
                             scale_colour_manual(values = c('#ccbb44', '#228833', '#4477aa', '#cc3311')) +
-                            scale_shape_manual(values = c(17, 19)) +
+                            scale_shape_manual(values = c(2, 19, 6)) +
                             labs(x = paste0("PC1 (", pc1.var, "%)"), y = paste0("PC2 (", pc2.var, "%)")) +
                             labs(colour = 'stage') +
                             theme(text = element_text(size = 10),
@@ -101,7 +101,7 @@
        units = 'px',
        res = 300,
        compression = 'lzw')
-  pca.all
+  print(pca.all)
   dev.off()  
   
 # PCA per stage ####