I stabilize my data with rlog and then plot PCA with the DESeq proposed method. 

dds = DESeqDataSetFromMatrix(countData=histone_m, colData=Design, design=~condition)
cds=estimateSizeFactors(dds) 
met = rlog(dds)     
data <- plotPCA(met, intgroup=c("condition"), returnData=TRUE)
percentVar <- round(100 * attr(data, "percentVar"))
myPlot = ggplot(data, aes(PC1, PC2, color=name)) +
       geom_point(size=3) +
        xlab(paste0("PC1: ",percentVar[1],"% variance")) +
        ylab(paste0("PC2: ",percentVar[2],"% variance"))
      
        
 
Then I decided to do a normal pca from r:     

met =assay(met)
pca<- prcomp(t(met))
screeplot_percent(pca)
col=c("red","pink","black","blue")
idx <- seq_len(3)
print(splom(pca$x[,idx], col=col,pch=19))
 

Function for screeplot:

screeplot_percent <- function(x, npcs = min(10, length(x$sdev)), ...) {
  idx <- seq_len(npcs)
  sum_var <- sum(x$sdev ^ 2)
  vars <- 100 * (x$sdev[idx] ^ 2 / sum_var)
  cumvar <- cumsum(vars)
  
  barplot(vars, width = 0.9, space = 0.1, names.arg = idx, ylim = c(0, 100),
          xlab = "Principal Component", ylab = "Percent Variance",
          xaxp = c(1, npcs, npcs - 1), las = 1)
  lines(x = idx - 0.5, y = cumvar, type = "b", lty = 2)
  legend("bottomright", legend = c("Proportion", "Cumulative"), lty = c(1, 2),
         pch = c(19, 1))
}

red -> WEN1
pink -> WEN3
black -> WNN1
blue -> WNN3

In fact PCA plots differ a bit and screeplot differ a lot. However, I do not understand why. 
As you see WEN3 and WNN3 are quite apart on the second plot in comparison to the first plot. Additionally, the scale on the y-axis and x-axis is different. What is the reason for this?

Also my screeplot tells me that PC1 explains app. 75% of variance whereas ggplot claims 87%.