pcaplot function in DESeq2

####################################################################
# Load a count data

countsTable <- read.csv("counts.csv",row.names=1, header=T)
head(countsTable)
dim(countsTable)

####################################################################
# run DESeq2

conds<- factor(c(rep("SETDB1_High",20),rep("SETDB1_Low",20)))
coldat=DataFrame(conds=factor(conds))

dds <- DESeqDataSetFromMatrix(cnts_noZero, colData=coldat, design = ~ conds)
dds <- DESeq(dds, fitType = "mean")

####################################################################
# plots for PCA results

rld <- rlogTransformation(dds,fitType = "mean")

# automatic 'pcaplot' function
#pcaplot(rld,intgroup=c("conds"),ntop=500, text_labels=F, title="PCA",pcX=1,pcY=2,point_size=3,ellipse=F)


# manual plotting
pca <- prcomp(t(assay(rld)))
percentVar <- pca$sdev^2/sum(pca$sdev^2)
df <- data.frame(PC1 = pca$x[, 1], PC2 = pca$x[, 2], group = conds, t(setdb1))
colnames(df) <-c("PC1","PC2","conds","Setdb1")

tiff("PCAplot.tif",units = "px",width = 400, height = 300)
ggplot(data = df, aes_string(x = "PC1", y = "PC2", size="Setdb1", color="Setdb1")) + 
  geom_point(alpha=.8) + theme_bw() + 
  xlab(paste0("PC1: ", round(percentVar[1] * 100), "% variance")) + 
  ylab(paste0("PC2: ", round(percentVar[2] * 100), "% variance")) +
  scale_colour_gradient2(midpoint=40,low="green",mid="grey60",high="red", space = "Lab", na.value = "grey50", guide = "colourbar")
dev.off()
#