Hi guys,

I wonder if someone has succeeded to apply the plotPCA function to group samples of Mass Spectrometry (MS) based data such as quantitative proteomics. I like much of the hierarchical clustering and PCA ploting functions implemented in DESeq2, but it seems the inputs of these functions are DESeqTransform objects, which is transformed from count-based data. Can someone provide suggestions on how to apply these functions to other types data? Thanks.

All the best,

Zhu

Why bother using the DESEQ functions? It is better to just use prcomp function in R for your PCA plot and ggplot2 - it is more customisable (see example below). Again, for clustering you don't have to use DESEQ, read the NMF aheatmap manual for example, or there are other options - again which would be easier to work with than inbuilt DESEQ functions that are wrapped up in so much other stuff it is harder to customise them.

pca1 = prcomp(data2)
scores <- data.frame(pca1$x)
scores <- cbind(scores, factor(names))
colnames(scores)[ncol(scores)] <- 'type'
myColors <- brewer.pal(9,"Set3")
myColors <- sample(myColors)
names(myColors) <- levels(scores$type)
colScale <- scale_colour_manual(name = "type",values = myColors)

row.names(scores) <- des4$ID

ggplot(data = scores, aes(x = PC1, y = PC2, colour = type, label = rownames(scores))) +
  geom_point(size = 5) +
  theme_bw() +
  theme(axis.title=element_text(size=14,face="bold"),
        axis.text=element_text(size=14),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        legend.title=element_blank(),
        legend.text = element_text(size = 14)) + colScale)