DESeq2 PCA different from Prcomp PCA
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tiago211287 ▴ 10
@tiago211287-9049
Last seen 8 months ago
Brazil

I made a PCA using the rlog matrix from DESEQ and got this plot where one of my sample groups did not group together.

 plotPCA(rld, intgroup=c("condition"))

http://s3.postimg.org/whlalmp6b/Rplot03.png

Using the same matrix in Prcomp from r the samples get more clustered.

cruzi.pca <- prcomp(rldMat2,

                      center = TRUE,
                      scale. = FALSE) 

library(ggbiplot)

 g <- ggbiplot(pcobj = cruzi.pca, scale = 1, obs.scale = 1, var.scale = 1, 
                groups = groups, ellipse = TRUE, 
                circle = TRUE, var.axes = FALSE)
  g <- g + scale_color_discrete(name = '')
  g <- g + theme(legend.direction = 'horizontal', 
                 legend.position = 'top')
  print(g)
  

http://s4.postimg.org/vdsax2drx/Rplot04.png

How can I decide what plot to use? And Why a same matrix of transformed data got so differently clusted ? Thank you.

deseq2 Prcomp PCA • 3.8k views
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@mikelove
Last seen 12 hours ago
United States

See ?plotPCA in particular the arguments and the note.

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Adding on to Mike's comment, it is most likely due to the number of genes you use for the DESeq2::plotPCA function. This number defaults to 500, while you take all the genes in the rldMat2 object - at least, if rld and rldMat2 are exactly the same objects.

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Indeed. This explain the difference.

Why I would make the PCA for only 500 genes instead of all of them ?

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Making a PCA plot after first ranking the genes by total variance helps to make more clear the sample groupings. Of course, you can tune this parameter, but 500 is a good number for many RNA-seq datasets.

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Thanks for the clarifications Michael.

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