Crossposted from Bioconductor:
Sorry for this naive question, but I am new to principal component analyses and how to display them. I have two different gene sets, coding and non-coding genes, and count data from two different conditions (four replicates of both). I want to compare how the samples cluster in the two gene sets, i.e. do they have the same expression signatures. I made PCAplots of each gene set separately in DESeq2, but I wonder if it makes sense at all to plot them in the same PCA-plot?
Thanks, Jon
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jon.brate
2 answers
It is very easy to understand it. In the traditional PCA analysis, each point in PC1-PC2 dimension represent one sample. Since two of your gene set share same samples, you can not repeatly plot them again in one PC1-PC2 figure. In your situation, you can do two PCA plot and compare the difference and the variance distribution of the PCA (Loading trending)
The two plots would most likely not be comparable because PC1/PC2 would be different between the two PCAs. If you want to see how the samples cluster between two conditions and be able to say something about the similarity/difference then you should analyze the two datasets jointly.
WTF man? lol
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