Hi

I would like to plot RNASeq data that I have downloaded from TCGA in a PCA plot. I have found some great guides on how to plot the actual data in PCA using r in ggplot2 and such but my main question is what format data should I plot?

I currently have raw counts and RSEM data. Should I input raw counts into something like edgeR or deseq2 and filter for expression by cpm first? Should I normalise it? Should I stabilise variance using rlog2? Or convert to TPM and plot that? Argh I'm so confused. Grateful for any advice you can give me :)
I would recommend you to input your raw counts into DESeq2, run the pipeline, convert normalized reads to rlog (regularized log transformed reads) and then just run the plotPCA function from DESeq2. It is very easy if you are familiarized with the program. 

     # Creating deseq2 object
     dds <- DESeqDataSetFromMatrix(countData = inputData, 
                                colData = samples, 
                                design = design)

    dds <- DESeq(dds, betaPrior = betaPrior)
    # Regularized log transformation for different analysis (clustering, heatmaps, etc)
    rld <- rlogTransformation(dds)
    pca <- plotPCA(rld, intgroup = c(colGroups))



The idea behind using rlog transformation for Quality Control checks is described in [DESeq2 paper][1]: "[...] Therefore, we use the shrinkage approach of DESeq2 to implement a regularized logarithm transformation (rlog), which behaves similarly to a log2 transformation for genes with high counts, while shrinking together the values for different samples for genes with low counts. It therefore avoids a commonly observed property of the standard logarithm transformation, the spreading apart of data for genes with low counts, where random noise is likely to dominate any biologically meaningful signal[...]"


  [1]: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8