I have RNA-seq data for 20 samples with 2 condition and 2 sex (male, female : control, treatment). I am very new to RNA-seq analysis and am trying to find the DEGs using DESeq2. Since I want to have the normalization to be calculated based on all samples, I get the rld based on all and then I will use contrast to find the DEG for 1. tratment vs. control male 2.  tratment vs. control female.

For the QC, the PCA plot separates the male and female but the control and treatment is not separated very well on the PC2  for male C1 (Figure 1)![enter image description here][1]

Then I decided plot PCA only for male samples. again PC1 does not separate the control and treatment male samples but PC2 is kinda separating them. (Figure2)

![enter image description here][2]

My questions are:

1. What numbers on the PCA plot (x and y axis) decide the separation? I read in a [website][3] that samples that are at PC1>0 are outlier. is that true? 

2. Can I just look at figure 1 and remove male c1 and continue DEG analysis with 9 samples? or I should definately plot figure 2 ?

3. If I need to consider Figure 2, can I rely on only the PC2 which separates control and treatment and continue the DEG analysis or I should remove C1, tr4, tr5 samples and then work on DEG analysis based on remaining 7 samples?


  [1]: https://i.imgur.com/UgC0byK.png
  [2]: https://i.imgur.com/mpnghro.png
  [3]: https://www.huber.embl.de/users/klaus/Teaching/DESeq2Predoc2014.html#pca-and-sample-heatmaps
Hey,

Regarding the website to which you are referring, that was written by Wolfgang Huber. He is *not* saying, in his tutorial, that PC1>0 is a global definition of an outlier from a PCA bi-plot. It is just in *his* example that he has decided to select 2 outlier samples via a vertical cut-off line drawn at PC1>0. In another experiment, the cut-off for outliers may be at PC1 < -500, or PC1 > 42. The units on these axes are, well, essentially unitless.

Looking at your first plot, you cannot really remove male sample C1 just because it groups away from the main group of samples. You should first investigate *why* it may be grouping in the way that it is [grouping]. Is it a sample mix-up?; is it a male with Klinefelter Syndrome?; is the clinical information for this sample incorrect?


For your third question, you need to consider the fact that `sex` represents a substantial amount of variation in your dataset, i.e., it is greater than the very condition of interest that you are studying - was this expected?; did you include `sex` in your design formula as a covariate? If you include it as a covariate in your design formula, normalise your data again, and then run rld function with `blind=FALSE`, do you see the same PCA bi-plot?

You can also segregate your dataset based on `male` | `female` and then cross-compare results in a sort of meta-analysis, if you wish. `sex` is a clear and major source of variation among your samples. If you proceed with this route (sample segregation based on `sex`), then I see no reason to exclude c1.

Kevin