Question: I'm using ChIPQC_1.10.3,/DiffBind_2.2.12 and deeptools 2.5. **Basically the idea of this question, is to try to reproduce almost the same pca plot with the two tools.**

**On the right** , I have done the pca plot with ChIPQC :
I have used consensus mode with 250 bp around peaks.
I think that I used 275 777 regions where at least a peak was called for a mark. I retrieved these regions from ChipQC. (sum of all regions found with peaks for all my different marks to use with deeptools pca).
I think ChIPQ do a log2 transformation on the raw count.

ChIPQCreport(..)

The samples seems to be separated by condition/color (T1,T7,unT7) but PC2 is low compared to PC1. Pink one seems to have a strange behavior... Is PC2 the effect of the treatment and PC1 "genomic effect" ?

**On the left** I have use these 275 777 regions that I suspect to be use by ChIPQC with deeptools.

multiBamSummary BED-file --BED regions.bed --ignoreDuplicates ...
plotPCA ...

The samples merged well by condition.
I don't understand why the PC1 is very high but all the samples are aligned on a vertical axis ! Is this a bug ? [here][1]
How can you explain that ?

Also pink point that were disjoined in previous chipqc plot seems to be perfectly match now ! Is this a display effect ?

Does deeptools also transform the raw count in log2 ?

Devon Ryan said "the size of the effect accounted for by a PC doesn't tell you anything about how informative that will be in discriminating between samples...The size of an effect is telling you about how much it contributes to the variation inside a sample. In ChIPseq that tends to be dominated by genomic position, since you'll have things like GC bias or other random library-prep. artefacts that are common between samples" here][2]

Sorry but I'don't understand how you interpret the degree of PC.

Why your argument on genomic position is more valuable for ChipSeq than RnaSeq ? If you are looking at the same histone mark between samples with different treatment, it should not play a strong role ?

**Update :**
Also what is the impact of --rowCenter because it totally changes PC, and plot ;-/
![enter image description here][2]

![pca plot][3]

[1]: https://github.com/fidelram/deepTools/issues/477
[3]: https://github.com/ZheFrenchKitchen/pics/blob/master/pcaPLOT.png?raw=true
[2]: https://github.com/ZheFrenchKitchen/pics/blob/master/rawCenter.png?raw=true

Answer: Until version 2.6, plotPCA is only able to calculate the PCA of a matrix with regions as rows and samples as columns. ChIPQC is likely performing the PCA on the transposed matrix. In version 2.6 deepTools will be able to do this too (you can install the develop branch from github if you need it now), because I had to implement my own PCA method since the one provided by matplotlib is artificially limited. Anyway, this transposition of the matrix is the difference between the two plots.

> Does deeptools also transform the raw count in log2 ?

DeepTools does not do anything different with the values you input when performing a PCA (just the standard centering/scaling).

> Sorry but I'don't understand how you interpret the degree of PC.

I don't know what you mean by "degree" in this context.

> Why your argument on genomic position is more valuable for ChipSeq than RnaSeq ?

ChIPseq has historically had a number of problems not generally seen in RNAseq. Chief among those are a variety of genomic position biases caused by things like GC bias in TAQ polymerases or "blacklisted regions". That generally tends to be what will dominate PC1 if you perform a PCA with regions as rows. In such cases, you would very much like to see your samples stacking up in a vertical line on PC1, since that indicates that these biases are similar between samples.

> If you are looking at the same histone mark between samples with different treatment, it should not play a strong role ?

Your biological effect will be minuscule compared to the background biases.

> Is PC2 the effect of the treatment and PC1 "genomic effect" ?

PCs will generally not correspond to any coherent biological concept, particularly when you perform the PCA on a transposed matrix.

> Also what is the impact of --rowCenter

It subtracts the row mean from each row, which effectively removes the "background genomic position bias" signal. Frankly, that was mostly a hack I put in because I rarely want to see that signal and hadn't yet gotten around to implementing the `--transpose` option.