Our experience with spike-in is also mixed. In some cases, the normalization caused considerably increases in variation between replicates. On more than one occasion, we ended up not using the spike-in data as the global loss or gain of ChIP-seq signal was smaller than the increased variation between replicates.

I have seen several examples of spike-in adjusted data, where the only difference(s) in the presented signal originate from the normalization factor(s) themsel(f/ves). I see two issues with this: 1) seq-based assays of this is like cracking nuts with a sledge-hammer, and 2) the normalization results in an apparent genome-wide difference, which essentially relies on a single data point (the normalization factor difference), so the large set of affected loci can appear as several independent observations while actually being based on a single observation (the difference in spike-in counts).

The seq-based methods can shine for the identification of local differences at a subset of loci. However, for a low information content, which generally boils down to loss or gain of signal everywhere, it would make more sense to rely on more robust and simple methods such as western blotting or qPCR-ChIP as the primary assay.

I haven't looked much into the technical details of the spike-in free method. It is either beyond my level of understanding (indeed possible), or it must rely on a number of assumptions about the differences in the samples. In case of the latter, it would then be independent assays that assessed the validity of the assumptions. It would be interesting to get your take on this.

I agree with your point that it is difficult to rationalize the application of one drosophila read-derived correction factor genome wide. Our biggest problem is that Active motif suggests 50ng of spike in chromatin is sufficient to achieve ~2-5% drosophila reads, and we rarely do better than 0.05% drosophila reads for most ChIPs. It seems that I would need to make my own drosophila chromatin, because I cannot afford the amount of spike-in chromatin that I think I would need to get to 2%.

I also agree with your point that for a case like H3K27me3 chip in the presence of EZH2i, there should be very little H3K27me3 available to IP anyways. A case such as this is really the only time we see the drosophila spike in working. This is largely due to how we make the libraries. We balance the ng of DNA going in, even though we know there should be less from the EZH2i sample. Perhaps it is valid to just library prep the same volume of DMSO vs EZH2i rather than the same mass? Then I doubt we would need downstream normalization.

Our real trouble is this : We have a situation where in condition A we see many superenhancer-like sites of H3K27Ac that are lost in condition B. In condition B we also see a massive gain of smaller enhancer and promoter-like sites of H3K27Ac that are not present in A. We know by western that total H3K27Ac is higher in B than in A. When we sequence these ChIPs we have trouble calling the small gains in H3K27Ac in B, and we have trouble calling decreased H3K27Ac at superenhancers in condition A. Drosophila spike in fails miserably here because the H3K27Ac is abundant in both samples, and the antibody is great. The H3K27Ac is locally redistributed and globally increased.

So out of 100M reads, we only get like 200k drosophila reads, which is not statistically reliable. So we try this computational method for normalization and we see exactly what we would expect and what all our other data (ChIP-qPCR, western, etc) suggest we should see.

I have spent quite some time trying to understand this ChIPseqSpikeInFree method. It is true that it falls back on your point that the assumptions you make about the data must be orthogonally validated by other experiments. I think that you must have a hypothesis driven reason to use this computational method or the drosophila spike-in method. For the EZH2i H3K27me3 ChIP, I suppose a western blot is really sufficient to say that H3K@7me3 is depleted, but for H3K27Ac, we need to know where it is depleted and enriched, and we need to account for it being globally increased in abundance.

Our worry is that although this computational normalization idea has been published by bioinformaticists, it has not been vetted by biologists for real world application. I am just worried that even though this seems to work for my experiment, I will get killed by reviewers whether this method is valid or not, because drosophila spike-in is a lot easier to understand and explain.