Hi. I am studying a protein that potentially affects H2A.Z levels by regulating the complex depositing this histone variant (SWR1c). So we did ChIP-seq in WT, SWR1c knockout and a knockout of my protein. We sequenced input, H2A.Z and H2B. I would like to plot heatmaps and metagene plots, but I have no idea what would be the best normalization to use.
1) Lots of posts don't recommend the use of FPKM or TPM. It seems that TMM, RLE or quantile normalization are generally better methods for ChIP normalization. However, these methods are not a good option when there's loss of signal at a global level, as it happens for H2a.Z in the SWR1 knockout. Which method would be more appropriate?
2) We don't expect global loss of H2A.Z when our protein is knocked out, only a subset of genes affected. So, in order to compare between samples, should I use the same normalization method in both, even when it's not de most appropriate for one of them? Or should I normalize each one with a suitable method?
3)In addition to that, one of my libraries failed and I have one sample with half the reads than the others, so I should normailze to correct that too.
Do you have any suggestions about the best strategy?
Thank you very much for your help.
Thanks for your suggestion! I've just tried ChIPseqSpikeInFree, but I am not sure if I understand it correctly. You are supposed to get larger scaling factors for lower histone mark levels, but I got SF=1 for all my inputs and knockouts, and SF=1.44 for WT. I ran my data through SICER and it detects around 10000 peaks with decreased histone levels (even without spike-in), so I'm sure there is less histone in my mutant sample...
Have you confirmed the global changes via western blot or similar? That is going to be the easiest way to determine if the effect is real or if the IP for the knockout in question just didn't work as well.