Downsampling ChIP-seq BAM files with spike in normalization factor before feeding into differential binding analysis

Question

[m.sadman.sakib]

I am planning to use exogenous chromatin as a spike in control with my actual sample from mouse to perform ChIP-seq for peak calling and differential binding analysis for histone modifications. this involves down-sampling the uniquely mapped read files to the calculated normalization factor from the spike in. This is seemingly helpful for peak calling and visualizing in genome browser. But I have not found any reference on whether it is considered in differential binding analysis. Since I am using Diffbind, I also could not find anything regarding this in the vignette. Could anyone please explain how this strategy might affect using the diffbind package? Or, does using spike-in for ChIP-seq normalization makes sense?

I greatly appreciate your time to read and answer to my question! Thank you in advance!

An example of this normalization process is given below(Image from Active motif's ChIP-seq spike in kit.)

[ATpoint]

To my knowledge, DiffBind uses DESeq2 internally for differential analysis and normalization. DESeq2 expects raw counts and it is strongly recommended to provide these instead of customly normalized values.

[m.sadman.sakib]

But technically, this will still be raw counts, just spike in adjusted for all samples. For RNA-seq, there are already a package (Ruvseq) which does this spike in normalization for the counts matrix, which can be used for DESeq2 analysis.

Answer 1

[nicolas.descostes]

To my knowledge only csaw enables to include the spike-in before doing differential binding analysis with "searching". You can also have a look at the package that we are developing, ChIPSeqSpike:

https://bioconductor.org/packages/3.7/bioc/html/ChIPSeqSpike.html

If you know already where your histone modifications are located, I think that you can generate directly figures with our package. If you want to perform peak calling after scaling, you can use the "BamCoverage" function of deepTools by applying the scaling factors given by our "SpikeSummary" function.

We will integrate further solutions to perform all analysis at once in the near future.