I have 2 biological replicates for chip-seq transcription factor data and I have applied macs2 peak calling for each replicate separately.
How can i make use of the biological replicates to extract the most out the data?
Thanks!
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1$\begingroup$ Hello Dimtiris, that's quite a broad question, is there a way to narrow it? What authors of macs2 say? How it's treated in other publications? How do you think you should treat your two replicates? $\endgroup$– Kamil S JaronCommented Jun 24, 2019 at 11:53
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2 Answers
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You could first look at the degree of correlation between the two replicates - what proportion of peaks are shared between the two, versus peaks found in one sample only? This will give an idea of how repeatable the analysis is, and how many peaks are variable between samples or due to artefacts of the method.
When it comes to interpreting the biology behind the binding patterns, you are probably going to want to only interpret peaks which you find in both replicates.
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This question is somewhat generic, so a generic answer is that ENCODE has a Transcription Factor ChIP-seq Data Standards and Processing page that can give you a useful starting point.
For TF ChIP-seq data with replicates, the Irreproducible Discovery Rate (IDR) method helps leverage replicates to produce higher confidence peak calls, producing both "optimal" and "conservative" peaks, defined by some IDR threshold. Here's ENCODE's description of the method:
A statistical procedure that operates on the replicated peak set and compares consistency of ranks of these peaks in individual replicate/pseudoreplicate peak sets. Peaks with high rank consistency are retained. IDR can operate on peaks across a pair of true replicates resulting in a “conservative” output peak set, or across a pair of pseudoreplicates resulting in an “optimal“ output peak set. Peaks in the conservative peak set can be interpreted as high confidence peaks, representing reproducible events across true biological replicates and accounting for true biological and technical noise. Peaks in the optimal set can be interpreted as high-confidence peaks, representing reproducible events and accounting for read sampling noise. The optimal set is more sensitive, especially when one of the replicates has lower data quality than the other.
The Python code to run IDR on peak sets can be found on ENCODE's GitHub Chip-seq pipeline repo.
