HiChIP Comparative Analyses

Introduction

Biological questions are seldom answered by analysing single samples in isolation. It is often the case that an experiment aims to make comparisons between two (or more) biological conditions, such as:

1. Untreated wild type vs treatment

2. Wild type vs knockout

3. Normal sample vs tumor

In all cases the goal is to produce a list of differentially interacting regions in one condition relative to the other. The main output for comparitive analses is analogous to what is expeected for differential gene expression, where the primary result is a table of regions, the fold change between conditions, and a statistical measure of signficance. For HiCHiP, the unit for comparison are the loop calls identified using the FitHiChIP software as described in previous steps.

Figure 1:

Differential Analysis

Question: How do I perform differential analyses for HiChIP?

Process: Results files from FitHiChIP are used to construct a differential design, and comparison is performed using the scripts bundled with fithichip software.

Results: Final results consist of a table of differentially interacting regions, fold change, and measure of statistical signficance.

Files and tools needed:

• FitHiChIP loop calls for each condition: PREFIX.interactions_FitHiC.bed

• FitHiChIP differential analysis software and scripts

• Associated ChIP-seq peak files [optional]

As the design of differential analysis experiments are unique to each biological question, there are multiple possibilites for how the analysis can be set up. A common scenario is to compare two conditions where each condition has two replicates, and is described in the FitHiChIP documentation pages.

Interpreting results:

FitHiChIP differential analysis produces a number of intermediate in addition to the final results table. The most important is the list of significant loops and is named “Loops_EdgeR_Default_SIG.bed”. In general, the interpretation of differential loop analysis is the same as what is familiar for gene expression analysis, where intereactions can be prioritized based on the fold change and statistical significance. An example output file is given below.

chr1	start1	end1	chr2	start2	end2	group1_R1_RawCC	group1_R1_QVal	group2_R1_RawCC	group2_R1_QVal	logFC	logCPM	PValue	FDR	group1_SigRepl	group2_SigRepl
chr1	930000	940000	chr1	940000	950000	0	1	37	0.8722	8.2960	5.4070	0.0000	0.0022	0	0
chr1	940000	950000	chr1	950000	960000	0	1	40	0.9991	8.4081	5.5105	0.0000	0.0016	0	0
chr1	1020000	1030000	chr1	1030000	1040000	0	1	43	1	8.5122	5.6071	0.0000	0.0012	0	0
chr1	1030000	1040000	chr1	1040000	1050000	0	1	55	0.872	8.8664	5.9403	0.0000	0.0004	0	0
chr1	1060000	1070000	chr1	1070000	1080000	0	1	48	0.9865	8.6705	5.7550	0.0000	0.0007	0	0
chr1	1070000	1080000	chr1	1080000	1090000	0	1	25	0.8722	7.7326	4.8990	0.0003	0.0127	0	0
chr1	1140000	1150000	chr1	1150000	1160000	0	1	30	0.9681	7.9945	5.1326	0.0001	0.0052	0	0
chr1	1150000	1160000	chr1	1160000	1170000	0	1	40	0.9984	8.4081	5.5105	0.0000	0.0016	0	0
chr1	1250000	1260000	chr1	1260000	1270000	28	0.8747	0	1	-7.7365	4.9817	0.0002	0.0109	0	0

The most relevant fields from the output will be:

• logFC – the log fold change in coverage between the two conditions

• FDR – a p-value, after correction for multiple hypothesis testing, on the statistical signficance of the observed fold change

Considerations:

• Replication – It is generally advisable to have technical replicates for differential analyses, as this will produce more statistically robust results. FitHiChIP is still able to perform differential analysis with single-replicate samples, and in this case reverts to the square-root-dispersion method used by EdgeR.

• Paired ChIP-seq experiments – As mentioned above, it is best practices to have paired ChIP-seq experiments. If that is not do-able, FitHiChIP is bundled with a script that can call peaks de novo from the HiChIP data directly.