1 Introduction

CHiCAGO is a method for detecting statistically significant interaction events in Capture HiC data. This vignette will walk you through a typical CHiCAGO analysis.

A typical Chicago job for two biological replicates of CHi-C data takes 2-3 h wall-clock time (including sample pre-processing from bam files) and uses 50G RAM.

NOTE A wrapper to perform this type of analysis, called runChicago.R, is provided as part of chicagoTools, which is available from our Bitbucket repository. Refer to the chicagoTools README for more information.

The statistical foundations of CHiCAGO are presented in a separate paper that is currently available as a preprint (Jonathan Cairns*, Paula Freire-Pritchett* et al. 2015). Briefly, CHiCAGO uses a convolution background model accounting for both ‘Brownian collisions’ between fragments (distance-dependent) and ‘technical noise’. It borrows information across interactions (with appropriate normalisation) to estimate these background components separately on different subsets of data. CHiCAGO then uses a p-value weighting procedure based on the expected true positive rates at different distance ranges (estimated from data), with scores representing soft-thresholded -log weighted p-values. The score threshold of 5 is a suggested stringent score threshold for calling significant interactions.

WARNING The data set used in this tutorial comes from the package PCHiCdata. This package contains small parts (two chromosomes each) of published Promoter Capture HiC data sets in mouse ESCs (Schoenfelder et al. 2015) and GM12878 cells, derived from human LCLs (Mifsud et al. 2015) - note that both papers used a different interaction-calling algorithm and we are only reusing raw data from them. Do not use any of these sample input data for purposes other than training.

In this vignette, we use the GM12878 data (Mifsud et al. 2015):


2 Workflow

2.1 Input files required

Before you start, you will need:

  1. Five restriction map information files (“design files”):
  • Restriction map file (.rmap) - a bed file containing coordinates of the restriction fragments. By default, 4 columns: chr, start, end, fragmentID.
  • Bait map file (.baitmap) - a bed file containing coordinates of the baited restriction fragments, and their associated annotations. By default, 5 columns: chr, start, end, fragmentID, baitAnnotation. The regions specified in this file, including their fragmentIDs, must be an exact subset of those in the .rmap file. The baitAnnotation is a text field that is used only to annotate the output and plots.
  • nperbin file (.npb), nbaitsperbin file (.nbpb), proxOE file (.poe) - Precompute these tables from the .rmap and .baitmap files, using the Python script from chicagoTools at our Bitbucket repository. Refer to the chicagoTools README file for more details.

We recommend that you put all five of these files into the same directory (that we refer to as designDir). An example of a valid design folder, for a two-chromosome sample of the GM12878 data used in this vignette, is provided in the PCHiCdata package, as follows.

dataPath <- system.file("extdata", package="PCHiCdata")
testDesignDir <- file.path(dataPath, "hg19TestDesign")
## [1] "h19_chr20and21.baitmap" "h19_chr20and21.nbpb"    "h19_chr20and21.npb"    
## [4] "h19_chr20and21.poe"     "h19_chr20and21.rmap"
NOTE Though we talk about “restriction fragments” throughout, any non-overlapping regions can be defined in .rmap (with a subset of these defined as baits). For example, if one wanted to increase power at the cost of resolution, it is possible to use bins of restriction fragments either throughout, or for some selected regions. However, in the binned fragment framework, we advise setting removeAdjacent to FALSE - see ?setExperiment for details on how to do this.
  1. You will also need input data files, which should be in CHiCAGO input format, .chinput. You can obtain .chinput files from aligned Capture Hi-C BAM files by running, available as part of chicagoTools. (To obtain BAM files from raw fastq files, use a Hi-C alignment & QC pipeline such as HiCUP.

Example .chinput files are provided in the PCHiCdata package, as follows:

testDataPath <- file.path(dataPath, "GMchinputFiles")
## [1] "GM_rep1.chinput" "GM_rep2.chinput" "GM_rep3.chinput"
files <- c(
    file.path(testDataPath, "GM_rep1.chinput"),
    file.path(testDataPath, "GM_rep2.chinput"),
    file.path(testDataPath, "GM_rep3.chinput")

OPTIONAL: The data set in this vignette requires some additional custom settings, both to ensure that the vignette compiles in a reasonable time and to deal with the artificially reduced size of the data set:

settingsFile <- file.path(system.file("extdata", package="PCHiCdata"),
                          "sGM12878Settings", "sGM12878.settingsFile")

Omit this step unless you know which settings you wish to alter. If you are using non-human samples, or a very unusual cell type, one set of options that you might want to change is the weighting parameters: see Using different weights.

2.2 Example workflow

We run CHiCAGO on the test data as follows. First, we create a blank chicagoData object, and we tell it where the design files are. For this example, we also provide the optional settings file - in your analysis, you can omit the settingsFile argument.


cd <- setExperiment(designDir = testDesignDir, settingsFile = settingsFile)

The properties of chicagoData objects are discussed more in The chicagoData object.

Next, we read in the input data files:

cd <- readAndMerge(files=files, cd=cd)

Finally, we run the pipeline with chicagoPipeline():

cd <- chicagoPipeline(cd)

2.3 Output plots

chicagoPipeline() produces a number of plots. You can save these to disk by setting the outprefix argument in chicagoPipeline().

The plots are as follows (an explanation follows):

2.3.1 Interpreting the plots

Here, we describe the expected properties of the diagnostic plots.

Note that the diagnostic plots above are computed on the fly using only a small fraction of the full GM12878 dataset. In real-world, genome-wide datasets, more fragment pools will be visible and thus the trends described below should be more pronounced.

  1. Brownian other end factors: The adjustment made to the mean Brownian read count, estimated in the pools of other ends. (“tlb” refers to the number of trans-chromosomal reads that the other end accumulates in total. “B2B” stands for a “bait-to-bait” interactions).
  • The red bars should generally increase in height from left to right.
  • The blue bars should be higher than the red bars on average, and should also increase in height from left to right.
  1. Technical noise estimates: The mean number of technical noise reads expected for other ends and baits, respectively, per pools of fragments. These pools, displayed on the x axis, again refer to the number of trans-chromosomal reads that the fragments accumulate.
  • The distributions’ median and variance should trend upwards as we move from left to right.
  • In the lower subplot, the bait-to-bait estimates (here, the four bars on the far right) should be higher, on average, than the others. Both groups should also have medians and variances that trend upwards, moving from left to right.
  1. Distance function: The mean number of Brownian reads expected for an average bait, as a function of distance, plotted on a log-log scale.
  • The function should monotonically decrease.
  • The curve should fit the points reasonably well.

2.4 Output files

Two main output methods are provided. Here, we discuss the first: exporting to disk. However, it is also possible to export to a GenomeInteractions object, as described in Further downstream analysis.

You can export the results to disk, using exportResults(). (If you use runChicago.R, the files appear in ./<results-folder>/data). By default, the function outputs three different output file formats:

outputDirectory <- tempdir()
exportResults(cd, file.path(outputDirectory, "vignetteOutput"))
## Reading the restriction map file...
## Reading the bait map file...
## Preparing the output table...
## Writing out for seqMonk...
## Writing out interBed...
## Preprocessing for WashU outputs...
## Writing out text file for WashU browser upload...

Each called interaction is assigned a score that represents how strong CHiCAGO believes the interaction is (formally, it is based on -log(adjusted P-value)). Thus, a larger score represents a stronger interaction. In each case, the score threshold of 5 is applied.

Summary of output files:

2.4.1 ibed format (ends with …ibed):

##   bait_chr bait_start bait_end                  bait_name otherEnd_chr
## 1       20     119103   138049                    DEFB126           20
## 2       20     119103   138049                    DEFB126           20
## 3       20     161620   170741                    DEFB128           20
## 4       20     233983   239479                    DEFB132           20
## 5       20     268639   284501 AL034548.1;C20orf96;ZCCHC3           20
## 6       20     268639   284501 AL034548.1;C20orf96;ZCCHC3           20
##   otherEnd_start otherEnd_end otherEnd_name N_reads score
## 1         161620       170741       DEFB128      11  5.04
## 2         523682       536237       CSNK2A1       6  6.76
## 3          73978        76092             .      16  5.07
## 4         206075       209203       DEFB129      33  5.89
## 5         293143       304037             .      34  7.26
## 6         304038       305698             .      34  8.82
  • each row represents an interaction
  • first four columns give information about the chromosome, start, end and name of the bait fragment
  • next four columns give information about the chromosome, start, end and name of the other end that interacts with the bait fragment
  • N_reads is the number of reads
  • score is as defined above

2.4.2 seqmonk format (ends with …seqmonk.txt):

##   V1     V2     V3      V4 V5   V6
## 1 20 119103 138049 DEFB126 11 5.04
## 2 20 161620 170741 DEFB128 11 5.04
## 3 20 119103 138049 DEFB126  6 6.76
## 4 20 523682 536237 CSNK2A1  6 6.76
## 5 20 161620 170741 DEFB128 16 5.07
## 6 20  73978  76092       . 16 5.07
  • Can be read by seqmonk.
  • An interaction is represented by two rows: the first row is the bait, the second the other end. Thus, the file alternates: bait1, otherEnd1, bait2, otherEnd2, …
  • Columns are: chromosome, start, end, name, number of reads, interaction score (see above)

2.4.3 washU_text format (ends with …washU_text.txt):

##                    V1                  V2   V3
## 1 chr20,119103,138049 chr20,161620,170741 5.04
## 2 chr20,119103,138049 chr20,523682,536237 6.76
## 3 chr20,161620,170741   chr20,73978,76092 5.07
## 4 chr20,233983,239479 chr20,206075,209203 5.89
## 5 chr20,268639,284501 chr20,293143,304037 7.26
## 6 chr20,268639,284501 chr20,304038,305698 8.82
  • Can be read by the WashU browser
  • Upload via the “Got text files instead? Upload them from your computer” link.
  • Note - Advanced users may wish to export to washU_track format instead. See the help page for exportResults().

For bait-to-bait interactions, the interaction can be tested either way round (i.e. either fragment can be considered the “bait”). In most output formats, both of these tests are preserved. The exception is washU output, where these scores are consolidated by taking the maximum.

NOTE When comparing interactions detected between multiple replicates, the degree of overlap may appear to be lower than expected. This is due to the undersampled nature of most CHi-C datasets. Sampling error can drive down the sensitivity, particularly for interactions that span large distances and have low read counts. As such, low overlap is not necessarily an indication of a high false discovery rate.

Undersampling needs to be taken into consideration when interpreting CHiCAGO results. In particular, we recommend performing comparisons at the score-level rather than at the level of thresholded interaction calls. Potentially, differential analysis algorithms for sequencing data such as DESeq2 (Love, Huber, and Anders 2014) may also be used to formally compare the enrichment at CHiCAGO-detected interactions between conditions at the count level, although power will generally be a limiting factor.

Formal methods such as sdef (Blangiardo, Cassese, and Richardson 2010) may provide a more balanced view of the consistency between replicates. Alternatively, additional filtering based on the mean number of reads per detected interaction (e.g. removing calls with N<10 reads) will reduce the impact of undersampling on the observed overlap, but at the cost of decreasing the power to detect longer-range interactions.

2.5 Visualising interactions

The plotBaits() function can be used to plot the raw read counts versus linear distance from bait for either specific or random baits, labelling significant interactions in a different colour. By default, 16 random baits are plotted, with interactions within 1 Mb from bait passing the threshold of 5 shown in red and those passing the more lenient threshold of 3 shown in blue.

plottedBaitIDs <- plotBaits(cd, n=6)