# Chromatin Segmentation Analysis Using segmenter

# Overview

Chromatin segmentation analysis transforms ChIP-seq data into signals over the genome. The latter represents the observed states in a multivariate Markov model to predict the chromatin’s underlying (hidden) states. ChromHMM, written in Java, integrates histone modification datasets to learn the chromatin states de-novo. We developed an R package around this program to leverage the existing R/Bioconductor tools and data structures in the segmentation analysis context. `segmenter` wraps the Java modules to call ChromHMM and captures the output in an `S4` object. This allows for iterating with different parameters, which are given in R syntax. Capturing the output in R makes it easier to work with the results and to integrate them in downstream analyses. Finally, `segmenter` provides additional tools to test, select and visualize the models.

# Installation

The package can be installed from Bioconductor using `BiocManager` or from GitHub using `remotes``

    # install from bioconductor
    BiocManager::install('segmenter')

    # install from github
    remotes::install_github('MahShaaban/segmenter@devel')

# Background

## Hidden Markov Models

Hidden Markov Models (HMM) assumes that a system (process) with unobservable or hidden states can be modeled with a dependent observable process. In applying this model to segmentation analysis, the chromatin configurations are the hidden states and they can be modeled using histone modification markers that are associated with these configurations.

## ChromHMM

ChromHmm is a Java program to learn chromatin states from multiple sets of histone modification markers ChIP-seq datasets. The states are modeled as the combination of markers on the different regions of the genome. A multi-variate hidden Markov model is used to model the presence or absence of the markers. In addition, the fold-enrichment of the states over genomic annotation and locations is calculated. These models can be useful in annotating genomes by showing where histone markers occur and interpreting this as a given chromatin configuration. By comparing states between different cells or condition, one can determine the cell or condition specific changes in the chromatin and study how they might impact the gene regulation.

## This package!

The goal of the `segmenter` package is to

- Call ChromHMM using R syntax
- Capture the output in R objects
- Interact with the model output for the purposes of summarizing or visualizing

# Getting started

This is a quick example of using `segmenter` to call the Java modules and will be followed by a detailed description in the following sections.

First, we need to load the package

    # load library
    library(segmenter)
    #> Warning: replacing previous import 'utils::findMatches' by
    #> 'S4Vectors::findMatches' when loading 'AnnotationDbi'

The Java modules and example files are bundled with `segmenter`. We can locate these files using `system.file`. The required files are the binarized data, genomic coordinates, anchors files and the chromosomes’ sizes file. When more than one file is required, passing the directory where they reside is sufficient.

    # locate input and annotation files
    inputdir <- system.file('extdata/SAMPLEDATA_HG18',
                            package = 'segmenter')
    coordsdir <- system.file('extdata/COORDS',
                             package = 'chromhmmData')
    anchorsdir <- system.file('extdata/ANCHORFILES',
                              package = 'chromhmmData')
    chromsizefile <- system.file('extdata/CHROMSIZES',
                                 'hg18.txt',
                                 package = 'chromhmmData')

Other arguments are required to ensure the Java modules correctly recognize the inputs and output the correct file names. Those include the number of states, the name of the genome assembly, the names of the cells/conditions, annotation and the bin size that were used to generate the binarized input files.

    # run command
    obj <- learn_model(inputdir = inputdir,
                       coordsdir = coordsdir,
                       anchorsdir = anchorsdir,
                       chromsizefile = chromsizefile,
                       numstates = 3,
                       assembly = 'hg18',
                       cells = c('K562', 'GM12878'),
                       annotation = 'RefSeq',
                       binsize = 200)

To get a quick glance on the return object, just call the `show` method.

    # show the object
    show(obj)
    #> # An object of class 'segmentation'
    #> # Contains a chromatin segmentation model:
    #> ## States: 1 2 3
    #> ## Marks: H3K27me3 H3K4me3 H3K9ac H3K27ac H3K4me2 WCE H3K4me1 CTCF H4K20me1 H3K36me3
    #> ## Cells: K562 GM12878
    #> # Contains nine slots:
    #> ## model: use 'model(object)' to access
    #> ## emission: use 'emission(object)' to access
    #> ## transition: use 'transition(object)' to access
    #> ## overlap: use 'overlap(object)' to access
    #> ## TSS: use 'TSS(object)' to access
    #> ## TES: use 'TES(object)' to access
    #> ## segment: use 'segment(object)' to access
    #> ## bins: use 'bins(object)' to access
    #> ## counts: use 'counts(object)' to access
    #> # For more info about how to use the object, use ?accessors

The rest of this document discusses in details the inputs and outputs mentioned above as well as some of the tools provided in `segmenter` to explore the resulting chromatin models.

# Segmentation analysis using `segmenter`

## Inputs

ChromHMM requires two types of input files. Those are

- Genomic annotation files.

The genomic annotation is divided into three different files

- Coordinates: the start and end location of genomic features to calculate enrichment
- Anchors: the transcription start and end sites
- Chromosome size: the length of each chromosome

ChromHMM contains pre-formatted files for commonly used genomes. We will be using the human genome (hg18).

    # load required libraries
    library(segmenter)
    library(Gviz)
    library(ComplexHeatmap)
    library(TxDb.Hsapiens.UCSC.hg18.knownGene)

    # coordinates
    coordsdir <- system.file('extdata/COORDS',
                             package = 'chromhmmData')

    list.files(file.path(coordsdir, 'hg18'))
    #> [1] "CpGIsland.hg18.bed.gz"    "RefSeqExon.hg18.bed.gz"
    #> [3] "RefSeqGene.hg18.bed.gz"   "RefSeqTES.hg18.bed.gz"
    #> [5] "RefSeqTSS.hg18.bed.gz"    "RefSeqTSS2kb.hg18.bed.gz"
    #> [7] "laminB1lads.hg18.bed.gz"

    # anchors
    anchorsdir <- system.file('extdata/ANCHORFILES',
                              package = 'chromhmmData')

    list.files(file.path(anchorsdir, 'hg18'))
    #> [1] "RefSeqTES.hg18.txt.gz" "RefSeqTSS.hg18.txt.gz"

    # chromosomes' sizes
    chromsizefile <- system.file('extdata/CHROMSIZES',
                                 'hg18.txt',
                                  package = 'chromhmmData')

    readLines(chromsizefile, n = 3)
    #> [1] "chr1\t247249719" "chr2\t242951149" "chr3\t199501827"

- Binarized signal files from the ChIP-seq data

The binarized signal files are text files, often one for each chromosome, that divide the chromosome length into bins of a given size (rows) and have binary values 1 or 0 for each histone markers (columns). ChromHMM provide modules to generate these files from ChIP-seq aligned reads in `bam` or `bed`. Those modules are wrapped in two functions that can be called from within R.

- `binarize_bam` convert `bam` files into binary columns of 0 or 1 depending on whether the given marker exist in each bin across the length of the chromosome.
- `binarize_bed` similarly convert `bed` files.

These files are often large and need to be prepared in advance. Here, we are showing only an example using a `bam` file with random reads. Because multiple files are often needed to generate chromatin models, a table is required to assign each file a chromatin marker and a cell type or condition. In addition, a file containing the size of each chromosome is required. Finally, the desired bin size is indicated, the default is 200kb.

    # a table to assign marker and cell names to the bam files
    cellmarkfiletable <- system.file('extdata',
                                     'cell_mark_table.tsv',
                                     package = 'segmenter')

    readLines(cellmarkfiletable, n = 3)
    #> [1] "cell1\tmark1\trandomBam.bam" "cell1\tmark2\trandomBam.bam"
    #> [3] "cell2\tmark1\trandomBam.bam"

    # locate input and output
    inputdir <- system.file("extdata", package = "bamsignals")
    outputdir <- tempdir()

    # run command
    binarize_bam(inputdir,
                 chromsizefile = chromsizefile,
                 cellmarkfiletable = cellmarkfiletable,
                 outputdir = outputdir)

    # show output files
    example_binaries <- list.files(outputdir, pattern = '*_binary.txt')
    example_binaries
    #> [1] "cell1_chr1_binary.txt" "cell1_chr2_binary.txt" "cell1_chr3_binary.txt"
    #> [4] "cell2_chr1_binary.txt" "cell2_chr2_binary.txt" "cell2_chr3_binary.txt"

    # show the format of the binary file
    readLines(file.path(outputdir, example_binaries[1]), n = 3)
    #> [1] "cell1\tchr1"  "mark1\tmark2" "1\t1"

Note that the cell/condition and the chromosome name are written on the first line and the last bin is often removed as the end of the chromosome does not reach the 200kb bin size.

Two example files are provided by ChromHMM. Those were generated from two ChIP- seq experiments of nine histone modification markers in the K562 and GM12878 cell lines. The aligned reads were counted and binarized into 0 or 1 in bins of 200kb in chromosome 11.

    # locate input and output files
    inputdir <- system.file('extdata/SAMPLEDATA_HG18',
                            package = 'segmenter')

    list.files(inputdir)
    #> [1] "GM12878_chr11_binary.txt.gz" "K562_chr11_binary.txt.gz"

## Model learning

The main function in `segmenter` is called `learn_model`. This wraps the the Java module that learns a chromatin segmentation model of a given number of states. In addition to the input files explained before, the function takes the desired number of stats, `numstates` and the information that were used to generate the binarized files. Those are the names of the genome `assembly`, the type of `annotation`, the `binsize` and the names of `cells` or conditions.

    # run command
    obj <- learn_model(inputdir = inputdir,
                       coordsdir = coordsdir,
                       anchorsdir = anchorsdir,
                       outputdir = outputdir,
                       chromsizefile = chromsizefile,
                       numstates = 3,
                       assembly = 'hg18',
                       cells = c('K562', 'GM12878'),
                       annotation = 'RefSeq',
                       binsize = 200)

The return of this function call is the an S4 `segmentation` object, which we describe next.

## Output `segmentation` Object

The `show` method prints a summary of the contents of the object. The three main variables of the data are the states, marks and cells. The output of the learning process are saved in slots those are

- `model`: the initial and final parameters of the models
- `emission`: the probabilities of each mark being part of a given state
- `transition`: the probabilities of each state transition to/from another
- `overlap`: the enrichment of the states at every genomic features
- `TSS`: the enrichment of the states around the transcription start sites
- `TES`: the enrichment of the states around the transcription end sites
- `segment`: the assignment of states to every bin in the genome
- `bins`: the binarize inputs
- `counts`: the non-binarized counts in every bin

The last two slots are empty, unless indicated otherwise in the previous call. Counts are only loaded when the path to the `bam` files are provided.

    # show the object
    show(obj)
    #> # An object of class 'segmentation'
    #> # Contains a chromatin segmentation model:
    #> ## States: 1 2 3
    #> ## Marks: H3K27me3 H3K4me3 H3K9ac H3K27ac H3K4me2 WCE H3K4me1 CTCF H4K20me1 H3K36me3
    #> ## Cells: K562 GM12878
    #> # Contains nine slots:
    #> ## model: use 'model(object)' to access
    #> ## emission: use 'emission(object)' to access
    #> ## transition: use 'transition(object)' to access
    #> ## overlap: use 'overlap(object)' to access
    #> ## TSS: use 'TSS(object)' to access
    #> ## TES: use 'TES(object)' to access
    #> ## segment: use 'segment(object)' to access
    #> ## bins: use 'bins(object)' to access
    #> ## counts: use 'counts(object)' to access
    #> # For more info about how to use the object, use ?accessors

For each slot, an accessor function with the same name is provided to access its contents. For example, to access the emission probabilities call `emission` on the object.

    # access object slots
    emission(obj)
    #>        H3K27me3      H3K4me3       H3K9ac      H3K27ac      H3K4me2
    #> [1,] 0.02210946 0.0007877468 0.0002115838 0.0003912198 0.0003992906
    #> [2,] 0.01154739 0.0100515031 0.0093233199 0.0211596897 0.0330031299
    #> [3,] 0.05276061 0.6906489856 0.6030276625 0.6487866066 0.8252990950
    #>               WCE    H3K4me1        CTCF    H4K20me1   H3K36me3
    #> [1,] 0.0003263765 0.00150328 0.004697351 0.004263375 0.00121958
    #> [2,] 0.0034382153 0.21393308 0.052151182 0.048625137 0.24715916
    #> [3,] 0.0339041491 0.68908408 0.157048603 0.107478936 0.14438998

Some accessors more arguments to subset the object. For example, the `segment` method take a `cell` name to return on the segments in the corresponding cell.

    # subset the segment slot
    segment(obj, cell = 'K562')
    #> $K562
    #> GRanges object with 16280 ranges and 1 metadata column:
    #>           seqnames              ranges strand |       state
    #>              <Rle>           <IRanges>  <Rle> | <character>
    #>       [1]    chr11             0-50800      * |          E1
    #>       [2]    chr11         50800-52400      * |          E2
    #>       [3]    chr11         52400-57800      * |          E1
    #>       [4]    chr11         57800-58000      * |          E2
    #>       [5]    chr11         58000-58200      * |          E3
    #>       ...      ...                 ...    ... .         ...
    #>   [16276]    chr11 134227400-134243000      * |          E1
    #>   [16277]    chr11 134243000-134244200      * |          E2
    #>   [16278]    chr11 134244200-134450800      * |          E1
    #>   [16279]    chr11 134450800-134451600      * |          E2
    #>   [16280]    chr11 134451600-134452200      * |          E3
    #>   -------
    #>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

# Comparing models

To choose a model that fits the data well, one can learn multiple models with different parameters, for example the number of states and compare them. In this example, we will be calling `learn_model` several times using `lapply` with the same inputs except the number of states (`numstates`). The output would be a list of `segmentation` objects. `segmenter` contain functions to do basic comparison between the models.

    # relearn the models with 3 to 8 states
    objs <- lapply(3:8,
        function(x) {
          learn_model(inputdir = inputdir,
                       coordsdir = coordsdir,
                       anchorsdir = anchorsdir,
                       chromsizefile = chromsizefile,
                       numstates = x,
                       assembly = 'hg18',
                       cells = c('K562', 'GM12878'),
                       annotation = 'RefSeq',
                       binsize = 200)
        })

- `compare_models` takes a list of `segmentation` objects and returns a vector with the same length. The default is to compare the correlation between the emission parameters of the states in the different models. Only the correlations of the states that has the maximum correlation with one of the states in the biggest model is returned.

    # compare the models max correlation between the states
    compare_models(objs)
    #> [1] 0.6992815 0.9911192 0.9935068 0.9925634 0.9936017 1.0000000

- The other value to compare is the likelihood of the models which can be indicated through the `type` argument.

    # compare the models likelihood
    compare_models(objs, type = 'likelihood')
    #> [1] -889198.8 -834128.3 -806108.9 -790283.4 -773662.1 -766802.5

Setting `plot = TRUE` returns a plot with data points corresponding to the models in the list.

    # compare models plots
    par(mfrow = c(1, 2))
    compare_models(objs,
                   plot = TRUE,
                   xlab = 'Model', ylab = 'State Correlation')
    compare_models(objs, type = 'likelihood',
                   plot = TRUE,
                   xlab = 'Model', ylab = 'Likelihood')

As the number of states increases, one of the states in the smaller model would be split into more than one and its emission probabilities would have higher correlations with the states in the larger model.

# Interpreting models parameters

This section deals with the output of the model which are saved separately in the slots of the `segmentation` object. As mentioned before, the package provides functions to access these slots and interact with it for purposes of visualization or computing summaries.

## Emissions & transitions

The first and most important of the model output are the emissions and transitions probabilities. Emission is the frequency of a particular histone mark in a given chromatin state. Transition is the frequency by which a state (rows) transitions to another (column). These probabilities capture the spatial relationships between the markers (emission) and the states (transition).

To access these probabilities, we use accessors of the corresponding names. The output in both cases is a matrix of values between 0 and 1. The emissions matrix has a row for each state and a columns for each marker. The transition matrix has a rows (from) and columns (to) for each state.

    # access object slots
    emission(obj)
    #>        H3K27me3      H3K4me3       H3K9ac      H3K27ac      H3K4me2
    #> [1,] 0.02210946 0.0007877468 0.0002115838 0.0003912198 0.0003992906
    #> [2,] 0.01154739 0.0100515031 0.0093233199 0.0211596897 0.0330031299
    #> [3,] 0.05276061 0.6906489856 0.6030276625 0.6487866066 0.8252990950
    #>               WCE    H3K4me1        CTCF    H4K20me1   H3K36me3
    #> [1,] 0.0003263765 0.00150328 0.004697351 0.004263375 0.00121958
    #> [2,] 0.0034382153 0.21393308 0.052151182 0.048625137 0.24715916
    #> [3,] 0.0339041491 0.68908408 0.157048603 0.107478936 0.14438998
    transition(obj)
    #>              X1          X2          X3
    #> [1,] 0.99019055 0.008790184 0.001019266
    #> [2,] 0.05818283 0.907774152 0.034043020
    #> [3,] 0.02226795 0.114866997 0.862865050

The `plot_heatmap` takes the `segmentation` object and visualize the slot in `type`. By default, this is `emission`. The output is a `Heatmap` object from the `ComplexHeatmap` package. These objects are very flexible and can be customized to produce diverse informative figures.

    # emission and transition plots
    h1 <- plot_heatmap(obj,
                       row_labels = paste('S', 1:3),
                       name = 'Emission')

    h2 <- plot_heatmap(obj,
                       type = 'transition',
                       row_labels = paste('S', 1:3),
                       column_labels = paste('S', 1:3),
                       name = 'Transition')
    h1 + h2

Here, the `emission` and `transition` probabilities are combined in one heatmap.

## Overlap Enrichemnt

The `overlap` slots contains the fold enrichment of each state in the genomic coordinates provided in the main call. The enrichment is calculated by first dividing the number of bases in a state and an annotation and the number of bases in an annotation and in the genome.

These values can be accessed and visualized using `overlap` and `plot_heatmap`.

    # overlap enrichment
    overlap(obj)
    #> $K562
    #>   Genome.. CpGIsland.hg18.bed.gz RefSeqExon.hg18.bed.gz RefSeqGene.hg18.bed.gz
    #> 1 84.12164               0.44121                0.67556                0.89779
    #> 2 12.25670               0.83856                2.50467                1.61138
    #> 3  3.62166              14.52551                3.44377                1.30507
    #>   RefSeqTES.hg18.bed.gz RefSeqTSS.hg18.bed.gz RefSeqTSS2kb.hg18.bed.gz
    #> 1               0.72611               0.47881                  0.63069
    #> 2               2.35117               1.08139                  1.36524
    #> 3               2.78906              12.83038                  8.34190
    #>   laminB1lads.hg18.bed.gz
    #> 1                 1.13940
    #> 2                 0.25741
    #> 3                 0.27527
    #>
    #> $GM12878
    #>   Genome.. CpGIsland.hg18.bed.gz RefSeqExon.hg18.bed.gz RefSeqGene.hg18.bed.gz
    #> 1 84.68675               0.36002                0.66630                0.87837
    #> 2 11.37758               1.02611                2.69113                1.74670
    #> 3  3.93567              14.69536                3.29165                1.45856
    #>   RefSeqTES.hg18.bed.gz RefSeqTSS.hg18.bed.gz RefSeqTSS2kb.hg18.bed.gz
    #> 1               0.74512               0.47684                  0.66201
    #> 2               2.43884               1.06522                  1.16491
    #> 3               2.32497              12.06876                  7.79610
    #>   laminB1lads.hg18.bed.gz
    #> 1                 1.12892
    #> 2                 0.27933
    #> 3                 0.30929

An important thing to note here is that the enrichment is calculated for each cell or condition separately. And comparing these values between them can be very useful.

    # overlap enrichment plots
    plot_heatmap(obj,
                 type = 'overlap',
                 column_labels = c('Genome', 'CpG', 'Exon', 'Gene',
                                   'TES', 'TSS', 'TSS2kb', 'laminB1lads'),
                 show_heatmap_legend = FALSE)

In this example, eight different types of coordinates or annotations were included in the call. Those are shown in the columns of the heatmap and the fold enrichment of each state in the rows.

## Genomic locations enrichment

A similar fold enrichment is calculated for the regions around the transcription start (TSS) and end (TES) sits which are defined in the `anchordir` directory. Accessors of the same name and plotting functions are provided. These values are also computed for each cell/condition separately.

    # genomic locations enrichment
    TSS(obj)
    #> $K562
    #>       X.2000  X.1800  X.1600  X.1400  X.1200  X.1000    X.800    X.600    X.400
    #> [1,] 0.70688 0.68787 0.66703 0.64863 0.61553 0.58733  0.55667  0.52479  0.49843
    #> [2,] 2.01130 1.96501 1.85561 1.67047 1.62839 1.49375  1.32123  1.13609  0.98882
    #> [3,] 4.38597 4.98405 5.83846 6.89223 7.80360 8.91434 10.21019 11.57724 12.68798
    #>         X.200       X0     X200     X400     X600     X800    X1000    X1200
    #> [1,]  0.48494  0.47881  0.47513  0.48372  0.50456  0.52295  0.54012  0.55361
    #> [2,]  1.03090  1.08139  0.98461  0.92991  0.90466  0.94253  1.03931  1.26232
    #> [3,] 12.85886 12.83038 13.24334 13.22910 12.83038 12.27501 11.54876 10.48075
    #>        X1400   X1600   X1800   X2000
    #> [1,] 0.57752 0.59591 0.60695 0.61982
    #> [2,] 1.46009 1.69572 1.90190 2.06600
    #> [3,] 9.25610 8.03145 7.07736 6.22295
    #>
    #> $GM12878
    #>       X.2000  X.1800  X.1600  X.1400  X.1200  X.1000   X.800    X.600    X.400
    #> [1,] 0.79777 0.77828 0.75331 0.72347 0.69303 0.64613 0.60229  0.55966  0.52373
    #> [2,] 1.50944 1.50944 1.46865 1.39612 1.27827 1.22841 1.14228  1.01989  0.77059
    #> [3,] 3.87878 4.29810 4.95330 5.80506 6.80096 7.95411 9.14658 10.41766 11.91151
    #>         X.200       X0     X200     X400     X600     X800    X1000    X1200
    #> [1,]  0.48597  0.47684  0.47379  0.49023  0.50180  0.51764  0.54139  0.55539
    #> [2,]  1.01989  1.06522  0.82498  0.87484  0.90204  0.96097  0.99270  1.07429
    #> [3,] 12.00324 12.06876 12.82879 12.33084 12.00324 11.49219 10.88940 10.35214
    #>        X1400   X1600   X1800   X2000
    #> [1,] 0.57914 0.60899 0.62299 0.63700
    #> [2,] 1.23294 1.38706 1.61370 1.81314
    #> [3,] 9.38245 8.29482 7.33823 6.46026
    TES(obj)
    #> $K562
    #>       X.2000  X.1800  X.1600  X.1400  X.1200  X.1000   X.800   X.600   X.400
    #> [1,] 0.71834 0.71481 0.71975 0.71975 0.72328 0.71834 0.71551 0.71128 0.71551
    #> [2,] 2.37056 2.41904 2.40934 2.42389 2.39965 2.39480 2.34632 2.36571 2.37056
    #> [3,] 2.90390 2.82187 2.73984 2.69062 2.69062 2.82187 3.05156 3.08437 2.96952
    #>        X.200      X0    X200    X400    X600    X800   X1000   X1200   X1400
    #> [1,] 0.71904 0.72611 0.72470 0.72893 0.72823 0.73247 0.74094 0.74235 0.74730
    #> [2,] 2.39965 2.35117 2.30269 2.25422 2.25422 2.24452 2.20089 2.15241 2.14272
    #> [3,] 2.78906 2.78906 2.98593 3.05156 3.06796 3.00234 2.95312 3.08437 3.00234
    #>        X1600   X1800   X2000
    #> [1,] 0.74942 0.75012 0.76001
    #> [2,] 2.16211 2.12333 2.05546
    #> [3,] 2.88749 3.00234 3.00234
    #>
    #> $GM12878
    #>       X.2000  X.1800  X.1600  X.1400  X.1200  X.1000   X.800   X.600   X.400
    #> [1,] 0.72407 0.73038 0.73249 0.73389 0.72968 0.73389 0.73319 0.74161 0.74161
    #> [2,] 2.51717 2.48062 2.47017 2.46495 2.48584 2.50151 2.50151 2.42317 2.42317
    #> [3,] 2.55143 2.52123 2.50614 2.49104 2.52123 2.38536 2.40046 2.44575 2.44575
    #>        X.200      X0    X200    X400    X600    X800   X1000   X1200   X1400
    #> [1,] 0.74442 0.74512 0.73950 0.74582 0.75284 0.76757 0.77038 0.77459 0.77669
    #> [2,] 2.39706 2.43884 2.41795 2.37095 2.31350 2.16728 2.12550 2.10983 2.12028
    #> [3,] 2.46085 2.32497 2.50614 2.50614 2.52123 2.62692 2.68730 2.64201 2.56653
    #>        X1600   X1800   X2000
    #> [1,] 0.79353 0.79283 0.79563
    #> [2,] 2.06805 2.04194 2.00538
    #> [3,] 2.35517 2.44575 2.49104

    # genomic locations enrichment plots
    h1 <- plot_heatmap(obj,
                       type = 'TSS',
                       show_heatmap_legend = FALSE)
    h2 <- plot_heatmap(obj,
                       type = 'TES',
                       show_heatmap_legend = FALSE)

    h1 + h2

## Segments

The last model output is called `segment` and contains the assignment of the states to the genome. This is also provided for each cell/condition in the form of a `GRanges` object with the chromosome name, start and end sites in the ranges part of the object and the name of the state in a metadata columns.

    # get segments
    segment(obj)
    #> $K562
    #> GRanges object with 16280 ranges and 1 metadata column:
    #>           seqnames              ranges strand |       state
    #>              <Rle>           <IRanges>  <Rle> | <character>
    #>       [1]    chr11             0-50800      * |          E1
    #>       [2]    chr11         50800-52400      * |          E2
    #>       [3]    chr11         52400-57800      * |          E1
    #>       [4]    chr11         57800-58000      * |          E2
    #>       [5]    chr11         58000-58200      * |          E3
    #>       ...      ...                 ...    ... .         ...
    #>   [16276]    chr11 134227400-134243000      * |          E1
    #>   [16277]    chr11 134243000-134244200      * |          E2
    #>   [16278]    chr11 134244200-134450800      * |          E1
    #>   [16279]    chr11 134450800-134451600      * |          E2
    #>   [16280]    chr11 134451600-134452200      * |          E3
    #>   -------
    #>   seqinfo: 1 sequence from an unspecified genome; no seqlengths
    #>
    #> $GM12878
    #> GRanges object with 14009 ranges and 1 metadata column:
    #>           seqnames              ranges strand |       state
    #>              <Rle>           <IRanges>  <Rle> | <character>
    #>       [1]    chr11             0-66800      * |          E1
    #>       [2]    chr11         66800-67600      * |          E2
    #>       [3]    chr11        67600-116200      * |          E1
    #>       [4]    chr11       116200-116400      * |          E3
    #>       [5]    chr11       116400-117000      * |          E2
    #>       ...      ...                 ...    ... .         ...
    #>   [14005]    chr11 133963800-134243400      * |          E1
    #>   [14006]    chr11 134243400-134244200      * |          E2
    #>   [14007]    chr11 134244200-134450800      * |          E1
    #>   [14008]    chr11 134450800-134451600      * |          E2
    #>   [14009]    chr11 134451600-134452200      * |          E3
    #>   -------
    #>   seqinfo: 1 sequence from an unspecified genome; no seqlengths

To visualize these segments, we can take advantage of Bioconductor annotation and visualization tools to subset and render a visual representation of the segments on a given genomic region.

As an example, we extracted the genomic coordinates of the gene ‘ACAT1’ on chromosome 11 and resized it to 10kb around the transcription start site. We then used `Gviz`’s `AnnotationTrack` to render the ranges as tracks grouped by the `state` column in the `GRanges` object for each of the cell lines.

    # gene gene coordinates
    gen <- genes(TxDb.Hsapiens.UCSC.hg18.knownGene,
                 filter = list(gene_id = 38))

    # extend genomic region
    prom <- promoters(gen,
                      upstream = 10000,
                      downstream = 10000)

    # annotation track
    segs1 <- segment(obj, 'K562')
    atrack1 <- AnnotationTrack(segs1$K562,
                              group = segs1$K562$state,
                              name = 'K562')

    segs2 <- segment(obj, 'GM12878')
    atrack2 <- AnnotationTrack(segs2$GM12878,
                              group = segs2$GM12878$state,
                              name = 'GM12878')

    # plot the track
    plotTracks(atrack1, from = start(prom), to = end(prom))

    plotTracks(atrack2, from = start(prom), to = end(prom))

Other tracks can be added to the plot to make it more informative. Here, we used

- `IdeogramTrack` to show a graphic representation of chromosome 11
- `GenomeAxisTrack` to show a scale of the exact location on the chromosome
- `GeneRegionTrack` to show the exon, intron and transcripts of the target gene

Those can be put together in one plot using `plotTracks`

    # ideogram track
    itrack <- IdeogramTrack(genome = 'hg18', chromosome = 11)

    # genome axis track
    gtrack <- GenomeAxisTrack()

    # gene region track
    data("geneModels")
    grtrack <- GeneRegionTrack(geneModels,
                               genom = 'hg18',
                               chromosome = 11,
                               name = 'ACAT1')

    # put all tracks together
    plotTracks(list(itrack, gtrack, grtrack, atrack1, atrack2),
               from = min(start(prom)),
               to = max(end(gen)),
               groupAnnotation = 'group')

Moreover, we can summarize the segmentation output in different ways to either show how the combination of chromatin markers are arranged or to compare different cells and condition.

One simple summary, is to count the occurrence of states across the genome. `get_frequency` does that and returns the output in tabular or graphic formats.

    # get segment frequency
    get_frequency(segment(obj), tidy = TRUE)
    #>   state frequency    cell
    #> 1    E1      5150    K562
    #> 2    E2      7489    K562
    #> 3    E3      3641    K562
    #> 4    E1      4371 GM12878
    #> 5    E2      6359 GM12878
    #> 6    E3      3279 GM12878

The frequency of the states in each cell can also be normalized by the total number of states to make comparing across cell and condition easier.

    # frequency plots
    par(mfrow=c(1, 2))
    get_frequency(segment(obj),
                  plot = TRUE,
                  ylab = 'Segment Frequency')

    get_frequency(segment(obj),
                  normalize = TRUE,
                  plot = TRUE,
                  ylab = 'Segment Fraction')

# Final remarks

To conclude, the chromatin states models - Emissions and transition probabilities show the frequency with which histone marker or their combination occur across the genome (states). The meaning of these states depends on the biological significance of the markers. Some markers associate with particular regions or (e.g. promoters, enhancers, etc) or configurations (e.g. active, repressed, etc). - Fold-enrichment can be useful in defining the regions in which certain states occur or how they change in frequency between cells or conditions. - The segmentation of the genome on which these probabilities are defined can be used to visualize or integrate this information in other analyses such as over-representation or investigating the regulation of specific regions of interest.

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