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<body>
<h2>Abstract</h2>
<p>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. <em>ChromHMM</em>, written in
<em>Java</em>, integrates histone modification datasets to learn the chromatin states
de-novo. We developed an <em>R</em> package around this program to leverage the
existing <em>R/Bioconductor</em> tools and data structures in the segmentation analysis
context. <code>segmenter</code> wraps the <em>Java</em> modules to call <em>ChromHMM</em> and captures
the output in an <code>S4</code> object. This allows for iterating with different
parameters, which are given in <em>R</em> syntax. Capturing the output in <em>R</em> makes it
easier to work with the results and to integrate them in downstream analyses.
Finally, <code>segmenter</code> provides additional tools to test, select and visualize the
models.</p>
<h3>Keywords</h3>
<p>Chromatin Segmentation, ChromHMM, Histone Modification, ChIP-Seq</p>
<h2>Methods</h2>
<h3>Hidden Markov Models</h3>
<p>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 [@Ernst2017].</p>
<h3><a href="http://compbio.mit.edu/ChromHMM/">ChromHMM</a></h3>
<p><em>ChromHmm</em> is a Java program to learn chromatin states from multiple sets of
histone modification markers ChIP-seq datasets [@Ernst2012]. 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.</p>
<h3>This package!</h3>
<p>The goal of the <code>segmenter</code> package is to</p>
<ul>
<li>Call <em>ChromHMM</em> using R syntax</li>
<li>Capture the output in R objects</li>
<li>Interact with the model output for the purposes of summarizing or visualizing</li>
</ul>
<h2>Findings</h2>
<h3>Segmentation analysis using <code>segmenter</code></h3>
<h4>Inputs</h4>
<p>ChromHMM requires two types of input files. Those are</p>
<ul>
<li>Genomic annotation files.</li>
<li>Binarized signal files from the ChIP-seq data (Check the package vignette to
see how to generate those)</li>
</ul>
<p>ChromHMM contains pre-formatted files for commonly used genomes. We will
be using the human genome (hg18) which is available from the <code>chromhmmData</code>
package.</p>
<pre><code class="r">## load required libraries
library(segmenter)
library(Gviz)
library(ComplexHeatmap)
library(TxDb.Hsapiens.UCSC.hg18.knownGene)
</code></pre>
<pre><code class="r">## coordinates
coordsdir <- system.file('extdata/COORDS',
package = 'chromhmmData')
list.files(file.path(coordsdir, 'hg18'))
</code></pre>
<pre><code>## [1] "CpGIsland.hg18.bed.gz" "laminB1lads.hg18.bed.gz" "RefSeqExon.hg18.bed.gz"
## [4] "RefSeqGene.hg18.bed.gz" "RefSeqTES.hg18.bed.gz" "RefSeqTSS.hg18.bed.gz"
## [7] "RefSeqTSS2kb.hg18.bed.gz"
</code></pre>
<pre><code class="r">## anchors
anchorsdir <- system.file('extdata/ANCHORFILES',
package = 'chromhmmData')
list.files(file.path(anchorsdir, 'hg18'))
</code></pre>
<pre><code>## [1] "RefSeqTES.hg18.txt.gz" "RefSeqTSS.hg18.txt.gz"
</code></pre>
<pre><code class="r">## chromosomes' sizes
chromsizefile <- system.file('extdata/CHROMSIZES',
'hg18.txt',
package = 'chromhmmData')
readLines(chromsizefile, n = 3)
</code></pre>
<pre><code>## [1] "chr1\t247249719" "chr2\t242951149" "chr3\t199501827"
</code></pre>
<pre><code class="r">## locate input and output files
inputdir <- system.file('extdata/SAMPLEDATA_HG18',
package = 'segmenter')
list.files(inputdir)
</code></pre>
<pre><code>## [1] "GM12878_chr11_binary.txt.gz" "K562_chr11_binary.txt.gz"
</code></pre>
<h4>Model learning</h4>
<p>The main function in <code>segmenter</code> is called <code>learn_model</code>. 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, <code>numstates</code> and the information that were used to
generate the binarized files. Those are the names of the genome <code>assembly</code>, the
type of <code>annotation</code>, the <code>binsize</code> and the names of <code>cells</code> or conditions.</p>
<pre><code class="r">## make an output director
outputdir <- tempdir()
## 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)
</code></pre>
<p>The return of this function call is the an S4 <code>segmentation</code> object, which we
describe next.</p>
<h3>Output <code>segmentation</code> Object</h3>
<p>The <code>show</code> 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</p>
<ul>
<li><code>model</code>: the initial and final parameters of the models</li>
<li><code>emission</code>: the probabilities of each mark being part of a given state</li>
<li><code>transition</code>: the probabilities of each state transition to/from another</li>
<li><code>overlap</code>: the enrichment of the states at every genomic features</li>
<li><code>TSS</code>: the enrichment of the states around the transcription start sites</li>
<li><code>TES</code>: the enrichment of the states around the transcription end sites</li>
<li><code>segment</code>: the assignment of states to every bin in the genome</li>
<li><code>bins</code>: the binarize inputs</li>
<li><code>counts</code>: the non-binarized counts in every bin</li>
</ul>
<p>The last two slots are empty, unless indicated otherwise in the previous call.
Counts are only loaded when the path to the <code>bam</code> files are provided.</p>
<pre><code class="r">## show the object
show(obj)
</code></pre>
<pre><code>## # 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
</code></pre>
<p>For each slot, an accessor function with the same name is provided to access its
contents. For example, to access the emission probabilities call <code>emission</code> on
the object.</p>
<h4>Emissions & transitions</h4>
<p>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).</p>
<p>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.</p>
<pre><code class="r">## access object slots
emission(obj)
</code></pre>
<pre><code>## H3K27me3 H3K4me3 H3K9ac H3K27ac H3K4me2 WCE H3K4me1
## [1,] 0.02210946 0.0007877468 0.0002115838 0.0003912198 0.0003992906 0.0003263765 0.00150328
## [2,] 0.01154739 0.0100515031 0.0093233199 0.0211596897 0.0330031299 0.0034382153 0.21393308
## [3,] 0.05276061 0.6906489856 0.6030276625 0.6487866066 0.8252990950 0.0339041491 0.68908408
## CTCF H4K20me1 H3K36me3
## [1,] 0.004697351 0.004263375 0.00121958
## [2,] 0.052151182 0.048625137 0.24715916
## [3,] 0.157048603 0.107478936 0.14438998
</code></pre>
<pre><code class="r">transition(obj)
</code></pre>
<pre><code>## X1 X2 X3
## [1,] 0.99019055 0.008790184 0.001019266
## [2,] 0.05818283 0.907774152 0.034043020
## [3,] 0.02226795 0.114866997 0.862865050
</code></pre>
<p>The <code>plot_heatmap</code> takes the <code>segmentation</code> object and visualize the slot in
<code>type</code>. By default, this is <code>emission</code>. The output is a <code>Heatmap</code> object from
the <code>ComplexHeatmap</code> package. These objects are very flexible and can be
customized to produce diverse informative figures.</p>
<pre><code class="r">## 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
</code></pre>
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" width="576" style="display: block; margin: auto;" /></p>
<p>Here, the <code>emission</code> and <code>transition</code> probabilities are combined in one heatmap.</p>
<h4>Overlap Enrichemnt</h4>
<p>The <code>overlap</code> 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 <code>overlap</code> and <code>plot_heatmap</code>.</p>
<pre><code class="r">## overlap enrichment
overlap(obj)
</code></pre>
<pre><code>## $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 laminB1lads.hg18.bed.gz
## 1 0.72611 0.47881 0.63069 1.13940
## 2 2.35117 1.08139 1.36524 0.25741
## 3 2.78906 12.83038 8.34190 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 laminB1lads.hg18.bed.gz
## 1 0.74512 0.47684 0.66201 1.12892
## 2 2.43884 1.06522 1.16491 0.27933
## 3 2.32497 12.06876 7.79610 0.30929
</code></pre>
<p>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.</p>
<pre><code class="r">## overlap enrichment plots
plot_heatmap(obj,
type = 'overlap',
column_labels = c('Genome', 'CpG', 'Exon', 'Gene',
'TES', 'TSS', 'TSS2kb', 'laminB1lads'),
show_heatmap_legend = FALSE)
</code></pre>
<p><img 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" width="576" style="display: block; margin: auto;" /></p>
<p>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.</p>
<h4>Genomic locations enrichment</h4>
<p>A similar fold enrichment is calculated for the regions around the transcription
start (TSS) and end (TES) sits which are defined in the <code>anchordir</code> directory.
Accessors of the same name and plotting functions are provided. These values are
also computed for each cell/condition separately.</p>
<pre><code class="r">## genomic locations enrichment
TSS(obj)
</code></pre>
<pre><code>## $K562
## X.2000 X.1800 X.1600 X.1400 X.1200 X.1000 X.800 X.600 X.400 X.200 X0
## [1,] 0.70688 0.68787 0.66703 0.64863 0.61553 0.58733 0.55667 0.52479 0.49843 0.48494 0.47881
## [2,] 2.01130 1.96501 1.85561 1.67047 1.62839 1.49375 1.32123 1.13609 0.98882 1.03090 1.08139
## [3,] 4.38597 4.98405 5.83846 6.89223 7.80360 8.91434 10.21019 11.57724 12.68798 12.85886 12.83038
## X200 X400 X600 X800 X1000 X1200 X1400 X1600 X1800 X2000
## [1,] 0.47513 0.48372 0.50456 0.52295 0.54012 0.55361 0.57752 0.59591 0.60695 0.61982
## [2,] 0.98461 0.92991 0.90466 0.94253 1.03931 1.26232 1.46009 1.69572 1.90190 2.06600
## [3,] 13.24334 13.22910 12.83038 12.27501 11.54876 10.48075 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 X.200 X0
## [1,] 0.79777 0.77828 0.75331 0.72347 0.69303 0.64613 0.60229 0.55966 0.52373 0.48597 0.47684
## [2,] 1.50944 1.50944 1.46865 1.39612 1.27827 1.22841 1.14228 1.01989 0.77059 1.01989 1.06522
## [3,] 3.87878 4.29810 4.95330 5.80506 6.80096 7.95411 9.14658 10.41766 11.91151 12.00324 12.06876
## X200 X400 X600 X800 X1000 X1200 X1400 X1600 X1800 X2000
## [1,] 0.47379 0.49023 0.50180 0.51764 0.54139 0.55539 0.57914 0.60899 0.62299 0.63700
## [2,] 0.82498 0.87484 0.90204 0.96097 0.99270 1.07429 1.23294 1.38706 1.61370 1.81314
## [3,] 12.82879 12.33084 12.00324 11.49219 10.88940 10.35214 9.38245 8.29482 7.33823 6.46026
</code></pre>
<pre><code class="r">TES(obj)
</code></pre>
<pre><code>## $K562
## X.2000 X.1800 X.1600 X.1400 X.1200 X.1000 X.800 X.600 X.400 X.200 X0
## [1,] 0.71834 0.71481 0.71975 0.71975 0.72328 0.71834 0.71551 0.71128 0.71551 0.71904 0.72611
## [2,] 2.37056 2.41904 2.40934 2.42389 2.39965 2.39480 2.34632 2.36571 2.37056 2.39965 2.35117
## [3,] 2.90390 2.82187 2.73984 2.69062 2.69062 2.82187 3.05156 3.08437 2.96952 2.78906 2.78906
## X200 X400 X600 X800 X1000 X1200 X1400 X1600 X1800 X2000
## [1,] 0.72470 0.72893 0.72823 0.73247 0.74094 0.74235 0.74730 0.74942 0.75012 0.76001
## [2,] 2.30269 2.25422 2.25422 2.24452 2.20089 2.15241 2.14272 2.16211 2.12333 2.05546
## [3,] 2.98593 3.05156 3.06796 3.00234 2.95312 3.08437 3.00234 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 X.200 X0
## [1,] 0.72407 0.73038 0.73249 0.73389 0.72968 0.73389 0.73319 0.74161 0.74161 0.74442 0.74512
## [2,] 2.51717 2.48062 2.47017 2.46495 2.48584 2.50151 2.50151 2.42317 2.42317 2.39706 2.43884
## [3,] 2.55143 2.52123 2.50614 2.49104 2.52123 2.38536 2.40046 2.44575 2.44575 2.46085 2.32497
## X200 X400 X600 X800 X1000 X1200 X1400 X1600 X1800 X2000
## [1,] 0.73950 0.74582 0.75284 0.76757 0.77038 0.77459 0.77669 0.79353 0.79283 0.79563
## [2,] 2.41795 2.37095 2.31350 2.16728 2.12550 2.10983 2.12028 2.06805 2.04194 2.00538
## [3,] 2.50614 2.50614 2.52123 2.62692 2.68730 2.64201 2.56653 2.35517 2.44575 2.49104
</code></pre>
<pre><code class="r">## 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
</code></pre>
<p><img 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" width="672" style="display: block; margin: auto;" /></p>
<h4>Segments</h4>
<p>The last model output is called <code>segment</code> and contains the assignment of the
states to the genome. This is also provided for each cell/condition in the form
of a <code>GRanges</code> 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.</p>
<pre><code class="r">## get segments
segment(obj)
</code></pre>
<pre><code>## $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
##
## $GM12878
## GRanges object with 14009 ranges and 1 metadata column:
## seqnames ranges strand | state
## <Rle> <IRanges> <Rle> | <character>
## [1] chr11 0-66800 * | E1
## [2] chr11 66800-67600 * | E2
## [3] chr11 67600-116200 * | E1
## [4] chr11 116200-116400 * | E3
## [5] chr11 116400-117000 * | E2
## ... ... ... ... . ...
## [14005] chr11 133963800-134243400 * | E1
## [14006] chr11 134243400-134244200 * | E2
## [14007] chr11 134244200-134450800 * | E1
## [14008] chr11 134450800-134451600 * | E2
## [14009] chr11 134451600-134452200 * | E3
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
</code></pre>
<p>To visualize these segments, we can take advantage of Bioconductor annotation
and visualization tools to subset and render a visual representation of the
segments on a given genomic region.</p>
<p>As an example, we extracted the genomic coordinates of the gene 'ACAT1' on
chromosome 11 and resized it to 10kb around the transcription start site. We
then used <code>Gviz</code>'s <code>AnnotationTrack</code> to render the ranges as tracks grouped by
the <code>state</code> column in the <code>GRanges</code> object for each of the cell lines. </p>
<pre><code class="r">## gene gene coordinates
gen <- genes(TxDb.Hsapiens.UCSC.hg18.knownGene,
filter = list(gene_id = 38))
## extend genomic region
prom <- promoters(gen,
upstream = 10000,
downstream = 10000)
## annotation track
segs1 <- segment(obj, 'K562')
atrack1 <- AnnotationTrack(segs1$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))
</code></pre>
<p><img src="data:image/png;base64,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" width="288" style="display: block; margin: auto;" /></p>
<pre><code class="r">plotTracks(atrack2, from = start(prom), to = end(prom))
</code></pre>
<p><img src="data:image/png;base64,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" width="288" style="display: block; margin: auto;" /></p>
<p>Other tracks can be added to the plot to make it more informative. Here, we used</p>
<ul>
<li><code>IdeogramTrack</code> to show a graphic representation of chromosome 11</li>
<li><code>GenomeAxisTrack</code> to show a scale of the exact location on the chromosome</li>
<li><code>GeneRegionTrack</code> to show the exon, intron and transcripts of the target gene</li>
</ul>
<p>Those can be put together in one plot using <code>plotTracks</code></p>
<pre><code class="r">## 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')
</code></pre>
<p><img 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" width="384" style="display: block; margin: auto;" /></p>
<p>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.</p>
<p>One simple summary, is to count the occurrence of states across the genome.
<code>get_frequency</code> does that and returns the output in tabular or graphic formats.</p>
<pre><code class="r">## get segment frequency
get_frequency(segment(obj), tidy = TRUE)
</code></pre>
<pre><code>## 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
</code></pre>
<p>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.</p>
<pre><code class="r">## 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')
</code></pre>
<p><img 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" width="672" style="display: block; margin: auto;" /></p>
<h3>Comparing models</h3>
<p>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 <code>learn_model</code> several times using <code>lapply</code> with the
same inputs except the number of states (<code>numstates</code>). The output would be a
list of <code>segmentation</code> objects. <code>segmenter</code> contain functions to do basic
comparison between the models.</p>
<pre><code class="r">## 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)
})
</code></pre>
<ul>
<li><code>compare_models</code> takes a list of <code>segmentation</code> 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.</li>
</ul>
<pre><code class="r">## compare the models max correlation between the states
compare_models(objs)
</code></pre>
<pre><code>## [1] 0.6992815 0.9911192 0.9935068 0.9925634 0.9936017 1.0000000
</code></pre>
<ul>
<li>The other value to compare is the likelihood of the models which can be
indicated through the <code>type</code> argument.</li>
</ul>
<pre><code class="r">## compare the models likelihood
compare_models(objs, type = 'likelihood')
</code></pre>
<pre><code>## [1] -889198.8 -834128.3 -806108.9 -790283.4 -773662.1 -766802.5
</code></pre>
<p>Setting <code>plot = TRUE</code> returns a plot with data points corresponding to the
models in the list. </p>
<pre><code class="r">## 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')
</code></pre>
<p><img 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" width="672" style="display: block; margin: auto;" /></p>
<p>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.</p>
<h2>Concluding remarks</h2>
<p>To conclude, the chromatin states models </p>
<ul>
<li>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).</li>
<li>Fold-enrichment can be useful in defining the regions in which certain states
occur or how they change in frequency between cells or conditions.</li>
<li>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.</li>
</ul>
<h3><strong>Availability of supporting source code and requirements</strong></h3>
<p>List the following:</p>
<ul>
<li> Project name: segmenter</li>
<li> Project home page: <a href="https://github.com/MahShaaban/segmenter">https://github.com/MahShaaban/segmenter</a></li>
<li> Operating system(s): Platform independent</li>
<li> Programming language: R</li>
<li> Other requirements: R 4.1 or higher</li>
<li> License: GPL-3</li>
</ul>
<h3>Declarations</h3>
<h4>Competing interests</h4>
<p>The authors declare no conflict of interest.</p>
<h4>Funding</h4>
<p>This work was supported by the National Research Foundation of Korea (NRF) grant
funded by the Ministry of Science and ICT (MSIT) of the Korea government
[2015R1A5A2008833 and 2020R1A2C2011416].</p>
<h4>Author contributions</h4>
<p>Mahmoud Ahmed developed and maintains the package. Deok Ryong Kim supervised
the project.</p>
<h3>References</h3>
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