Abstract
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.
Keywords
Chromatin Segmentation, ChromHMM, Histone Modification, ChIP-Seq
Methods
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 [@Ernst2017].
ChromHMM
ChromHmm 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.
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
Findings
Segmentation analysis using segmenter
Inputs
ChromHMM requires two types of input files. Those are
- Genomic annotation files.
- Binarized signal files from the ChIP-seq data (Check the package vignette to see how to generate those)
ChromHMM contains pre-formatted files for commonly used genomes. We will
be using the human genome (hg18) which is available from the chromhmmData
package.
## 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" "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"
## 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"
## 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.
## 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)
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 modelsemission: the probabilities of each mark being part of a given statetransition: the probabilities of each state transition to/from anotheroverlap: the enrichment of the states at every genomic featuresTSS: the enrichment of the states around the transcription start sitesTES: the enrichment of the states around the transcription end sitessegment: the assignment of states to every bin in the genomebins: the binarize inputscounts: 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.
Emissions & transitions
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 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
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 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
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 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
TES(obj)
## $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
## 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
IdeogramTrackto show a graphic representation of chromosome 11GenomeAxisTrackto show a scale of the exact location on the chromosomeGeneRegionTrackto 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')
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_modelstakes a list ofsegmentationobjects 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
typeargument.
## 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.
Concluding 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.
Availability of supporting source code and requirements
List the following:
- Project name: segmenter
- Project home page: https://github.com/MahShaaban/segmenter
- Operating system(s): Platform independent
- Programming language: R
- Other requirements: R 4.1 or higher
- License: GPL-3
Declarations
Competing interests
The authors declare no conflict of interest.
Funding
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].
Author contributions
Mahmoud Ahmed developed and maintains the package. Deok Ryong Kim supervised the project.