HiC vignette for GenomicInteractions package
Introduction
This vignette shows you how GenomicInteractions can be used to investigate significant interactions in HiC data that has been analysed using HOMER software [1]. GenomicInteractions can take a HOMER interaction file as input.
HiC data comes from chromosome conformation capture followed by high-throughput sequencing. Unlike 3C, 4C or 5C, which target specific regions, it can provide genome-wide information about the spatial proximity of regions of the genome. The raw data takes the form of paired-end reads connecting restriction fragments. The resolution of a HiC experiment is limited by the number of paired-end sequencing reads produced and by the sizes of restriction fragments. To increase the power to distinguish real interactions from random noise, HiC data is commonly analysed in bins from 20kb - 1Mb. There are a variety of tools available for binning the data, controlling for noise (e.g. self-ligations of restriction fragments), and finding significant interactions.
The data we are using comes from this paper [2] and can be downloaded from GEO. It is HiC data from wild type mouse double positive thymocytes. The experiment was carried out using the HindIII restriction enzyme. The paired-end reads were aligned to the mm9 mouse genome assembly and HOMER software was used to filter reads and detect significant interactions at a resolution of 100kb. For the purposes of this vignette, we will consider only data from chromosomes 14 and 15.
Making a GenomicInteractions object
Load the data by specifying the file location and experiment type. You can also include an experiment name and description.
hic_file <- system.file("extdata", "Seitan2013_WT_100kb_interactions.txt",
package="GenomicInteractions")
hic_data <- makeGenomicInteractionsFromFile(hic_file,
type="homer",
experiment_name = "HiC 100kb",
description = "HiC 100kb resolution")
seqlengths(hic_data) <- c(chr15 = 103494974, chr14 = 125194864)The GenomicInteractions class is an extension of the
GInteractions class from the InteractionSet
package. The object contains a set of regions involved in interactions,
stored as a GRanges object, and two sets of indices giving
the regions involved in each interaction (the anchors). Metadata for
each interaction (e.g. p-value, FDR) is stored as a
DataFrame accessed by mcols() or
elementMetadata(), similar to the metadata of a simple
GRanges. You can also access single metadata columns using
$.
## GenomicInteractions object with 23276 interactions and 14 metadata columns:
## seqnames1 ranges1 seqnames2 ranges2 |
## <Rle> <IRanges> <Rle> <IRanges> |
## [1] chr15 97600000-97699999 --- chr15 97500000-97599999 |
## [2] chr15 74800000-74899999 --- chr15 74700000-74799999 |
## [3] chr14 55000000-55099999 --- chr14 54800000-54899999 |
## [4] chr15 80400000-80499999 --- chr15 80300000-80399999 |
## [5] chr14 55100000-55199999 --- chr14 54800000-54899999 |
## ... ... ... ... ... ... .
## [23272] chr15 82900000-82999999 --- chr15 6500000-6599999 |
## [23273] chr15 100500000-100599999 --- chr15 89000000-89099999 |
## [23274] chr15 46500000-46599999 --- chr15 19200000-19299999 |
## [23275] chr14 58500000-58599999 --- chr14 15100000-15199999 |
## [23276] chr14 72100000-72199999 --- chr14 47000000-47099999 |
## counts InteractionID PeakID.1. strand.1.
## <integer> <character> <character> <character>
## [1] 344 interaction66 chr15-97600000 +
## [2] 373 interaction94 chr15-74800000 +
## [3] 258 interaction103 chr14-55000000 +
## [4] 397 interaction118 chr15-80400000 +
## [5] 213 interaction122 chr14-55100000 +
## ... ... ... ... ...
## [23272] 7 interaction279065 chr15-82900000 +
## [23273] 9 interaction279070 chr15-100500000 +
## [23274] 9 interaction279096 chr15-46500000 +
## [23275] 8 interaction279101 chr14-58500000 +
## [23276] 10 interaction279102 chr14-72100000 +
## Total.Reads.1. PeakID.2. strand.2. Total.Reads.2. Distance
## <integer> <character> <character> <integer> <character>
## [1] 7144 chr15-97500000 + 8598 80527
## [2] 8002 chr15-74700000 + 11112 93528
## [3] 7617 chr14-54800000 + 11577 198082
## [4] 9403 chr15-80300000 + 11387 80980
## [5] 6775 chr14-54800000 + 11577 298783
## ... ... ... ... ... ...
## [23272] 9936 chr15-6500000 + 12876 76405433
## [23273] 8840 chr15-89000000 + 11127 11504595
## [23274] 13170 chr15-19200000 + 9540 27303452
## [23275] 14212 chr14-15100000 + 8057 43413143
## [23276] 11299 chr14-47000000 + 13665 25090445
## Expected.Reads Z.score LogP
## <numeric> <numeric> <numeric>
## [1] 59.6631 4.88746 -327.744
## [2] 79.8436 4.30370 -290.945
## [3] 37.4725 3.64848 -284.012
## [4] 94.9088 3.99679 -274.607
## [5] 25.4555 3.55835 -271.035
## ... ... ... ...
## [23272] 1.52056 1.53689 -6.90821
## [23273] 2.45306 1.30185 -6.90814
## [23274] 2.45312 1.31508 -6.90780
## [23275] 1.97143 1.40053 -6.90776
## [23276] 2.96131 1.24805 -6.90776
## FDR.Benjamini..based.on.3.68e.08.total.tests. Circos.Thickness
## <numeric> <integer>
## [1] 0 30
## [2] 0 26
## [3] 0 26
## [4] 0 24
## [5] 0 24
## ... ... ...
## [23272] 1 2
## [23273] 1 2
## [23274] 1 2
## [23275] 1 2
## [23276] 1 2
## -------
## regions: 2154 ranges and 0 metadata columns
## seqinfo: 2 sequences from an unspecified genome## DataFrame with 23276 rows and 14 columns
## counts InteractionID PeakID.1. strand.1. Total.Reads.1.
## <integer> <character> <character> <character> <integer>
## 1 344 interaction66 chr15-97600000 + 7144
## 2 373 interaction94 chr15-74800000 + 8002
## 3 258 interaction103 chr14-55000000 + 7617
## 4 397 interaction118 chr15-80400000 + 9403
## 5 213 interaction122 chr14-55100000 + 6775
## ... ... ... ... ... ...
## 23272 7 interaction279065 chr15-82900000 + 9936
## 23273 9 interaction279070 chr15-100500000 + 8840
## 23274 9 interaction279096 chr15-46500000 + 13170
## 23275 8 interaction279101 chr14-58500000 + 14212
## 23276 10 interaction279102 chr14-72100000 + 11299
## PeakID.2. strand.2. Total.Reads.2. Distance Expected.Reads
## <character> <character> <integer> <character> <numeric>
## 1 chr15-97500000 + 8598 80527 59.6631
## 2 chr15-74700000 + 11112 93528 79.8436
## 3 chr14-54800000 + 11577 198082 37.4725
## 4 chr15-80300000 + 11387 80980 94.9088
## 5 chr14-54800000 + 11577 298783 25.4555
## ... ... ... ... ... ...
## 23272 chr15-6500000 + 12876 76405433 1.52056
## 23273 chr15-89000000 + 11127 11504595 2.45306
## 23274 chr15-19200000 + 9540 27303452 2.45312
## 23275 chr14-15100000 + 8057 43413143 1.97143
## 23276 chr14-47000000 + 13665 25090445 2.96131
## Z.score LogP FDR.Benjamini..based.on.3.68e.08.total.tests.
## <numeric> <numeric> <numeric>
## 1 4.88746 -327.744 0
## 2 4.30370 -290.945 0
## 3 3.64848 -284.012 0
## 4 3.99679 -274.607 0
## 5 3.55835 -271.035 0
## ... ... ... ...
## 23272 1.53689 -6.90821 1
## 23273 1.30185 -6.90814 1
## 23274 1.31508 -6.90780 1
## 23275 1.40053 -6.90776 1
## 23276 1.24805 -6.90776 1
## Circos.Thickness
## <integer>
## 1 30
## 2 26
## 3 26
## 4 24
## 5 24
## ... ...
## 23272 2
## 23273 2
## 23274 2
## 23275 2
## 23276 2## [1] -327.74 -290.94 -284.01 -274.61 -271.04 -234.65The set of all regions included in the object can be accessed using
regions(), or the first and second anchors of the
interactions can be accessed using anchors(). You can also
choose to just return the indices of the ranges of
regions() that correspond to the anchors.
We also provide convenience functions anchorOne and
anchorTwo to return the first/second anchors as
GRanges.
## GRanges object with 2154 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr15 3000000-3099999 *
## [2] chr15 3100000-3199999 *
## [3] chr15 3200000-3299999 *
## [4] chr15 3300000-3399999 *
## [5] chr15 3400000-3499999 *
## ... ... ... ...
## [2150] chr14 124700000-124799999 *
## [2151] chr14 124800000-124899999 *
## [2152] chr14 124900000-124999999 *
## [2153] chr14 125000000-125099999 *
## [2154] chr14 125100000-125189535 *
## -------
## seqinfo: 2 sequences from an unspecified genome## GRanges object with 23276 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr15 97600000-97699999 *
## [2] chr15 74800000-74899999 *
## [3] chr14 55000000-55099999 *
## [4] chr15 80400000-80499999 *
## [5] chr14 55100000-55199999 *
## ... ... ... ...
## [23272] chr15 82900000-82999999 *
## [23273] chr15 100500000-100599999 *
## [23274] chr15 46500000-46599999 *
## [23275] chr14 58500000-58599999 *
## [23276] chr14 72100000-72199999 *
## -------
## seqinfo: 2 sequences from an unspecified genome## [1] 939 711 1458 767 1459 629## GRanges object with 23276 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr15 97600000-97699999 *
## [2] chr15 74800000-74899999 *
## [3] chr14 55000000-55099999 *
## [4] chr15 80400000-80499999 *
## [5] chr14 55100000-55199999 *
## ... ... ... ...
## [23272] chr15 82900000-82999999 *
## [23273] chr15 100500000-100599999 *
## [23274] chr15 46500000-46599999 *
## [23275] chr14 58500000-58599999 *
## [23276] chr14 72100000-72199999 *
## -------
## seqinfo: 2 sequences from an unspecified genomeWe can check that the anchors are of the expected size (100kb).
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 89536 100000 100000 99991 100000 100000Some anchors are shorter than 100kb due to the bin being at the end
of a chromosome. There are 23276 interactions in total, with a total of
447000 reads supporting them. To calculate the average number of reads
per interaction, first use interactionCounts() to get the
number of reads supporting each individual interaction.
## [1] 344 373 258 397 213 441## [1] 19.204However, since we have FDRs and p-values, it is probably more informative to use these to find interactions of interest. Note that the FDR column in the dataset will be named differently depending on the number of interactions in your data. For simplicity in this document we will rename it!
Summary statistics
The package provides some functions to plot summary statistics of your data that may be of interest, such as the percentage of interactions that are between regions on the same chromosome (cis-interactions) or on different chromosomes (trans-interactions), or the number of reads supporting each interaction. These plots can be used to assess the level of noise in your dataset - the presence of many interactions with high FDRs or low read counts suggests that the data may be noisy and contain a lot of false positive interactions. You can subset the GenomicInteractions object by FDR or by number of reads.
## [1] 8171
Subsetting by FDR will tend to remove interactions that are supported by fewer reads. Trans interactions tend to have lower read support than cis interactions, so the percentage of trans interactions decreases.
Annotation
One of the most powerful features of GenomicInteractions is that it allows you to annotate interactions by whether the anchors overlap genomic features of interest, such as promoters or enhancers.
Genome annotation data can be obtained from, for example, UCSC databases using the GenomicFeatures package. We will use promoters of Refseq genes extended to a width of 5kb. Downloading all the data can be a slow process, so the data for promoters for chromosomes 14 and 15 is provided with this package.
We will also use a set of putative enhancers defined in Shen et al 2012 using mouse ENCODE data.
## Not run
library(GenomicFeatures)
mm9.refseq.db <- makeTxDbFromUCSC(genome="mm9", table="refGene")
refseq.genes = genes(mm9.refseq.db)
refseq.transcripts = transcriptsBy(mm9.refseq.db, by="gene")
refseq.transcripts = refseq.transcripts[ names(refseq.transcripts) %in% unlist(refseq.genes$gene_id) ]
mm9_refseq_promoters <- promoters(refseq.transcripts, 2500,2500)
mm9_refseq_promoters <- unlist(mm9_refseq_promoters[seqnames(mm9_refseq_promoters) %in% c("chr14", "chr15")])
mm9_refseq_promoters <- unique(mm9_refseq_promoters) # some duplicate promoters from different transcript isoforms
#get gene symbols
mart = useMart("ensembl", dataset="mmusculus_gene_ensembl")
genes <- getBM(attributes = c("mgi_symbol", "refseq_mrna"), filter = "refseq_mrna",
values = mm9_refseq_promoters$tx_name, mart = mart)
mm9_refseq_promoters$geneSymbol <- genes$mgi_symbol[match(mm9_refseq_promoters$tx_name, genes$refseq_mrna)]
names(mm9_refseq_promoters) <- mm9_refseq_promoters$geneSymbol
na.symbol <- is.na(names(mm9_refseq_promoters))
names(mm9_refseq_promoters)[na.symbol] <- mm9_refseq_promoters$tx_name[na.symbol]#Not run
## get enhancers from http://chromosome.sdsc.edu/mouse/download.html
download.file("http://chromosome.sdsc.edu/mouse/download/thymus.zip", "thymus.zip")
unzip("thymus.zip")
thymus_enh <- read.table("thymus/thymus.enhancer.txt", sep="\t", stringsAsFactors = FALSE)
thymus_enh <- GRanges(seqnames=thymus_enh$V1, ranges=IRanges(thymus_enh$V2, width=1))
thymus_enh <- resize(thymus_enh, fix="center", width=500)
thymus_enh <- thymus_enh[seqnames(thymus_enh) %in% c("chr14", "chr15")]
names(thymus_enh) <- paste("ENH", as.character(thymus_enh), sep = "_")annotateInteractions takes a list of features in GRanges
or GRangesList format and annotates the interaction anchors based on
overlap with these features. The list of annotation features should have
descriptive names, as these names are stored in the annotated
GenomicInteractions object and used to assign anchor (node) classes.
data("mm9_refseq_promoters")
data("thymus_enhancers")
annotation.features <- list(promoter = mm9_refseq_promoters, enhancer = thymus_enh)
annotateInteractions(hic_data_subset, annotation.features)## Warning in annotateInteractions(hic_data_subset, annotation.features): Some
## features contain duplicate IDs which will result in duplicate annotations## Annotating with promoter ...## Annotating with enhancer ...In addition, the features themselves should have names or IDs. These
can be the names() of the feature object, or an “id”
metadata column (note lowercase). These names or IDs for each feature
are stored in the metadata columns of the regions of the
GenomicInteractions object. Each anchor may overlap multiple features of
each type, so the columns containing feature names or IDs are stored as
lists.
## GRanges object with 6 ranges and 3 metadata columns:
## seqnames ranges strand | node.class promoter.id
## <Rle> <IRanges> <Rle> | <character> <list>
## [1] chr15 3000000-3099999 * | distal <NA>
## [2] chr15 3100000-3199999 * | enhancer <NA>
## [3] chr15 3200000-3299999 * | promoter Ccdc152,Sepp1,Sepp1,...
## [4] chr15 3300000-3399999 * | distal <NA>
## [5] chr15 3400000-3499999 * | distal <NA>
## [6] chr15 3500000-3599999 * | promoter Ghr,Ghr
## enhancer.id
## <list>
## [1] <NA>
## [2] ENH_chr15:3122350-31..,ENH_chr15:3180350-31..,ENH_chr15:3183250-31..
## [3] ENH_chr15:3214450-32..,ENH_chr15:3259750-32..
## [4] <NA>
## [5] <NA>
## [6] <NA>
## -------
## seqinfo: 2 sequences from an unspecified genome## [[1]]
## [1] NA
##
## [[2]]
## [1] NA
##
## [[3]]
## [1] "Ccdc152" "Sepp1" "Sepp1" "Sepp1"
##
## [[4]]
## [1] NA
##
## [[5]]
## [1] NA
##
## [[6]]
## [1] "Ghr" "Ghr"Node classes
Node classes (or anchor classes) are assigned to each anchor based on
overlap with annotation features and the order of those features within
the list passed to the annotation function. If the list is
list(promoter=..., transcript=...) then an anchor which
overlaps both a promoter and a transcript will be given the node class
“promoter”. The features earlier in the list take priority. Any anchors
which are not annotated with any of the given features will be assigned
the class “distal”. In this case anchors can be “promoter”, “enhancer”,
or “distal”.
As the anchors are large, most of them overlap at least one promoter or enhancer.
##
## distal enhancer promoter
## 989 275 890Interaction types
Interaction types are determined by the classes of the interacting nodes. As we only have two node classes, we have three possible interaction classes, summarised in the plot below. Most of the interactions are between promoters. We can subset the data to look at interaction types that are of particular interest.
Distal regions interacting with a promoter may contain regulatory elements such as enhancers or insulators. To get all promoter–distal interactions:
## [1] 492As this is a common type of interaction of interest, there is a
function specifically for identifying these interactions (see the
reference manual or help(isInteractionType) for additional
built in interaction types). isInteractionType can be used
with any pair of node classes. There are also functions for identifying
cis or trans interactions.
## [1] 492## [1] 6However in this case we have annotated the anchors with known enhancer positions, so we can subset the data to get just enhancer–promoter interactions.
To find the strongest promoter–enhancer interaction:
hic_data_ep <- hic_data_subset[isInteractionType(hic_data_subset, "promoter", "enhancer")]
max(interactionCounts(hic_data_ep))## [1] 385## GenomicInteractions object with 1 interaction and 16 metadata columns:
## seqnames1 ranges1 seqnames2 ranges2 | counts
## <Rle> <IRanges> <Rle> <IRanges> | <integer>
## [1] chr15 59400000-59499999 --- chr15 59300000-59399999 | 385
## InteractionID PeakID.1. strand.1. Total.Reads.1. PeakID.2.
## <character> <character> <character> <integer> <character>
## [1] interaction816 chr15-59400000 + 14076 chr15-59300000
## strand.2. Total.Reads.2. Distance Expected.Reads Z.score LogP
## <character> <integer> <character> <numeric> <numeric> <numeric>
## [1] + 13320 79223 152.99 2.57926 -128.741
## FDR.Benjamini..based.on.3.68e.08.total.tests. Circos.Thickness
## <numeric> <integer>
## [1] 0 10
## p.value fdr
## <numeric> <numeric>
## [1] 1.22661e-56 0
## -------
## regions: 2154 ranges and 3 metadata columns
## seqinfo: 2 sequences from an unspecified genomeOr the most significant promoter–enhancer interaction:
## [1] 9.9935e-102## GenomicInteractions object with 1 interaction and 16 metadata columns:
## seqnames1 ranges1 seqnames2 ranges2 | counts
## <Rle> <IRanges> <Rle> <IRanges> | <integer>
## [1] chr15 59100000-59199999 --- chr15 58800000-58899999 | 250
## InteractionID PeakID.1. strand.1. Total.Reads.1. PeakID.2.
## <character> <character> <character> <integer> <character>
## [1] interaction188 chr15-59100000 + 13566 chr15-58800000
## strand.2. Total.Reads.2. Distance Expected.Reads Z.score LogP
## <character> <integer> <character> <numeric> <numeric> <numeric>
## [1] + 11010 305729 44.2523 2.90834 -232.562
## FDR.Benjamini..based.on.3.68e.08.total.tests. Circos.Thickness
## <numeric> <integer>
## [1] 0 20
## p.value fdr
## <numeric> <numeric>
## [1] 9.99354e-102 0
## -------
## regions: 2154 ranges and 3 metadata columns
## seqinfo: 2 sequences from an unspecified genomeThe distance between these interacting regions, or any interacting
regions, can be found using calculateDistances. For
trans interactions the distance will be NA.
## [1] 100000## [1] 300000## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 100000 1100000 6200000 15298995 22700000 112700000 6Visualising interactions of interest
The interaction with the highest number of counts in this dataset is between an anchor containing the promoter of a gene called Trib1, and an adjacent region containing more than ten putative enhancers.
## [[1]]
## [1] "Trib1"## [[1]]
## [1] "ENH_chr15:59317450-59317949" "ENH_chr15:59321250-59321749"
## [3] "ENH_chr15:59339600-59340099" "ENH_chr15:59348250-59348749"
## [5] "ENH_chr15:59352650-59353149" "ENH_chr15:59357650-59358149"
## [7] "ENH_chr15:59362650-59363149" "ENH_chr15:59369750-59370249"
## [9] "ENH_chr15:59380150-59380649" "ENH_chr15:59385400-59385899"
## [11] "ENH_chr15:59398650-59399149"GenomicInteractions provides methods to visualise
interactions using the Gviz package in order to investigate
regions of interest further. For example, we can view interactions in
the region around the Trib1 promoter by creating an
InteractionTrack.
Trib1_region <- resize(mm9_refseq_promoters["Trib1"], fix = "center", width = 1000000)
interaction_track <- InteractionTrack(hic_data_subset, name = "HiC", chromosome = "chr15")
plotTracks(interaction_track, chromosome="chr15",
from=start(Trib1_region), to=end(Trib1_region))
Using functions from the Gviz package we can add more
data to the plot to visualise features in this region and customise how
this data is displayed. Here interactions within the region of interest
are coloured red, and interactions with other regions of chr15 are shown
in blue. The height of the arcs representing the interactions is scaled
to the number of counts supporting them.
promoterTrack <- AnnotationTrack(mm9_refseq_promoters, genome="mm9", name="Promoters",
id=names(mm9_refseq_promoters), featureAnnotation="id")
enhTrack <- AnnotationTrack(thymus_enh, genome="mm9", name="Enhancers", stacking = "dense")
displayPars(promoterTrack) <- list(fill = "deepskyblue", col = NA,
fontcolor.feature = "black", fontsize=8,
just.group="below")
displayPars(enhTrack) <- list(fill = "black", col = NA)
displayPars(interaction_track) = list(col.interactions="red",
col.anchors.fill ="blue",
col.anchors.line = "black",
interaction.dimension="height",
interaction.measure ="counts",
plot.trans=FALSE,
plot.outside = TRUE,
col.outside="lightblue",
anchor.height = 0.1)
plotTracks(list(interaction_track, promoterTrack, enhTrack),
chromosome="chr15", from=start(Trib1_region), to=end(Trib1_region),
sizes=c(0.6, 0.2, 0.2))
You can see what customisation options are available for a Gviz track
using availableDisplayPars(), and find more information
about this and other track types in the Gviz vignette.
Export to BED12 format
Interactions stored in a GenomicInteractions object can
be exported to BED12
format for viewing in a genome browser. Anchors are visualised as
thick blocks connected by thinner interactions.
References
Heinz S, Benner C, Spann N, Bertolino E et al. Simple Combinations of Lineage-Determining Transcription Factors Prime cis-Regulatory Elements Required for Macrophage and B Cell Identities. Mol Cell (2010).
Seitan, VC et al. Cohesin-based chromatin interactions enable regulated gene expression within pre-existing architectural compartments. Genome Research (2013).
Shen, Y et al. A map of cis-regulatory sequences in the mouse genome. Nature (2012).