The seqsetvis package
Synopsis
seq - set - vis
seqsetvis enables the visualization and analysis of multiple genomic datasets. Although seqsetvis was designed for the comparison of mulitple ChIP-seq datasets, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files).
seqsetvis is ideal for the following tasks:
- Feature assessment and comparison.
- Differential enrichment or expression results assessment.
- Replicate comparison and reproducibility analysis.
- Chromatin state inspection.
- Clustering of histone modification and/or transcription factor enrichment at features such as promoters or enhancers.
seqsetvis accepts and outputs the Bioconductor standard GRanges object while leveraging the power and speed of the data.table package (an enhancement of data.frame) with the flexibility of ggplot2 to provide users with visualizations that are simultaneously publication ready while still being highly customizable. By default, data.table is used entirely internally so users unfamiliar with its use can safely ignore it. That said, fans of data.table can use seqsetvis in a data.table exclusive mode if they wish.
Features
- Mostly it “just works” with minimal assumed knowledge of R or other Bioconductor packages.
- All plots produced are ggplot2 objects.
- Although various plots are implemented, the underlying data are
simple in structure and easily accessible so you can do
what you want with them.
- membership table for feature overlaps is just a logical matrix.
- signal data is stored in a tidy formatted data.table, ideal for ggplot2 visualization.
Although initially designed to focus on peak call sets, seqsetvis accepts any data in GRanges format and is therefore flexible enough to work dowstream of many genomics tools and packages. Indeed, set comparison visualizations are genomics agnostic and also accept generic data types defining sets.
Functions
ssv*- most primary user facing functions are prefixed by ssvssvOverlapIntervalSets- derives sets from a list of GRangesssvMakeMembTable- coerces various ways of defining sets to a membership tablessvFactorizeMembTable- converts a multi column membership table into a single factor.ssvFetch*- retrieves properly formatted data from .bigwig and .bam filesssvFeature*- visualizations of set counts and overlapsssvSignal*- visualizations of signal mapped to genomic coordinates in sets context
- utility functions
easyLoad_*- loads files into a list of GRanges for input intossv*functions- miscellaneous functions such as wrappers for data.table functions.
Installation and Loading
From Bioconductor
Load optional useful libraries
Defining sets
seqsetvis has several ways of accepting set information. Since seqsetvis was originally intended to focus on comparing sets of peak calls, a list of GRanges or a GRangesList is directly supported but there are more generic alternatives.
Overlapping a list of GRanges
Many functions in seqsetvis require some kind of sets
definition as input. Sets can be defined in one of several ways.
ssvOverlapIntervalSets() creates a single GRanges
from a list of GRanges with a logical membership table as
metadata columns defining sets.
olaps = ssvOverlapIntervalSets(CTCF_in_10a_narrowPeak_grs)
head(olaps)
## GRanges object with 6 ranges and 3 metadata columns:
## seqnames ranges strand | MCF10A_CTCF MCF10AT1_CTCF MCF10CA1_CTCF
## <Rle> <IRanges> <Rle> | <logical> <logical> <logical>
## 1 chr1 17507898-17508319 * | FALSE TRUE FALSE
## 2 chr1 40855546-40855866 * | TRUE FALSE FALSE
## 3 chr1 53444333-53445008 * | TRUE TRUE TRUE
## 4 chr1 64137020-64137657 * | TRUE TRUE TRUE
## 5 chr1 65075339-65076014 * | TRUE TRUE FALSE
## 6 chr1 92818291-92818802 * | TRUE TRUE TRUE
## -------
## seqinfo: 21 sequences from an unspecified genome; no seqlengthsssvOverlapIntervalSets() also accepts a
GRangesList if that’s more natural.
olaps_fromGRangesList = ssvOverlapIntervalSets(
GenomicRanges::GRangesList(CTCF_in_10a_narrowPeak_grs))The membership data contained in the metadata columns of
olaps is
the data structure seqsetvis set overlap functions use
internally.
The S4 method ssvMakeMembTable() handles the necessary
conversions and can be extended to accept a new class of data as long as
it converts to an already accepted data type and calls ssvMakeMembTable
again.
For instance, ssvMakeMembTable(olaps) extracts the
membership table from the mcols of a single GRanges object.
Overlapping a list of more generic data
ssvMakeMembTable() can handle sets defined by a list of
vectors.
These happen to be numeric but nearly anything that
as.character() works on should be acceptable.
my_set_list = list(1:3, 2:3, 3:6)
ssvMakeMembTable(my_set_list)
## set_A set_B set_C
## 1 TRUE FALSE FALSE
## 2 TRUE TRUE FALSE
## 3 TRUE TRUE TRUE
## 4 FALSE FALSE TRUE
## 5 FALSE FALSE TRUE
## 6 FALSE FALSE TRUESupplying a named list is recommended
names(my_set_list) = c("first", "second", "third")
ssvMakeMembTable(my_set_list)
## first second third
## 1 TRUE FALSE FALSE
## 2 TRUE TRUE FALSE
## 3 TRUE TRUE TRUE
## 4 FALSE FALSE TRUE
## 5 FALSE FALSE TRUE
## 6 FALSE FALSE TRUEA list of character vectors works just as well.
my_set_list_char = lapply(my_set_list, function(x)letters[x])
ssvMakeMembTable(my_set_list_char)
## first second third
## a TRUE FALSE FALSE
## b TRUE TRUE FALSE
## c TRUE TRUE TRUE
## d FALSE FALSE TRUE
## e FALSE FALSE TRUE
## f FALSE FALSE TRUENotice the rownames changed as the items changed.
Visualization of set overlaps, ssvFeature*
The ssvFeature* family of functions all accept a valid set definition and output a ggplot.
Pie chart
Pie charts are an alternative to barplots but are generally more difficult to interpret.
Venn diagram
Venn diagrams show the number of items falling into each overlap category. At release, seqsetvis is limited to displaying 3 sets via venn diagram. This will be increased in a future update.
Euler diagram
Often confused with Venn diagrams, Euler diagrams attempt to convey the size of each set and the degree to which the overlap with circles or ellipses of varying sizes. For data of any appreciable complexity, Euler diagrams are approximations by neccessity.
There is no hard limit to the number of sets seqsetvis can show in an Euler diagram but computation time increases rapidly along with the inaccuracy of approximation.
Visualization of genomic signal, ssvSignal*
The ssvSignal* family of functions all take as input continuous
signal data mapped to the genome. This type of data is generally stored
in bedGraph, wiggle, or bigWig format. At this time, seqsetvis
directly supports the bigwig format and creating pileups from indexed
.bam files.
This is accomplished via the ssvFetchBigwig() and
ssvFetchBam() functions.
If you don’t have .bigWig (or .bigwig or .bw) files you should be
able to easily generate some using rtracklayer
export.bw() alongside import.bedGraph() or
import.wig() as appropriate.
Loading bigwig data
bigwig_files = c(
system.file("extdata", "MCF10A_CTCF_FE_random100.bw",
package = "seqsetvis"),
system.file("extdata", "MCF10AT1_CTCF_FE_random100.bw",
package = "seqsetvis"),
system.file("extdata", "MCF10CA1_CTCF_FE_random100.bw",
package = "seqsetvis")
)
names(bigwig_files) = sub("_FE_random100.bw", "", basename(bigwig_files))
# names(bigwig_files) = letters[1:3]
olap_gr = CTCF_in_10a_overlaps_gr
target_size = quantile(width(olap_gr), .75)
window_size = 50
target_size = round(target_size / window_size) * window_size
olap_gr = resize(olap_gr, target_size, fix = "center")
bw_gr = ssvFetchBigwig(bigwig_files, olap_gr, win_size = window_size)
bw_grOverlap information must be transformed into a factor tied to id to be useful.
Line plots
Line plots are perhaps the most natural and common type of visualization for this type of continuous genomic data. Many genome browsers and other BioC packages support this type of visualization. These other approaches are very good at show lot’s of different data of multiple types at single regions. In contrast, seqsetvis is focused on bringing in data from multiple regions simultaneously.
Individual line plots
Basic line plot of a subset of regions
# facet labels will display better if split into multiple lines
bw_gr$facet_label = sub("_", "\n", bw_gr$sample)
ssvSignalLineplot(bw_data = subset(bw_gr, id %in% 1:12), facet_ = "facet_label")
Facetting by region id is less overwhelming
Aggregated line plots
Aggregate regions by mean
Apply spline of 10 to make a prettier plot
By adding some info to bw_gr, tweaking the group_
parameter, and adding a facet call we can apply the aggregation function
by peak call set and plot them separately.
# append set info, modify aggregation group_ and add facet
olap_2groups = ssvFactorizeMembTable(ssvMakeMembTable(olap_gr)[, 1:2])
grouped_gr = GRanges(merge(bw_gr, olap_2groups))
grouped_gr = subset(grouped_gr, sample %in% c("MCF10A_CTCF", "MCF10AT1_CTCF"))
ssvSignalLineplotAgg(bw_data = grouped_gr, spline_n = 10,
group_ = c("sample", "group")) +
facet_wrap("group", ncol = 2) +
labs(title = "Aggregated by peak call set", y = "FE", x = "bp from center")
Scatterplot
Scatterplots are a common way of viewing the relationship between two variables. seqsetvis allows users to easily apply a summary function (mean by default) to each region as measured in two samples and plot them on the x and y axis.
A basic scatterplot
Using the result of
olap_groups = ssvFactorizeMembTable(olap_gr) to add color
based on our peak calls.
ssvSignalScatterplot(bw_gr, x_name = "MCF10A_CTCF", y_name = "MCF10AT1_CTCF",
color_table = olap_groups)
More than a few colors gets confusing quickly. Let’s limit the color groups to the samples being plotted.
# by subsetting the matrix returned by ssvMakeMembTable() we have a lot of
# control over the coloring.
olap_2groups = ssvFactorizeMembTable(ssvMakeMembTable(olap_gr)[, 1:2])
ssvSignalScatterplot(bw_gr, x_name = "MCF10A_CTCF", y_name = "MCF10AT1_CTCF",
color_table = olap_2groups)
Coloring by the third sample’s peak call might be interesting.
olap_OutGroups = ssvFactorizeMembTable(
ssvMakeMembTable(olap_gr)[, 3, drop = FALSE])
ssvSignalScatterplot(bw_gr,
x_name = "MCF10A_CTCF",
y_name = "MCF10AT1_CTCF",
color_table = olap_OutGroups)
We can also get clever with ggplot2’s facetting.
#tweaking group description will clean up plot labels a lot
olap_groups$group = gsub("_CTCF", "", olap_groups$group)
olap_groups$group = gsub(" & ", "\n", olap_groups$group)
ssvSignalScatterplot(bw_gr, x_name = "MCF10A_CTCF", y_name = "MCF10AT1_CTCF",
color_table = olap_groups) +
facet_wrap("group") + guides(color = "none") +
theme_linedraw() 
Banded quantiles
These quantile bands are a more sophisticated way to aggregate the same data shown in the line plots.
ssvSignalBandedQuantiles(bw_gr, by_ = "sample", hsv_grayscale = TRUE,
hsv_symmetric = TRUE,
hsv_reverse = TRUE)
Heatmap
Heatmaps combined with clustering are a powerful way to find similarly marked regions of the genome. Calling ssvSignalHeatmap will perform k-means clustering automatically. k-means clusters sorted by similarity as determined by hierarchical clustering.
Performing clustering manually allows for use of cluster info.
bw_clust = ssvSignalClustering(bw_gr, nclust = 3)
## clustering...
bw_clust
## GRanges object with 4200 ranges and 6 metadata columns:
## seqnames ranges strand | id y x
## <Rle> <IRanges> <Rle> | <factor> <numeric> <numeric>
## [1] chr1 17507759-17507808 * | 1 1.10252 -325
## [2] chr1 17507809-17507858 * | 1 1.16055 -275
## [3] chr1 17507859-17507908 * | 1 2.12024 -225
## [4] chr1 17507909-17507958 * | 1 3.03194 -175
## [5] chr1 17507959-17508008 * | 1 9.86468 -125
## ... ... ... ... . ... ... ...
## [4196] chrX 138128377-138128426 * | 100 3.79769 125
## [4197] chrX 138128427-138128476 * | 100 1.79891 175
## [4198] chrX 138128477-138128526 * | 100 1.59903 225
## [4199] chrX 138128527-138128576 * | 100 2.19866 275
## [4200] chrX 138128577-138128626 * | 100 1.99878 325
## sample facet_label cluster_id
## <character> <character> <factor>
## [1] MCF10A_CTCF MCF10A\nCTCF 3
## [2] MCF10A_CTCF MCF10A\nCTCF 3
## [3] MCF10A_CTCF MCF10A\nCTCF 3
## [4] MCF10A_CTCF MCF10A\nCTCF 3
## [5] MCF10A_CTCF MCF10A\nCTCF 3
## ... ... ... ...
## [4196] MCF10CA1_CTCF MCF10CA1\nCTCF 3
## [4197] MCF10CA1_CTCF MCF10CA1\nCTCF 3
## [4198] MCF10CA1_CTCF MCF10CA1\nCTCF 3
## [4199] MCF10CA1_CTCF MCF10CA1\nCTCF 3
## [4200] MCF10CA1_CTCF MCF10CA1\nCTCF 3
## -------
## seqinfo: 21 sequences from an unspecified genome; no seqlengthsCluster selection:
subset(bw_clust, cluster_id == 3)
## GRanges object with 2226 ranges and 6 metadata columns:
## seqnames ranges strand | id y x
## <Rle> <IRanges> <Rle> | <factor> <numeric> <numeric>
## [1] chr1 17507759-17507808 * | 1 1.10252 -325
## [2] chr1 17507809-17507858 * | 1 1.16055 -275
## [3] chr1 17507859-17507908 * | 1 2.12024 -225
## [4] chr1 17507909-17507958 * | 1 3.03194 -175
## [5] chr1 17507959-17508008 * | 1 9.86468 -125
## ... ... ... ... . ... ... ...
## [2222] chrX 138128377-138128426 * | 100 3.79769 125
## [2223] chrX 138128427-138128476 * | 100 1.79891 175
## [2224] chrX 138128477-138128526 * | 100 1.59903 225
## [2225] chrX 138128527-138128576 * | 100 2.19866 275
## [2226] chrX 138128577-138128626 * | 100 1.99878 325
## sample facet_label cluster_id
## <character> <character> <factor>
## [1] MCF10A_CTCF MCF10A\nCTCF 3
## [2] MCF10A_CTCF MCF10A\nCTCF 3
## [3] MCF10A_CTCF MCF10A\nCTCF 3
## [4] MCF10A_CTCF MCF10A\nCTCF 3
## [5] MCF10A_CTCF MCF10A\nCTCF 3
## ... ... ... ...
## [2222] MCF10CA1_CTCF MCF10CA1\nCTCF 3
## [2223] MCF10CA1_CTCF MCF10CA1\nCTCF 3
## [2224] MCF10CA1_CTCF MCF10CA1\nCTCF 3
## [2225] MCF10CA1_CTCF MCF10CA1\nCTCF 3
## [2226] MCF10CA1_CTCF MCF10CA1\nCTCF 3
## -------
## seqinfo: 21 sequences from an unspecified genome; no seqlengthsPlotting and pre-clustering (identical to just calling heatmap with same parameters)
Use case : CTCF in breast cancer
This vignette runs using a small subset of datasets that are publicly available via GEO. These data are fully described in GSE98551 and the associated paper: Intranuclear and higher-order chromatin organization of the major histone gene cluster in breast cancer
In brief, the three cell lines MCF10A, MCF10A-at1, and MCF10A-CA1a
are used as a model of breast cancer progression from normal-like MCF10A
to
metastatic MCF10A-CA1a. The ChIP-seq target CTCF is both a
transcriptional regulator and an insulator frequently associated with
the boundaries of genomic structural domains. Therefore, regions where
CTCF changes are interesting for follow-up to see if they are also
associated wtih regulatory or structural changes and perhaps important
to breast cancer progression.
To download the full dataset see: narrowPeak from GEO and bigwig from GEO
We’ll use cowplot to arrange our plots in this section.
Setup
Set paths to bigwig files and narrowPeak files. There is some extra
code here to download the complete datasets using BiocFileCache
IF the package BiocFileCache is installed and
cache_path already exists. If enabled, this downloads ~3 GB
of data.
pkgdata_path = system.file("extdata",
package = "seqsetvis")
cache_path = paste0(pkgdata_path, "/.cache")
# the next line is enough to initialize the cache
# BiocFileCache(cache = cache_path)
use_full_data = dir.exists(cache_path) & require(BiocFileCache)
## Loading required package: BiocFileCache
## Loading required package: dbplyr
use_full_data = FALSE
if(use_full_data){
library(BiocFileCache)
ssv_bfc = BiocFileCache(cache = cache_path)
bw_files = vapply(seq_along(CTCF_in_10a_bigWig_urls), function(i){
rname = paste(names(CTCF_in_10a_bigWig_urls)[i],
"bigwig",
sep = ",")
fpath = CTCF_in_10a_bigWig_urls[i]
#bfcrpath calls bfcadd() if necessary and returns file path
bfcrpath(ssv_bfc, rname = rname, fpath = fpath)
}, "char")
names(bw_files) = names(CTCF_in_10a_bigWig_urls)
np_files = vapply(seq_along(CTCF_in_10a_narrowPeak_urls), function(i){
rname = paste(names(CTCF_in_10a_narrowPeak_urls)[i],
"narrowPeak",
sep = ",")
fpath = CTCF_in_10a_narrowPeak_urls[i]
#bfcrpath calls bfcadd() if necessary and returns file path
bfcrpath(ssv_bfc, rname = rname, fpath = fpath)
}, "a")
names(np_files) = names(CTCF_in_10a_narrowPeak_urls)
}else{
bw_files = vapply(c("MCF10A_CTCF", "MCF10AT1_CTCF", "MCF10CA1_CTCF"),
function(x){
system.file("extdata", paste0(x, "_FE_random100.bw"),
package = "seqsetvis")
}, "char")
# set filepaths
np_files = c(
system.file("extdata", "MCF10A_CTCF_random100.narrowPeak",
package = "seqsetvis"),
system.file("extdata", "MCF10AT1_CTCF_random100.narrowPeak",
package = "seqsetvis"),
system.file("extdata", "MCF10CA1_CTCF_random100.narrowPeak",
package = "seqsetvis")
)
names(np_files) = sub("_random100.narrowPeak", "",
x = basename(np_files))
}Loading narrowPeak files as GRanges
Perform overlapping
Peak set desciption
Here we use a barplot (A) to convey the total number of peaks in each cell line. The Venn diagram (B) shows precisely how many peaks are in each overlap set. The Euler diagram (C) lacks the numerical precision (who could say how many peaks are only present in MCF10AT1?) but the basic trends are more obvious (MCF10A has the most peaks and the majority of peaks are common to all 3 cell lines).
p_bars = ssvFeatureBars(olaps, show_counts = FALSE) +
theme(legend.position = "left")
p_bars = p_bars + theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.justification = "center") +
labs(fill = "cell line")
p_venn = ssvFeatureVenn(olaps, counts_txt_size = 4) +
guides(fill = "none", color = "none")
p_euler = ssvFeatureEuler(olaps) +
guides(fill = "none", color = "none")
cowplot::ggdraw() +
cowplot::draw_plot(p_bars, x = 0, y = 0, width = .4, height = .7) +
cowplot::draw_plot(p_venn, x = .4, y = .1, width = .3, height = .7) +
cowplot::draw_plot(p_euler, x = 0.7, y = .1, width = 0.3, height = .7) +
cowplot::draw_plot_label(c("CTCF binding in breast cancer cell lines",
"A", "B", "C"),
x = c(.04, .17, 0.4, .7),
y = c(.92, .75, .75, .75), size = 10, hjust = 0)
Plot colors can be modified via the ggplot objects without recalling plot functions. (These colors aren’t better per se.)
col_vals = c("MCF10A_CTCF" = 'red',
"MCF10AT1_CTCF" = "blue",
"MCF10CA1_CTCF" = "green")
sf = scale_fill_manual(values = col_vals)
sc = scale_color_manual(values = col_vals)
cowplot::ggdraw() +
cowplot::draw_plot(p_bars + sf,
x = 0, y = 0,
width = .4, height = .7) +
cowplot::draw_plot(p_venn + sf + sc,
x = .4, y = .1,
width = .3, height = .7) +
cowplot::draw_plot(p_euler + sf + sc,
x = 0.7, y = .1,
width = 0.3, height = .7) +
cowplot::draw_plot_label(c("CTCF binding in breast cancer cell lines",
"A", "B", "C"),
x = c(.04, .17, 0.4, .7),
y = c(.92, .75, .75, .75), size = 10, hjust = 0)Annotation with ChIPpeakAnno
By using an annotation package like ChIPpeakAnno we can assign some biology to each peak.
library(ChIPpeakAnno)
data(TSS.human.GRCh38)
macs.anno <- annotatePeakInBatch(olaps, AnnotationData=TSS.human.GRCh38)This gets us to easily selectable “gene lists” that everyone wants.
Apply a limit of 10 kb to reduce non-specific associations.
Gene ids associated with peaks present in MCF10A and MCF10AT1 but not MCF10CA1.
subset(macs.anno, MCF10AT1_CTCF & MCF10A_CTCF & !MCF10CA1_CTCF)$feature
## [1] "ENSG00000124835" "ENSG00000186676" "ENSG00000254540" "ENSG00000260910"
## [5] "ENSG00000261419" "ENSG00000211534" "ENSG00000252163"Gene ids associated with the peaks present in MCF10A and neither MCF10AT1 or MCF10CA1.
subset(macs.anno, MCF10A_CTCF & !MCF10AT1_CTCF & !MCF10CA1_CTCF)$feature
## [1] "ENSG00000143515" "ENSG00000150722" "ENSG00000237732" "ENSG00000250012"
## [5] "ENSG00000223784" "ENSG00000251226" "ENSG00000089127" "ENSG00000230157"
## [9] "ENSG00000184939" "ENSG00000261567"With a meaningfully chosen set of substantial size we could then move on to gene ontology or a gene set enrichment type of analysis.
… if we’re confident that this peak call is picking up real biological differences. Let’s take a closer look at these peak calls in the next section.
Peak call validation
Digging into a new set of peak calls can be time-consuming and arbitrary. It’s easy to spend a lot of time with a genome browser checking genes of interest or peaks at random and not come away with a clear idea how you well your peak call is performing.
seqsetvis is ideal for systematically assessing just what qualifies as a peak in each sample of an experiment.
Loading bigwig data
Before loading bigwigs, we need to decide on a uniform region size to retrieve. In this case we’ll use the 75th quantile of merged peak widths and round up to the nearest multiple of window size. This is a bit arbitrary but a good fit in practice. We’ll fully capture 75% of peak regions and not be influenced by outliers.
This is the same procedure ssvFetchBigwig() uses to
automatically set a fixed width.
window_size = 50
width_q75 = quantile(width(olaps), .75)
width_q75 = ceiling(width_q75 / window_size) * window_size
hist_res = hist(width(olaps))
lines(rep(width_q75, 2), c(0, max(hist_res$counts)), col = "red", lwd = 5)
text(width_q75, max(hist_res$counts), "fixedSize", adj = c(-.1, 1), col = "red")
Fetch profiles
Reading bigWig formatted files will not work at all on Windows OS. If you are able to read bigwigs on your system, there are few options.
Loading from the subsetted bigwigs inlcuded with seqsetvis.
This code chunk results in identical data to that stored in the
CTCF_in_10a_profiles_gr data object.
Inspecting peaks
This scatterplot makes it clear that the peaks that looked unique to MCF10A have comparable fold-enrichment in MCF10AT1. This suggests that there may not be a meaningful biological difference between peaks that are present in MCF10A but absent in MCF10AT1.
Likewise for the comparison of MCF10A and MCF10CA1. In this case, there is a systematic reduction in fold-enrichment in CA1 compared to 10A. While it’s possible there’s some biology at play, it’s far more likely that the ChIP procedure for CA1 simply wasn’t as successful.
# shortening colnames will make group names less cumbersome in plot legend
colnames(mcols(olaps_fixedSize)) = sub("_CTCF", "",
colnames(mcols(olaps_fixedSize)))
all_groups = levels(ssvFactorizeMembTable(
ssvMakeMembTable(olaps_fixedSize))$group)
all_colors = RColorBrewer::brewer.pal(length(all_groups), "Set1")
all_colors[5:7] = safeBrew(3, "Dark2")
names(all_colors) = all_groups
olap_groups_12 = ssvFactorizeMembTable(
ssvMakeMembTable(olaps_fixedSize)[, 1:2])
p_12 = ssvSignalScatterplot(bw_gr,
x_name = "MCF10A_CTCF",
y_name = "MCF10AT1_CTCF",
color_table = olap_groups_12) +
scale_color_manual(values = all_colors)
olap_groups_13 = ssvFactorizeMembTable(
ssvMakeMembTable(olaps_fixedSize)[, c(1,3)])
p_13 = ssvSignalScatterplot(bw_gr,
x_name = "MCF10A_CTCF",
y_name = "MCF10CA1_CTCF",
color_table = olap_groups_13) +
scale_color_manual(values = all_colors)
if(use_full_data){
tp_12 = p_12 + scale_size_continuous(range = .1) +
scale_alpha_continuous(range = .1) +
geom_density2d(aes(color = group), h = 40, bins = 3)
tp_13 = p_13 + scale_size_continuous(range = .1) +
scale_alpha_continuous(range = .1) +
geom_density2d(aes(color = group), h = 40, bins = 3)
cowplot::plot_grid(tp_12 + labs(title = ""),
tp_13 + labs(title = ""),
label_y = .85, labels = "AUTO")
}else{
cowplot::plot_grid(p_12 + labs(title = ""),
p_13 + labs(title = ""),
label_y = .85, labels = "AUTO")
}
These observations are reinforced by the heatmap.
bw_gr$facet_label = sub("_", "\n", bw_gr$sample)
clust_gr = ssvSignalClustering(bw_gr, nclust = 3, facet_ = "facet_label")
ssvSignalHeatmap(clust_gr, facet_ = "facet_label") + labs(fill = "FE",
y = "region",
x = "bp from center")
The heatmap can be improved by using centerAtMax() to
allow each region to be shifted such that the maximum signal is at the
center. A couple parameter notes: + view_size controls how
far each region can be shifted.
Shifting will cause overhangs which are trimmed by default so the
resulting regions are smaller. You may need to extened the origial query
GRanges set and repeat the fetch if the resulting regions are too small.
+ The by_ parameter is particularly tricky. Its default
value of “id” is probably what we want. This causes each region to be
individually shifted according to the maximum across samples. One
alternative, by_ = c("id", "sample") would have shifted
each region in each sample.
Another alternative, by_ = "" or by_ = NULL
would shift every region in every sample identically.
center_gr = centerAtMax(clust_gr, view_size = 150,
by_ = "id", check_by_dupes = FALSE)
p_center_hmap = ssvSignalHeatmap(center_gr, facet_ = "facet_label") +
labs(fill = "FE",
y = "region",
x = "bp from center")
## since center_gr still retains clustering information, clustering is not
## repeated by default, the following reclusters the data.
clust_center_gr = ssvSignalClustering(center_gr, nclust = 3)
p_center_hmap_reclust = ssvSignalHeatmap(clust_center_gr,
facet_ = "facet_label") +
labs(fill = "FE",
y = "region",
x = "bp from center")
cowplot::plot_grid(p_center_hmap + labs(title = "original clustering"),
p_center_hmap_reclust + labs(title = "reclustered"))
Cluster 1 and 2 seem to have the most robust enrichment. Let’s extract them and annotate.
clust_df = as.data.frame(mcols(clust_gr))
clust_df = unique(clust_df[,c("id", "cluster_id")])
olap_clust_annot = olap_gr
mcols(olap_clust_annot) = data.frame(id = seq_along(olap_clust_annot))
olap_clust_annot = GRanges(merge(olap_clust_annot, clust_df))
olap_clust_annot = subset(olap_clust_annot, cluster_id %in% 1:2)
olap_clust_annot <- annotatePeakInBatch(olap_clust_annot,
AnnotationData=TSS.human.GRCh38)
olap_clust_annot$feature
## [1] "ENSG00000074964" "ENSG00000179862" "ENSG00000236253" "ENSG00000235804"
## [5] "ENSG00000163207" "ENSG00000143515" "ENSG00000233473" "ENSG00000213088"
## [9] "ENSG00000238934" "ENSG00000238012" "ENSG00000224655" "ENSG00000078098"
## [13] "ENSG00000150722" "ENSG00000135925" "ENSG00000237732" "ENSG00000270540"
## [17] "ENSG00000124835" "ENSG00000231304" "ENSG00000222490" "ENSG00000250012"
## [21] "ENSG00000181804" "ENSG00000272620" "ENSG00000248810" "ENSG00000179059"
## [25] "ENSG00000272239" "ENSG00000047579" "ENSG00000164483" "ENSG00000270638"
## [29] "ENSG00000164828" "ENSG00000221157" "ENSG00000214765" "ENSG00000186676"
## [33] "ENSG00000261451" "ENSG00000254049" "ENSG00000230589" "ENSG00000236060"
## [37] "ENSG00000233933" "ENSG00000267026" "ENSG00000227482" "ENSG00000243738"
## [41] "ENSG00000223784" "ENSG00000213724" "ENSG00000130600" "ENSG00000254957"
## [45] "ENSG00000254540" "ENSG00000014138" "ENSG00000255320" "ENSG00000137474"
## [49] "ENSG00000251226" "ENSG00000130037" "ENSG00000258169" "ENSG00000255655"
## [53] "ENSG00000089127" "ENSG00000120685" "ENSG00000225801" "ENSG00000260910"
## [57] "ENSG00000100906" "ENSG00000207172" "ENSG00000258520" "ENSG00000221102"
## [61] "ENSG00000230157" "ENSG00000240874" "ENSG00000265195" "ENSG00000172460"
## [65] "ENSG00000261419" "ENSG00000199448" "ENSG00000184939" "ENSG00000261567"
## [69] "ENSG00000238575" "ENSG00000130270" "ENSG00000236474" "ENSG00000211534"
## [73] "ENSG00000201021" "ENSG00000101203" "ENSG00000252163"Use case: ChromHMM inspection
In this demonstration we’ll use the same CTCF dataset in breast cancer but bring in a chromHMM state file from an other dataset. This is meant to highlight the flexibility of seqsetvis.
Setup
If the previous CTCF in breast cancer Use Case made use of BiocFileCache, we’ll continue caching external data sources. If not, we’ll skip to a pre-cooked version of the same data.
The pre-cooked version is quite a narrow slice and there’s a significant amount of data manipulation
target_data = "MCF10A_CTCF"
chmm_win = 200 #window size is an important chromHMM parameter.
# 200 is the default window size and matches the state segementation
if(use_full_data){
# set all file paths
chmm_bw_file = bfcrpath(ssv_bfc, rnames = paste(target_data, "bigwig",
sep = ","))
chmm_np_file = bfcrpath(ssv_bfc, rnames = paste(target_data, "narrowPeak",
sep = ","))
chmm_seg_file = bfcrpath(ssv_bfc, rnames = "MCF7,segmentation",
fpath = chromHMM_demo_segmentation_url)
query_chain = bfcquery(ssv_bfc, "hg19ToHg38,chain")
if(nrow(query_chain) == 0){
chain_hg19ToHg38_gz = bfcrpath(ssv_bfc, rnames = "hg19ToHg38,gz",
fpath = chromHMM_demo_chain_url)
ch_raw = readLines(gzfile(chain_hg19ToHg38_gz))
ch_file = bfcnew(ssv_bfc, rname = "hg19ToHg38,chain")
writeLines(ch_raw, con = ch_file)
}
chmm_chain_file = bfcrpath(ssv_bfc, rnames = "hg19ToHg38,chain")
ch = rtracklayer::import.chain(chmm_chain_file)
# load segmentation data
chmm_gr = rtracklayer::import.bed(chmm_seg_file)
#cleanup state names.
chmm_gr$name = gsub("\\+", " and ", chmm_gr$name)
chmm_gr$name = gsub("_", " ", chmm_gr$name)
#setup color to state mapping
colDF = unique(mcols(chmm_gr)[c("name", "itemRgb")])
state_colors = colDF$itemRgb
names(state_colors) = colDF$name
#liftover states from hg19 to hg38
ch = rtracklayer::import.chain(chmm_chain_file)
chmm_gr_hg38 = rtracklayer::liftOver(chmm_gr, ch)
chmm_gr_hg38 = unlist(chmm_gr_hg38)
chmm_grs_list = as.list(GenomicRanges::split(chmm_gr_hg38,
chmm_gr_hg38$name))
#transform narrowPeak ranges to summit positions
chmm_np_grs = easyLoad_narrowPeak(chmm_np_file, file_names = target_data)
chmm_summit_grs = lapply(chmm_np_grs, function(x){
start(x) = start(x) + x$relSummit
end(x) = start(x)
x
})
qlist = append(chmm_summit_grs[1], chmm_grs_list)
chmm_olaps = ssvOverlapIntervalSets(qlist, use_first = TRUE)
#discard the columns for peak call and no_hit, not informative here.
mcols(chmm_olaps)[[1]] = NULL
chmm_olaps$no_hit = NULL
#total width of genome assigned each state
state_total_widths = sapply(chmm_grs_list, function(my_gr){
sum(as.numeric(width(my_gr)))
})
#Expand state regions into 200 bp windows.
state_wingrs = lapply(chmm_grs_list, function(my_gr){
st = my_gr$name[1]
wgr = unlist(slidingWindows(my_gr, chmm_win, chmm_win))
wgr$state = st
wgr
})
state_wingrs = unlist(GRangesList(state_wingrs))
# fetch bw data for each state
# it probably isn't useful to grab every single window for each state
# so we can cap the number of each state carried forward
max_per_state = 5000
# flank size zooms out a bit from each chromHMM window
flank_size = 400
state_split = split(state_wingrs, state_wingrs$state)
state_split = lapply(state_split, function(my_gr){
samp_gr = sample(my_gr, min(length(my_gr), max_per_state))
samp_gr = sort(samp_gr)
names(samp_gr) = seq_along(samp_gr)
samp_gr
})
state_gr = unlist(GRangesList(state_split))
state_gr = resize(state_gr, width = chmm_win + 2 * flank_size,
fix = "center")
bw_states_gr = ssvFetchBigwig(file_paths = chmm_bw_file,
qgr = state_gr,
win_size = 50)
bw_states_gr$grp = sub("\\..+", "", bw_states_gr$id)
bw_states_gr$grp_id = sub(".+\\.", "", bw_states_gr$id)
}else{
max_per_state = 20
flank_size = 400
state_colors = chromHMM_demo_state_colors
bw_states_gr = chromHMM_demo_bw_states_gr
chmm_olaps = chromHMM_demo_overlaps_gr
state_total_widths = chromHMM_demo_state_total_widths
}Peak overlaps with states, counts
Create barplot of raw count of peaks overlapping states.
olaps_df = as.data.frame(mcols(chmm_olaps))
colnames(olaps_df) = gsub("\\.", " ", colnames(olaps_df))
p_state_raw_count = ssvFeatureBars(olaps_df, show_counts = FALSE) +
labs(fill = "state", x = "") +
scale_fill_manual(values = state_colors) +
theme_cowplot() + guides(fill = "none") +
theme(axis.text.x = element_text(angle = 90, size = 8,
hjust = 1, vjust = .5))
p_state_raw_count
Peak overlaps with states, enrichment
Create barplot of enrichment of peaks overlapping states.
state_width_fraction = state_total_widths / sum(state_total_widths)
state_peak_hits = colSums(olaps_df)
state_peak_fraction = state_peak_hits / sum(state_peak_hits)
enrichment = state_peak_fraction /
state_width_fraction[names(state_peak_fraction)]
enrich_df = data.frame(state = names(enrichment), enrichment = enrichment)
p_state_enrichment = ggplot(enrich_df) +
geom_bar(aes(x = state, fill = state, y = enrichment), stat = "identity") +
labs(x = "") +
theme_cowplot() + guides(fill = "none") +
scale_fill_manual(values = state_colors) +
theme(axis.text.x = element_text(angle = 90, size = 8,
hjust = 1, vjust = .5))
p_state_enrichment
Aggregated line plots by state
Plot aggregated line plot by state
p_agg_tracks = ssvSignalLineplotAgg(bw_states_gr,
sample_ = "grp",
color_ = "grp")
gb = ggplot2::ggplot_build(p_agg_tracks)
yrng = range(gb$data[[1]]$y)
p_agg_tracks = p_agg_tracks +
scale_color_manual(values = state_colors) +
annotate("line", x = rep(-chmm_win/2, 2), y = yrng) +
annotate("line", x = rep(chmm_win/2, 2), y = yrng) +
labs(y = "FE", x = "bp", color = "state",
title = paste("Average FE by state,", target_data),
subtitle = paste("states sampled to",
max_per_state,
chmm_win,
"bp windows each\n",
flank_size,
"bp flanking each side")) +
theme(plot.title = element_text(hjust = 0))
p_agg_tracks
States heatmap
A heatmap using the same data from the aggregated line plot. Facetting is done on state and regions are sorted in decreasing order of signal at center.
pdt = as.data.table(mcols(bw_states_gr))
pdt$grp_id = as.integer(pdt$grp_id)
# reassign grp_id to sort within each state set
dt_list = lapply(unique(pdt$grp), function(state){
dt = pdt[grp == state]
dtmax = dt[, .(ymax = y[which(x == x[order(abs(x))][1])]), by = grp_id]
dtmax = dtmax[order(ymax, decreasing = TRUE)]
dtmax[, grp_o := seq_len(.N)]
dtmax$ymax = NULL
dt = merge(dtmax, dt)
dt[, grp_id := grp_o]
dt$grp_o = NULL
dt
})
# reassemble
pdt = rbindlist(dt_list)
# heatmap facetted by state and sorted in decreasing order
p_state_hmap = ggplot(pdt) +
geom_raster(aes(x = x, y = grp_id, fill = y)) +
scale_y_reverse() +
facet_wrap("grp", nrow = 2) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5),
strip.text = element_text(size= 8)) +
scale_fill_gradientn(colors = c("white", "orange", "red")) +
labs(y = "", fill = "FE",
x = "bp from local summit",
title = paste(max_per_state, "random regions per state"))
p_state_hmap
Figure assembly
Tie it all together into one figure with cowplot.
bar_height = .25
line_height = .3
ggdraw() +
draw_plot(p_state_raw_count + guides(fill = "none"),
x = 0,
y = 1 - bar_height,
width = .5,
height = bar_height) +
draw_plot(p_state_enrichment + guides(fill = "none"),
x = .5,
y = 1 - bar_height,
width = .5,
height = bar_height) +
draw_plot(p_agg_tracks,
x = 0,
y = 1 - bar_height - line_height,
width = 1,
height = line_height) +
draw_plot(p_state_hmap,
x = 0,
y = 0,
width = 1,
height = 1 - bar_height - line_height) +
draw_plot_label(LETTERS[1:4],
c(0, 0.48, 0, 0),
c(1, 1, 1 - bar_height, 1 - bar_height - line_height),
size = 15)