In this vignette we demonstrate generating covariate-matched,
null-hypothesis GRanges using the matchRanges()
function to test for
the occupancy of CCCTC-binding factor (CTCF) at chromatin loop
anchors.
One of the fundamental principles of chromatin-looping suggests that most loops are bound at both ends by the CTCF transcription factor (TF). CTCF-bound loops can be formed by loop-extrusion, where the ring-like cohesin complex extrudes chromatin until stopped by bound CTCF. By this mechanism, we expect most loop anchors will be bound by CTCF.
While we could test this hypothesis by simple overlap or permutation
testing, these approaches fail to account for non-uniformly
distributed covariate genomic features. For example, loop anchors are
commonly bound by CTCF and located in open chromatin regions. We can
use matchRanges()
to test for CTCF occupancy at loop anchors
controlling for open chromatin regions.
Here, we generate a set of null-hypothesis GRanges to more rigorously
test CTCF occupancy at loop anchors independently from open chromatin
regions. We use the hg19_10kb_bins
dataset from the
nullrangesData
package, which contains ranges for every 10Kb bin
along the genome with CTCF, DNase, and loop feature annotations from
GM12878 (see ?nullrangesData::hg19_10kb_bins
).
## Define colors colors <- c("#e19995", "#adaf64", "#4fbe9b", "#6eb3d9", "#d098d7") ## Create artificial GRanges library(GenomicRanges) set.seed(5) pool <- GRanges(seqnames = "chr1", ranges = IRanges(start = sample(1:800, 120, replace = TRUE), width = sample(25:200, 120, replace = TRUE)), color = sample(1:5, 120, replace = TRUE)) focal <- GRanges(seqnames = "chr1", ranges = IRanges(start = sample(1:800, 16, replace = TRUE), width = sample(25:200, 16, replace = TRUE)), color = sample(1:5, 16, replace = TRUE)) ## Add width to metadata pool$length <- width(pool) focal$length <- width(focal) ## Match ranges library(nullranges) set.seed(123) x <- matchRanges(focal = focal, pool = pool, covar = ~color + length, method = 'n', replace = TRUE) ## Visualize sets library(plotgardener) library(grid) set.seed(123) pageCreate(width = 8.5, height = 6.5, showGuides = FALSE, xgrid = 0, ygrid = 0) ## Define common parameters p <- pgParams(chrom = "chr1", chromstart = 1, chromend = 1000) ## Pool set poolSet <- plotRanges(data = pool, params = p, x = 1, y = 1, width = 2.5, height = 2.5, fill = colors, colorby = colorby("color")) annoGenomeLabel(plot = poolSet, x = 1, y = 3.55) plotText(label = "Pool Set", x = 2.25, y = 0.9, just = c("center", "bottom"), fontcolor = "#33A02C", fontface = "bold", fontfamily = 'mono') ## Focal set focalSet <- plotRanges(data = focal, params = p, x = 5, y = 1, width = 2.5, height = 1, fill = colors, colorby = colorby("color")) annoGenomeLabel(plot = focalSet, x = 5, y = 2.05) plotText(label = "Focal Set", x = 6.25, y = 0.9, just = c("center", "bottom"), fontcolor = "#1F78B4", fontface = "bold", fontfamily = 'mono') ## Matched set matchedSet <- plotRanges(data = matched(x), params = p, x = 5, y = 2.5, width = 2.5, height = 1, fill = colors, colorby = colorby("color")) annoGenomeLabel(plot = matchedSet, x = 5, y = 3.55) plotText(label = "Matched Set", x = 6.25, y = 2.75, just = c("center", "bottom"), fontcolor = "#A6CEE3", fontface = "bold", fontfamily = 'mono') ## Arrow and matchRanges label plotSegments(x0 = 3.5, y0 = 3, x1 = 5, y1 = 3, arrow = arrow(type = "closed", length = unit(0.1, "inches")), fill = "black", lwd = 2) plotText(label = "matchRanges()", fontfamily = 'mono', x = 4.25, y = 2.9, just = c("center", "bottom")) ## Matching plots library(ggplot2) smallText <- theme(legend.title = element_text(size=8), legend.text=element_text(size=8), title = element_text(size=8), axis.title.x = element_text(size=8), axis.title.y = element_text(size=8)) plot1 <- plotPropensity(x, sets=c('f','m','p')) + smallText + theme(legend.key.size = unit(0.5, 'lines'), title = element_blank()) plot2 <- plotCovariate(x=x, covar=covariates(x)[1], sets=c('f','m','p')) + smallText + theme(legend.text = element_blank(), legend.position = 'none') plot3 <- plotCovariate(x=x, covar=covariates(x)[2], sets=c('f','m','p'))+ smallText + theme(legend.key.size = unit(0.5, 'lines')) ## Propensity scores plotText(label = "plotPropensity()", x = 1.10, y = 4.24, just = c("left", "bottom"), fontface = "bold", fontfamily = 'mono') plotText(label = "~color + length", x = 1.25, y = 4.5, just = c("left", "bottom"), fontsize = 10, fontfamily = "mono") plotGG(plot = plot1, x = 1, y = 4.5, width = 2.5, height = 1.5, just = c("left", "top")) ## Covariate balance plotText(label = "plotCovariate()", x = 3.75, y = 4.24, just = c("left", "bottom"), fontface = "bold", fontfamily = "mono") plotText(label = covariates(x), x = c(4, 5.9), y = 4.5, just = c("left", "bottom"), fontsize = 10, fontfamily = "mono") plotGG(plot = plot2, x = 3.50, y = 4.5, width = 1.8, height = 1.5, just = c("left", "top")) plotGG(plot = plot3, x = 5.30, y = 4.5, width = 2.75, height = 1.5, just = c("left", "top"))
matchRanges()
Before we generate our null ranges, let's take a look at our example dataset:
library(nullrangesData) ## Load example data bins <- hg19_10kb_bins() bins
matchRanges()
works by selecting a set of covariate-matched controls
from a pool of options based on an input focal set of interest. Here,
we define focal
as bins that contain a loop anchor, pool
as bins
that don't contain a loop anchor, and covar
as DNase signal and
number of DNase sites per bin:
library(nullranges) ## Match ranges set.seed(123) mgr <- matchRanges(focal = bins[bins$looped], pool = bins[!bins$looped], covar = ~dnaseSignal + n_dnase_sites) mgr
When the focal and pool arguments are GRanges
objects,
matchRanges()
returns a MatchedGRanges
object. The
MatchedGRanges
class extends GRanges
, so all of the same
operations can be applied:
library(GenomicRanges) library(plyranges) library(ggplot2) ## Summarize ctcfSignal by n_ctcf_sites mgr %>% group_by(n_ctcf_sites) %>% summarize(ctcfSignal = mean(ctcfSignal)) %>% as.data.frame() %>% ggplot(aes(x = n_ctcf_sites, y = ctcfSignal)) + geom_line() + geom_point(shape = 21, stroke = 1, fill = 'white') + theme_minimal() + theme(panel.border = element_rect(color = 'black', fill = NA))
Here, we utilize
the plyranges
package which
provides a set of "tidy" verbs for manipulating genomic ranges for a
seamless and integrated genomic analysis workflow.
We can get a quick summary of the matching quality with overview()
:
overview(mgr)
For continuous covariates (such as dnaseSignal
), overview()
shows
the mean and standard deviation between each matched set. For
categorical covariates, such as n_dnase_sites
, overview()
reports
the number of observations per category and matched set. The bottom
section shows the mean and s.d (or n, for factors) difference between
focal and matched sets.
overview()
also summarizes the propensity scores for each set to
give a quick idea of overall matching quality.
Let's visualize overall matching quality by plotting propensity scores for the focal, pool, and matched sets:
plotPropensity(mgr, sets = c('f', 'p', 'm'), type = 'ridges')
From this plot, it is clear that the matched set is much closer to the focal set than the pool set.
We can ensure that covariate distributions have been matched
appropriately by using the covariates()
function to extract matched
covariates along with patchwork
and plotCovarite
to visualize all
distributions:
library(patchwork) plots <- lapply(covariates(mgr), plotCovariate, x=mgr, sets = c('f', 'm', 'p')) Reduce('/', plots)
Using our matched ranges, we can compare CTCF occupancy in bins that
1) contain a loop anchor (i.e. looped), 2) don't contain a loop anchor
(i.e. unlooped), or 3) don't contain a loop anchor, but are also
matched for the strength and number of DNase sites (i.e. matched). In
this case, we calculate CTCF occupancy as the percent of bins that
contain CTCF among our 3 sets by using the focal()
and pool()
accessor functions.
In order to pipe the data into plyranges, we bind the ranges together and give each group a meaningful label in this scientific context (e.g. that the focal set is looped, while the background/matched sets are unlooped).
tidy_gr <- bind_ranges( looped_focal=focal(mgr), unlooped_pool=pool(mgr), unlooped_matched=mgr, .id="type" )
We define some custom colors for our barplot:
cols <- c(looped_focal="#1F78B4", unlooped_matched="#A6CEE3", unlooped_pool="#33A02C")
And finally we can make the plot, with a grouped summarization followed by some ggplot2 code:
tidy_gr %>% group_by(type) %>% summarize(CTCF_occupied = 100*mean(n_ctcf_sites >= 1)) %>% as.data.frame() %>% ggplot(aes(type, CTCF_occupied, fill=type)) + geom_col(show.legend = FALSE) + ylab("CTCF occupied bins (%)") + scale_fill_manual(values=cols) + ggtitle("CTCF occupancy")
sessionInfo()
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