## ----message=FALSE, warning=FALSE, echo=FALSE, fig.width=8.5, fig.height=6.5, eval=FALSE----
# ## Define colors
# colors <- c("#e19995", "#adaf64", "#4fbe9b", "#6eb3d9", "#d098d7")
# ## Create artificial GInteractions
# library(InteractionSet)
# set.seed(5)
# pool <- GInteractions(
# anchor1 = GRanges(seqnames = "chr1",
# ranges = IRanges(start = sample(1:990, 120, replace = TRUE),
# width = 10)),
# anchor2 = GRanges(seqnames = "chr1",
# ranges = IRanges(start = sample(1:990, 120, replace = TRUE),
# width = 10)),
# mode = "strict",
# color = sample(1:5, 120, replace = TRUE)
# )
# focal <- GInteractions(
# anchor1 = GRanges(seqnames = "chr1",
# ranges = IRanges(start = sample(1:990, 16, replace = TRUE),
# width = 10)),
# anchor2 = GRanges(seqnames = "chr1",
# ranges = IRanges(start = sample(1:990, 16, replace = TRUE),
# width = 10)),
# mode = "strict",
# color = sample(1:5, 16, replace = TRUE)
# )
# ## Add distance to metadata
# pool$distance <- pairdist(pool)
# focal$distance <- pairdist(focal)
# ## Match ranges
# library(nullranges)
# set.seed(123)
# x <- matchRanges(focal = focal,
# pool = pool,
# covar = ~color + distance,
# 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 <- plotPairs(data = pool,
# params = p,
# fill = colors[pool$color],
# x = 1, y = 1.25, width = 2.5, height = 2.25)
# annoGenomeLabel(plot = poolSet, x = 1, y = 3.55)
# plotText(label = "Pool Set",
# x = 2.25, y = 0.9,
# just = c("center", "bottom"),
# fontcolor = "#33A02C",
# fontface = "bold",
# fontfamily = 'mono')
# ## Focal set
# focalSet <- plotPairs(data = focal,
# params = p,
# fill = colors[focal$color],
# x = 5, y = 1.1, width = 2.5, height = 0.9)
# 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 <- plotPairs(data = x,
# params = p,
# fill = colors[x$color],
# x = 5, y = 2.6, width = 2.5, height = 0.9)
# annoGenomeLabel(plot = matchedSet, x = 5, y = 3.55)
# plotText(label = "Matched Set",
# x = 6.25, y = 2.50,
# 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 + distance",
# 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"))
## ----message=FALSE, warning=FALSE---------------------------------------------
library(nullrangesData)
## Load example data
binPairs <- hg19_10kb_ctcfBoundBinPairs()
binPairs
## ----message=FALSE, warning=FALSE---------------------------------------------
library(nullranges)
set.seed(123)
mgi <- matchRanges(focal = binPairs[binPairs$looped],
pool = binPairs[!binPairs$looped],
covar = ~ctcfSignal + distance + n_sites,
method = 'stratified')
mgi
## ----message=FALSE, warning=FALSE---------------------------------------------
library(plyranges)
library(ggplot2)
## Summarize ctcfSignal by n_sites
mgi %>%
regions() %>%
group_by(n_sites) %>%
summarize(ctcfSignal = mean(ctcfSignal)) %>%
as.data.frame() %>%
ggplot(aes(x = n_sites, y = ctcfSignal)) +
geom_line() +
geom_point(shape = 21, stroke = 1, fill = 'white') +
theme_minimal() +
theme(panel.border = element_rect(color = 'black',
fill = NA))
## -----------------------------------------------------------------------------
ov <- overview(mgi)
ov
## -----------------------------------------------------------------------------
ov$quality
## ----message=FALSE------------------------------------------------------------
plotPropensity(mgi, sets = c('f', 'p', 'm'))
## -----------------------------------------------------------------------------
plotPropensity(mgi, sets = c('f', 'p', 'm'), log = 'x')
## ----message=FALSE, warning=FALSE, fig.height=8, fig.width=5------------------
library(patchwork)
plots <- lapply(covariates(mgi), plotCovariate, x=mgi, sets = c('f', 'm', 'p'))
Reduce('/', plots)
## ----fig.width=6, fig.height=5------------------------------------------------
## Generate a randomly selected set from all binPairs
all <- c(focal(mgi), pool(mgi))
set.seed(123)
random <- all[sample(1:length(all), length(mgi), replace = FALSE)]
## Calculate the percent of convergent CTCF sites for each group
g1 <- (sum(focal(mgi)$convergent) / length(focal(mgi))) * 100
g2 <- (sum(pool(mgi)$convergent) / length(pool(mgi))) * 100
g3 <- (sum(random$convergent) / length(random)) * 100
g4 <- (sum(mgi$convergent) / length(mgi)) * 100
## Visualize
barplot(height = c(g1, g2, g3, g4),
names.arg = c('looped\n(focal)', 'unlooped\n(pool)',
'all pairs\n(random)', 'unlooped\n(matched)'),
col = c('#1F78B4', '#33A02C', 'orange2', '#A6CEE3'),
ylab = "Convergent CTCF Sites (%)",
main = "Testing the Convergence Rule",
border = NA,
las = 1)
## -----------------------------------------------------------------------------
sessionInfo()