## ---- message=FALSE, warning=FALSE, echo=FALSE, fig.width=8.5, fig.height=6.5, eval=TRUE----
## 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()

