Nothing
## ---- message = FALSE---------------------------------------------------------
library(BRGenomics)
data("PROseq")
data("txs_dm6_chr4")
txs_pr <- promoters(txs_dm6_chr4, 0, 100)
## ---- collapse = TRUE---------------------------------------------------------
countmatrix_pr <- getCountsByPositions(PROseq, txs_pr, ncores = 1)
dim(countmatrix_pr)
dim(countmatrix_pr) == c(length(txs_pr), unique(width(txs_pr)))
## -----------------------------------------------------------------------------
plot(x = 1:ncol(countmatrix_pr),
y = colMeans(countmatrix_pr),
type = "l",
xlab = "Distance to TSS (bp)",
ylab = "Mean PRO-seq Reads")
## ---- fig.height=7, fig.width=6-----------------------------------------------
plot_meds <- function(sig_thresh) {
idx <- which(rowSums(countmatrix_pr) > sig_thresh)
plot(x = 1:ncol(countmatrix_pr),
y = apply(countmatrix_pr[idx, ], 2, median),
type = "l",
main = sprintf("Regions with >%s reads", sig_thresh),
xlab = "Distance to TSS (bp)",
ylab = "Median PRO-seq Reads")
}
par(mfrow = c(3, 2))
for (i in c(0, 30*2^(0:4))) {
plot_meds(i)
}
## -----------------------------------------------------------------------------
bootmeans.df <- metaSubsample(PROseq, txs_pr, binsize = 5,
lower = 0.35, upper = 0.65, ncores = 1)
head(bootmeans.df)
## -----------------------------------------------------------------------------
plot(mean ~ x, data = bootmeans.df, type = "l",
main = "PRO-seq Signal", ylim = c(0, 1.4),
xlab = "Distance from TSS",
ylab = "Mean Signal + 30% CI")
# draw a polygon to add confidence bands,
# and use adjustcolor() to add transparency
polygon(c(bootmeans.df$x, rev(bootmeans.df$x)),
c(bootmeans.df$lower, rev(bootmeans.df$upper)),
col = adjustcolor("black", 0.1), border = FALSE)
## -----------------------------------------------------------------------------
require(ggplot2)
ggplot(bootmeans.df, aes(x, mean)) +
geom_line() +
geom_ribbon(aes(x, ymin = lower, ymax = upper),
alpha = 0.1) +
labs(title = "PRO-seq Signal",
x = "Distance from TSS",
y = "Mean Signal + 30% CI") +
theme_bw()
## -----------------------------------------------------------------------------
# make 3 datasets
ps_list <- list(ps1 = PROseq[seq(1, length(PROseq), 3)],
ps2 = PROseq[seq(2, length(PROseq), 3)],
ps3 = PROseq[seq(3, length(PROseq), 3)])
## -----------------------------------------------------------------------------
bm_list.df <- metaSubsample(ps_list, txs_pr, binsize = 5,
lower = 0.35, upper = 0.65, ncores = 1)
head(bm_list.df)
## -----------------------------------------------------------------------------
require(ggplot2)
ggplot(bm_list.df, aes(x, mean, color = sample.name)) +
geom_line() +
geom_ribbon(aes(x, ymin = lower, ymax = upper,
color = NULL, fill = sample.name),
alpha = 0.2) +
labs(title = "PRO-seq Signal",
x = "Distance from TSS",
y = "Mean Signal + 30% CI") +
theme_bw()
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