Nothing
## ---- message = FALSE---------------------------------------------------------
library(BRGenomics)
data("PROseq")
data("txs_dm6_chr4")
## -----------------------------------------------------------------------------
# make 3 datasets
ps1 <- PROseq[seq(1, length(PROseq), 3)]
ps2 <- PROseq[seq(2, length(PROseq), 3)]
ps3 <- PROseq[seq(3, length(PROseq), 3)]
# use the "=" assignment in list() to give names to the list elements
ps_list <- list(ps1 = ps1, ps2 = ps2, ps3 = ps3)
names(ps_list)
ps_list
## -----------------------------------------------------------------------------
getCountsByRegions(ps_list, txs_dm6_chr4[1:5], ncores = 1)
## -----------------------------------------------------------------------------
# melt, and use the optional region_names argument
txs_counts <- getCountsByRegions(ps_list, txs_dm6_chr4, melt = TRUE,
region_names = txs_dm6_chr4$tx_name,
ncores = 1)
head(txs_counts)
## -----------------------------------------------------------------------------
library(ggplot2)
ggplot(txs_counts, aes(x = sample, y = signal, fill = sample)) +
geom_violin() +
theme_bw()
## -----------------------------------------------------------------------------
cbp_maxtx <- getCountsByPositions(ps_list, txs_dm6_chr4[135],
melt = TRUE, ncores = 1)
head(cbp_maxtx)
## ---- fig.height=4, fig.width=6-----------------------------------------------
ggplot(cbp_maxtx, aes(x = position, y = signal)) +
facet_wrap(~sample, ncol = 1, strip.position = "right") +
geom_col(size = 0.5, color = "darkgray") +
coord_cartesian(expand = FALSE) +
labs(title = txs_dm6_chr4$tx_name[135],
x = "Distance from TSS", y = "PRO-seq Signal") +
theme_classic() +
theme(strip.text.y = element_text(angle = 0),
strip.background = element_blank(),
axis.line.x = element_blank(),
axis.ticks.x = element_blank())
## -----------------------------------------------------------------------------
ps_multi <- mergeGRangesData(ps1, ps2, ps3, multiplex = TRUE, ncores = 1)
ps_multi
## -----------------------------------------------------------------------------
mcols(ps_multi)
## ---- collapse = TRUE---------------------------------------------------------
ps_multi$ps1[1:5]
## -----------------------------------------------------------------------------
# for all datasets (all fields), get counts in the first 5 transcripts
getCountsByRegions(ps_multi, txs_dm6_chr4[1:5],
field = names(mcols(ps_multi)),
ncores = 1)
# get counts for ps2 dataset only
getCountsByRegions(ps_multi, txs_dm6_chr4[1:5],
field = "ps2", ncores = 1)
# if no field is given, most functions will default to using all fields
getCountsByRegions(ps_multi, txs_dm6_chr4[1:5],
ncores = 1)
## ---- eval = FALSE------------------------------------------------------------
# # save the PRO-seq GRanges for later import
# saveRDS(PROseq, file = "~/PROseq.RData")
#
# # save a list of GRanges
# saveRDS(ps_list, file = "~/ps_list.RData")
#
# # re-import
# ps_list <- readRDS("~/ps_list.RData")
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