R/figures.R
In alb202/PACER:
heatmap_plot <- function(heatmap_data, label=NULL){
df_melted <- melt.data.table(data = as.data.table(heatmap_data),
id.vars = c("X", "Y"),
variable.name = "Position",
value.name = "Ratio",
measure.vars = c("fu3", "fu2", "fu1", "fd1", "fd2", "fd3", "tu3", "tu2", "tu1", "td1", "td2", "td3"))
df_melted$Position <- factor(x = df_melted$Position,
ordered = TRUE,
levels = c("fu3","tu3","fu2","tu2","fu1","tu1","fd1","td1","fd2","td2","fd3","td3"),
labels = c("3 Upstream of 5'","3 Upstream of 3'",
"2 Upstream of 5'","2 Upstream of 3'",
"1 Upstream of 5'","1 Upstream of 3'",
"1 Downstream of 5'","1 Downstream of 3'",
"2 Downstream of 5'","2 Downstream of 3'",
"3 Downstream of 5'","3 Downstream of 3'"))
p <- ggplot() +
geom_raster(data = df_melted,
mapping = aes(x = X, y = Y,
fill = Ratio)) +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Position") +
xlab("5' Base") +
ggtitle(label = label) +
scale_x_discrete(position = "top") +
scale_y_discrete(position = "left",
limits = ordered(x = rev(levels(as.factor(df_melted$Y))))) +
facet_wrap(~ Position, ncol = 2) +
scale_fill_gradient2(low = muted("purple"),
mid = "white",
high = muted("green"),
midpoint = 0,
space = "Lab",
limits=c(-.4,.4))
return(p)
}
phasing_plot <- function(gr, length=26, start_base=c("A|C|T|G")){
phasing_data <- calculate_phasing(gr = gr, length = length, start_base = start_base)
melted_phasing_data <- melt(phasing_data)
data <- data.frame(cbind("distance" = melted_phasing_data$value,
"strand" = unlist(lapply(strsplit(x = melted_phasing_data$L1, split = "_"), '[', 1)),
"order" = unlist(lapply(strsplit(x = melted_phasing_data$L1, split = "_"), '[', 2))),
row.names = NULL, stringsAsFactors = FALSE)
data$strand_f = factor(data$strand, levels=c('plus','minus'))
p <- ggplot() +
geom_line(data = data,
stat = "bin",
bins = 50,
mapping = aes(x = as.numeric(data$distance),
group=order,
color=order)) +
facet_grid(.~strand_f,
labeller = as_labeller(c("plus"="Plus Strand",
"minus"="Minus Strand"))) +
scale_color_manual(values = c("orange", "blue", "red")) +
scale_x_continuous(breaks = seq(0,50,10), expand = c(0,0)) +
scale_y_continuous(limits = c(0, NA), expand = c(0, 0)) +
theme(legend.title=element_blank()) +
xlab("Distance to Subsequent Position") +
ylab("Count of Positions")
return(p)
}
five_prime_plot <- function(gr, min=NULL, max=NULL){
#df <- data.frame(width=width(two_mismatches), strand=strand(two_mismatches), five=mcols(two_mismatches)$five)
print('making the new dataframe for the five prime plot')
df <- data.frame(width=width(gr),
strand=strand(gr),
five=mcols(gr)$five,
stringsAsFactors = FALSE)
print('creating the five prime plot')
p <- ggplot(data = df) +
geom_line(aes(x=df$width,
group=df$five,
color=df$five),
stat = "count") +
xlab("Length") +
ylab("Count") +
scale_colour_manual(values = c("blue","red","darkgreen","purple")) +
theme(legend.title=element_blank()) +
scale_y_continuous(labels=comma) +
facet_grid(. ~ df$strand, labeller = as_labeller(c("+"="Plus Strand", "-"="Minus Strand")))
print('returning the five prime plot')
return(p)
}
scatter_plot <- function(df, x, y){
# Melt the reads/gene data for the two widths
melted_df <- data.table::melt(df[, c("Gene_strand",
as.character(x),
as.character(y))],
id.vars=c(1,2,3))
#names(melted_df) <- c("Gene_strand", as.character(x), as.character(y))
# calculate the linear regresssion for each strand
lm_results <- list(pos=summary(lm(formula = melted_df[melted_df[,1]=="+",][,2]~melted_df[melted_df[,1]=="+",][,3], na.action = na.omit)),
neg=summary(lm(formula = melted_df[melted_df[,1]=="-",][,2]~melted_df[melted_df[,1]=="-",][,3], na.action = na.omit)))
# Create the strand variables as factors
vars <- data.frame("Gene_strand"=c("+","-"), stringsAsFactors = TRUE)
# Make the main plot faceted with Rsquared annotation
p <- ggplot(data = melted_df,
aes(x=melted_df[as.character(x)],
y=melted_df[as.character(y)])) +
geom_point(inherit.aes = TRUE, size=.5) +
xlab(paste("log(10) of ", x, " reads / bp", sep = "")) +
ylab(paste("log(10) of ", y, " reads / bp", sep = "")) +
scale_x_log10(labels=comma) +
scale_y_log10(labels=comma) +
facet_grid(. ~ Gene_strand,
scales = "fixed",
space = "fixed",
labeller = as_labeller(c("+"="Plus Strand", "-"="Minus Strand", names(df)[2:length(names(df))]))) +
geom_smooth(method="lm", se=FALSE, inherit.aes = TRUE, size=.5)
# Find location of annotation at .2x by .8y
x_anno <- 10**layer_scales(p)$x$range$range
y_anno <- 10**layer_scales(p)$y$range$range
x_anno <- (x_anno[2] - x_anno[1]) * .001 + x_anno[1]
y_anno <- (y_anno[2] - y_anno[1]) * .7 + y_anno[1]
# Generate the annotation data frame
r2_df <- data.frame(x=c(x_anno, x_anno),
y=c(y_anno, y_anno),
vars,
"labels"=as.factor(c(paste("R-squared: ", as.character(round(lm_results$pos$r.squared, 3)), sep = ""),
paste("R-squared: ", as.character(round(lm_results$neg$r.squared, 3)), sep = ""))),
stringsAsFactors = TRUE)
p <- p +
geom_text(aes(x=x,
y=y,
label = labels,
group=NULL),
data = r2_df)
return(p)
}
#
# sequence_logo_comparison <- function(gr, method="bits", flanks=0){
# colors_scheme = make_col_scheme(chars=c('A', 'C', 'G', 'T'),
# groups=c('A', 'C', 'G', 'T'),
# cols=c('blue', 'red', 'green', 'purple'))
#
# p <- ggplot() +
# geom_logo(gr, col_scheme=colors_scheme, method = method) +
# #theme_logo() +
# theme(panel.grid.minor.x = element_blank()) +
# #coord_cartesian(ylim = c(0, 2)) +
# #coord_cartesian(ylim = c(0, 2), xlim = c(0-flanks, 22+flanks)) +
# #scale_x_discrete(breaks = seq(from = 0,to = 22, by = 5)) +
# #scale_y_discrete(breaks = seq(0, 2, by = .5)) +
# facet_wrap(.~seq_group) #+
# scale_y_continuous(limits = c(0, 2)) +
# scale_x_continuous(limits = c(0, 24), breaks = pretty_base1(1,2), labels = seq(1,22, by=5))
# return(p)
#
#
#
# #p <- ggplot() + geom_logo(sequences, col_scheme=colors_scheme, method = "bits") + theme(panel.grid.minor.x = element_blank()) + facet_grid(.~seq_group) + scale_y_continuous(limits = c(0, 2)) + scale_x_continuous(limits = c(0, 24), breaks = pretty_base1(1,22))
# #q <- ggplot() + geom_logo(sequences2, col_scheme=colors_scheme, method = "bits") + theme(panel.grid.minor.x = element_blank()) + facet_grid(.~seq_group) + scale_y_continuous(limits = c(0, 2)) + scale_x_continuous(limits = c(0, 42), breaks = pretty_base1(1,45)+1, labels = pretty_base1(-9,35))
#
# sequences <- list("plus_seq"=as.character(mcols(gr[width(gr)==22 & strand(gr)=="+"])$seq),
# "minus_seq"=as.character(mcols(gr[width(gr)==22 & strand(gr)=="-"])$seq))
# sequences2 <- list("plus_int"=as.character(mcols(gr[width(gr)==22 & strand(gr)=="+"])$with_flanks),
# "minus_int"=as.character(mcols(gr[width(gr)==22 & strand(gr)=="-"])$with_flanks))
# sequences3 <- c(sequences, sequences2)
# }
seq_logo_comparisons <- function(gr, shuffled_gr, length, five_prime_base=NULL){
# Set the color scheme for each base
colors_scheme = make_col_scheme(chars=c('A', 'C', 'G', 'T'),
groups=c('A', 'C', 'G', 'T'),
cols=c('blue', 'red', 'green', 'purple'))
# If the reads should be limited to a 5' base, filter the alignments here
if(!is.null(five_prime_base)){
gr <- c(gr[strand(gr)=="+" & mcols(gr)$five == five_prime_base],
gr[strand(gr)=="-" & mcols(gr)$five == five_prime_base])
shuffled_gr <- c(shuffled_gr[strand(shuffled_gr)=="+" & mcols(shuffled_gr)$five == five_prime_base],
shuffled_gr[strand(shuffled_gr)=="-" & mcols(shuffled_gr)$five == five_prime_base])
}
### Process the RNA sequences
# Get the alignments
interval_plus_data <- as.character(mcols(gr[width(gr)==length & strand(gr)=="+"])$seq)
interval_minus_data <- as.character(mcols(gr[width(gr)==length & strand(gr)=="-"])$seq)
# Run ggplot with geom_logo for the + strand
interval_plus <- ggplot() +
geom_logo(interval_plus_data,
col_scheme=colors_scheme,
method = "bits") +
theme(panel.grid.minor.x = element_blank(),
plot.title = element_text(lineheight=.5, hjust = 0.5)) +
ggtitle(label = "+ Strand") +
annotate("text", x = -5, y = 1.8,
label = paste("N = ", length(interval_plus_data), sep = "")) +
geom_rect(mapping = aes(xmin=0.5, xmax=length+0.5, ymin=0, ymax=2), fill="yellow", alpha=0.1, inherit.aes = FALSE) +
scale_y_continuous(limits = c(0, 2),
breaks = c(0,1,2)) +
scale_x_continuous(limits = c(0-10, length+1+10),
breaks = pretty_base1(0,length))
# Run ggplot with geom_logo for the - strand
interval_minus <- ggplot() +
geom_logo(interval_minus_data,
col_scheme=colors_scheme,
method = "bits") +
theme(panel.grid.minor.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_text(family = "Sans",
size = 12),
plot.title = element_text(lineheight=.5,
hjust = 0.5,
family = "Sans",
size = 12)) +
ylab("RNA Sequence") +
ggtitle(label = "- Strand") +
annotate("text", x = -5, y = 1.8,
label = paste("N = ", length(interval_minus_data), sep = "")) +
geom_rect(mapping = aes(xmin=0.5, xmax=length+0.5, ymin=0, ymax=2), fill="yellow", alpha=0.1, inherit.aes = FALSE) +
scale_y_continuous(limits = c(0, 2),
breaks = c(0,1,2),
position = "right",
labels = NULL) +
scale_x_continuous(limits = c(0-10, length+1+10),
breaks = pretty_base1(0,length))
# Build the plots
ip <- ggplot_gtable(ggplot_build(interval_plus))
im <- ggplot_gtable(ggplot_build(interval_minus))
### Process the DNA sequences with the flanks
# Get the alignments
flanks_plus_data <- as.character(mcols(gr[width(gr)==length & strand(gr)=="+"])$with_flanks)
flanks_minus_data <- as.character(mcols(gr[width(gr)==length & strand(gr)=="-"])$with_flanks)
# Run ggplot with geom_logo for the + strand
flanks_plus <- ggplot() +
geom_logo(flanks_plus_data,
col_scheme=colors_scheme,
method = "bits") +
theme(panel.grid.minor.x = element_blank()) +
geom_rect(mapping = aes(xmin=10.5, xmax=length+10.5, ymin=0, ymax=2), fill="yellow", alpha=0.1, inherit.aes = FALSE) +
annotate("text", x = 5, y = 1.8,
label = paste("N = ", length(flanks_plus_data), sep = "")) +
scale_y_continuous(limits = c(0, 2),
breaks = c(0,1,2)) +
scale_x_continuous(limits = c(0, length+20+1),
breaks = pretty_base1(0,length+20+1, c(0,10), c(1,11)),
labels = pretty_base1(0-10,length+20+1-10, c(-9, 0), c(-10, 1)))
# Run ggplot with geom_logo for the - strand
flanks_minus <- ggplot() +
geom_logo(flanks_minus_data,
col_scheme=colors_scheme,
method = "bits") +
theme(panel.grid.minor.x = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.y = element_text(family = "Sans",
size = 12)) +
ylab("DNA Sequence") +
annotate("text", x = 5, y = 1.8,
label = paste("N = ", length(flanks_minus_data), sep = "")) +
geom_rect(mapping = aes(xmin=10.5, xmax=length+10.5, ymin=0, ymax=2), fill="yellow", alpha=0.1, inherit.aes = FALSE) +
scale_y_continuous(limits = c(0, 2),
breaks = c(0,1,2),
position = "right",
labels = NULL) +
scale_x_continuous(limits = c(0, length+20+1),
breaks = pretty_base1(0,length+20+1, c(0,10), c(1,11)),
labels = pretty_base1(0-10,length+20+1-10, c(-9, 0), c(-10, 1)))
# Build the plots
fp <- ggplot_gtable(ggplot_build(flanks_plus))
fm <- ggplot_gtable(ggplot_build(flanks_minus))
### Process the shuffled DNA sequences with flanks
# Get the alignments
shuffled_flanks_plus_data <- as.character(mcols(shuffled_gr[width(shuffled_gr)==length & strand(shuffled_gr)=="+"])$with_flanks)
shuffled_flanks_minus_data <- as.character(mcols(shuffled_gr[width(shuffled_gr)==length & strand(shuffled_gr)=="-"])$with_flanks)
# Run ggplot with geom_logo for the + strand
shuffled_flanks_plus <- ggplot() +
geom_logo(shuffled_flanks_plus_data,
col_scheme=colors_scheme,
method = "bits") +
theme(panel.grid.minor.x = element_blank()) +
geom_rect(mapping = aes(xmin=10.5, xmax=length+10.5, ymin=0, ymax=2), fill="yellow", alpha=0.1, inherit.aes = FALSE) +
annotate("text", x = 5, y = 1.8,
label = paste("N = ", length(shuffled_flanks_plus_data), sep = "")) +
scale_y_continuous(limits = c(0, 2),
breaks = c(0,1,2)) +
scale_x_continuous(limits = c(0, length+20+1),
breaks = pretty_base1(0,length+20+1, c(0,10), c(1,11)),
labels = pretty_base1(0-10,length+20+1-10, c(-9, 0), c(-10, 1)))
# Run ggplot with geom_logo for the - strand
shuffled_flanks_minus <- ggplot() +
geom_logo(shuffled_flanks_minus_data,
col_scheme=colors_scheme,
method = "bits") +
theme(panel.grid.minor.x = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.y = element_text(family = "Sans",
size = 12)) +
ylab("Shuffled DNA Sequence") +
annotate("text", x = 5, y = 1.8,
label = paste("N = ", length(shuffled_flanks_minus_data), sep = "")) +
geom_rect(mapping = aes(xmin=10.5, xmax=length+10.5, ymin=0, ymax=2), fill="yellow", alpha=0.1, inherit.aes = FALSE) +
scale_y_continuous(limits = c(0, 2),
breaks = c(0,1,2),
position = "right",
labels = NULL) +
scale_x_continuous(limits = c(0, length+20+1),
breaks = pretty_base1(0,length+20+1, c(0,10), c(1,11)),
labels = pretty_base1(0-10,length+20+1-10, c(-9, 0), c(-10, 1)))
# Build the plots
sfp <- ggplot_gtable(ggplot_build(shuffled_flanks_plus))
sfm <- ggplot_gtable(ggplot_build(shuffled_flanks_minus))
# Arrange the plots with the labels
arranged_grobs <- list(textGrob(label = paste(as.character(length),
as.character(five_prime_base),
" Reads",
sep = ""),
gp=gpar(fontfamily = "Sans", size=24), vjust = 0),
arrangeGrob(ip, im, ncol = 2),
arrangeGrob(fp, fm, ncol = 2),
arrangeGrob(sfp, sfm, ncol = 2),
textGrob(label = "Position along RNA or Genome (1 is 5' end)",
gp=gpar(fontfamily = "Sans", size=12), vjust = 0))
# Arrange all the plots
full <- grid.arrange(grobs = arranged_grobs,
nrow = 5, ncol = 1, heights = c(.15,1,1,1,.07))
return(full)
}
offset_plot <- function(gr, overlap_type=c('sense', 'antisense'), primary_length, maximum_offset=10){
offset_data <- calculate_offsets(gr = gr,
overlap_type = overlap_type,
primary_length = primary_length,
maximum_offset = maximum_offset)
## Make the plot for the sense strand offsets
p <- ggplot() +
geom_line(data = offset_data,
mapping = aes(x = as.numeric(offset_data$offsets),
y = as.numeric(offset_data$ratio),
group=factor(widths),
color=factor(widths), linetype=factor(widths)
)) +
#geom_line(aes(linetype=, color=offset_data$widths)) +
facet_grid(.~strands,
labeller = as_labeller(c("+"="Plus Strand",
"-"="Minus Strand"))) +
# scale_color_distiller(palette = "Spectral") +
# scale_color_discrete() +
theme(legend.title=element_blank()) +
xlab("Offset from 5' end") +
ylab("Percentage of each length")
#
# ## Make the plot for the sense strand offsets
# q <- ggplot() +
# geom_line(data = antisense_offsets,
# mapping = aes(x = as.numeric(antisense_offsets$offsets),
# y = as.numeric(antisense_offsets$ratio),
# group=widths,
# color=widths)) +
# facet_grid(.~strands,
# labeller = as_labeller(c("+"="Plus Strand",
# "-"="Minus Strand"))) +
# scale_color_distiller(palette = "Spectral") +
# theme(legend.title=element_blank(),
# axis.title.x=element_blank()) +
# #xlab(NA) +
# ylab("Opposite Strand")
# same_strand <- ggplot_gtable(ggplot_build(p))
# opposite_strand <- ggplot_gtable(ggplot_build(q))
# Arrange the plots with the labels
# arranged_grobs <- list(textGrob(label = paste(as.character(length),
# as.character(five_prime_base),
# " Reads",
# sep = ""),
# gp=gpar(fontfamily = "Sans", size=24), vjust = 0),
# arrangeGrob(ip, im, ncol = 2),
# arrangeGrob(fp, fm, ncol = 2),
# arrangeGrob(sfp, sfm, ncol = 2),
# textGrob(label = "Position along RNA or Genome (1 is 5' end)",
# gp=gpar(fontfamily = "Sans", size=12), vjust = 0))
# arranged_grobs <- list(textGrob(label = paste("Offset from ",
# as.character(primary_length),
# #as.character(five_prime_base),
# "nt Reads",
# sep = ""),
# gp=gpar(fontfamily = "Sans", size=24), vjust = 0),
# arrangeGrob(same_strand),
# arrangeGrob(opposite_strand),
# textGrob(label = "Offset from 5' end",
# gp=gpar(fontfamily = "Sans",
# size=12),
# vjust = 0)
# )
# # Arrange all the plots
# full <- grid.arrange(grobs = arranged_grobs
# ,nrow = 4
# ,ncol = 1
# ,heights = c(.12,1,1,.09)
# )
return(p)
}
## Make plots and save
save_plot <- function(p, path, label){
print('ggsaving the plot')
triedOut <- try({
ggsave(filename = paste(label, ".svg", sep = ""),
plot = p,
device = "svg",
path = path)
})
#return(p)
}
# full <- grid.arrange(ip, im, fp, fm, nrow = 2, ncol = 2)
# gtable::gtable_show_layout(full)
# grid.draw(full)
# maxWidth <- unit.pmax(pgt$widths[2:3], qgt$widths[2:3])
# pgt$widths[2:3] <- maxWidth
# qgt$widths[2:3] <- maxWidth
# #grid.arrange(pgt, qgt, heights = c(7, 3), nrow = 2, ncol = 2)
# gtable::gtable_show_layout(arrangeGrob(pgt, qgt,
# heights = c(5, 5), #,
# widths = c(6, 6)))
# nrow = 2,
# ncol = 2))
# gtable::gtable_show_layout(arrangeGrob(pgt, qgt))
# q + annotate(geom = "text",
# x = c(11),
# y = c(),
# label = c("5'"),
# color=c("black"),
# size=c(5)) +
# annotate(geom = "text",
# x = c(33),
# y = c(2),
# label = c("3'"),
# color=c("black"),
# size=c(5))
# vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y)
# grid.newpage()
# pushViewport(viewport(layout = grid.layout(2, 100)))
# # print(interval_plus, vp = vplayout(1, (20-5):42))
# # print(interval_minus, vp = vplayout(1, 62:(62+22)))
# print(interval_plus, vp = vplayout(1, (10-5):52))
# print(interval_minus, vp = vplayout(1, 52:94))
# print(flanks_plus, vp = vplayout(2, (10-5):52))
# print(flanks_minus, vp = vplayout(2, 52:94))
#
#
# }
# }
alb202/PACER documentation built on May 16, 2019, 10:05 a.m.
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heatmap_plot <- function(heatmap_data, label=NULL){
df_melted <- melt.data.table(data = as.data.table(heatmap_data),
id.vars = c("X", "Y"),
variable.name = "Position",
value.name = "Ratio",
measure.vars = c("fu3", "fu2", "fu1", "fd1", "fd2", "fd3", "tu3", "tu2", "tu1", "td1", "td2", "td3"))
df_melted$Position <- factor(x = df_melted$Position,
ordered = TRUE,
levels = c("fu3","tu3","fu2","tu2","fu1","tu1","fd1","td1","fd2","td2","fd3","td3"),
labels = c("3 Upstream of 5'","3 Upstream of 3'",
"2 Upstream of 5'","2 Upstream of 3'",
"1 Upstream of 5'","1 Upstream of 3'",
"1 Downstream of 5'","1 Downstream of 3'",
"2 Downstream of 5'","2 Downstream of 3'",
"3 Downstream of 5'","3 Downstream of 3'"))
p <- ggplot() +
geom_raster(data = df_melted,
mapping = aes(x = X, y = Y,
fill = Ratio)) +
theme(plot.title = element_text(hjust = 0.5)) +
ylab("Position") +
xlab("5' Base") +
ggtitle(label = label) +
scale_x_discrete(position = "top") +
scale_y_discrete(position = "left",
limits = ordered(x = rev(levels(as.factor(df_melted$Y))))) +
facet_wrap(~ Position, ncol = 2) +
scale_fill_gradient2(low = muted("purple"),
mid = "white",
high = muted("green"),
midpoint = 0,
space = "Lab",
limits=c(-.4,.4))
return(p)
}
phasing_plot <- function(gr, length=26, start_base=c("A|C|T|G")){
phasing_data <- calculate_phasing(gr = gr, length = length, start_base = start_base)
melted_phasing_data <- melt(phasing_data)
data <- data.frame(cbind("distance" = melted_phasing_data$value,
"strand" = unlist(lapply(strsplit(x = melted_phasing_data$L1, split = "_"), '[', 1)),
"order" = unlist(lapply(strsplit(x = melted_phasing_data$L1, split = "_"), '[', 2))),
row.names = NULL, stringsAsFactors = FALSE)
data$strand_f = factor(data$strand, levels=c('plus','minus'))
p <- ggplot() +
geom_line(data = data,
stat = "bin",
bins = 50,
mapping = aes(x = as.numeric(data$distance),
group=order,
color=order)) +
facet_grid(.~strand_f,
labeller = as_labeller(c("plus"="Plus Strand",
"minus"="Minus Strand"))) +
scale_color_manual(values = c("orange", "blue", "red")) +
scale_x_continuous(breaks = seq(0,50,10), expand = c(0,0)) +
scale_y_continuous(limits = c(0, NA), expand = c(0, 0)) +
theme(legend.title=element_blank()) +
xlab("Distance to Subsequent Position") +
ylab("Count of Positions")
return(p)
}
five_prime_plot <- function(gr, min=NULL, max=NULL){
#df <- data.frame(width=width(two_mismatches), strand=strand(two_mismatches), five=mcols(two_mismatches)$five)
print('making the new dataframe for the five prime plot')
df <- data.frame(width=width(gr),
strand=strand(gr),
five=mcols(gr)$five,
stringsAsFactors = FALSE)
print('creating the five prime plot')
p <- ggplot(data = df) +
geom_line(aes(x=df$width,
group=df$five,
color=df$five),
stat = "count") +
xlab("Length") +
ylab("Count") +
scale_colour_manual(values = c("blue","red","darkgreen","purple")) +
theme(legend.title=element_blank()) +
scale_y_continuous(labels=comma) +
facet_grid(. ~ df$strand, labeller = as_labeller(c("+"="Plus Strand", "-"="Minus Strand")))
print('returning the five prime plot')
return(p)
}
scatter_plot <- function(df, x, y){
# Melt the reads/gene data for the two widths
melted_df <- data.table::melt(df[, c("Gene_strand",
as.character(x),
as.character(y))],
id.vars=c(1,2,3))
#names(melted_df) <- c("Gene_strand", as.character(x), as.character(y))
# calculate the linear regresssion for each strand
lm_results <- list(pos=summary(lm(formula = melted_df[melted_df[,1]=="+",][,2]~melted_df[melted_df[,1]=="+",][,3], na.action = na.omit)),
neg=summary(lm(formula = melted_df[melted_df[,1]=="-",][,2]~melted_df[melted_df[,1]=="-",][,3], na.action = na.omit)))
# Create the strand variables as factors
vars <- data.frame("Gene_strand"=c("+","-"), stringsAsFactors = TRUE)
# Make the main plot faceted with Rsquared annotation
p <- ggplot(data = melted_df,
aes(x=melted_df[as.character(x)],
y=melted_df[as.character(y)])) +
geom_point(inherit.aes = TRUE, size=.5) +
xlab(paste("log(10) of ", x, " reads / bp", sep = "")) +
ylab(paste("log(10) of ", y, " reads / bp", sep = "")) +
scale_x_log10(labels=comma) +
scale_y_log10(labels=comma) +
facet_grid(. ~ Gene_strand,
scales = "fixed",
space = "fixed",
labeller = as_labeller(c("+"="Plus Strand", "-"="Minus Strand", names(df)[2:length(names(df))]))) +
geom_smooth(method="lm", se=FALSE, inherit.aes = TRUE, size=.5)
# Find location of annotation at .2x by .8y
x_anno <- 10**layer_scales(p)$x$range$range
y_anno <- 10**layer_scales(p)$y$range$range
x_anno <- (x_anno[2] - x_anno[1]) * .001 + x_anno[1]
y_anno <- (y_anno[2] - y_anno[1]) * .7 + y_anno[1]
# Generate the annotation data frame
r2_df <- data.frame(x=c(x_anno, x_anno),
y=c(y_anno, y_anno),
vars,
"labels"=as.factor(c(paste("R-squared: ", as.character(round(lm_results$pos$r.squared, 3)), sep = ""),
paste("R-squared: ", as.character(round(lm_results$neg$r.squared, 3)), sep = ""))),
stringsAsFactors = TRUE)
p <- p +
geom_text(aes(x=x,
y=y,
label = labels,
group=NULL),
data = r2_df)
return(p)
}
#
# sequence_logo_comparison <- function(gr, method="bits", flanks=0){
# colors_scheme = make_col_scheme(chars=c('A', 'C', 'G', 'T'),
# groups=c('A', 'C', 'G', 'T'),
# cols=c('blue', 'red', 'green', 'purple'))
#
# p <- ggplot() +
# geom_logo(gr, col_scheme=colors_scheme, method = method) +
# #theme_logo() +
# theme(panel.grid.minor.x = element_blank()) +
# #coord_cartesian(ylim = c(0, 2)) +
# #coord_cartesian(ylim = c(0, 2), xlim = c(0-flanks, 22+flanks)) +
# #scale_x_discrete(breaks = seq(from = 0,to = 22, by = 5)) +
# #scale_y_discrete(breaks = seq(0, 2, by = .5)) +
# facet_wrap(.~seq_group) #+
# scale_y_continuous(limits = c(0, 2)) +
# scale_x_continuous(limits = c(0, 24), breaks = pretty_base1(1,2), labels = seq(1,22, by=5))
# return(p)
#
#
#
# #p <- ggplot() + geom_logo(sequences, col_scheme=colors_scheme, method = "bits") + theme(panel.grid.minor.x = element_blank()) + facet_grid(.~seq_group) + scale_y_continuous(limits = c(0, 2)) + scale_x_continuous(limits = c(0, 24), breaks = pretty_base1(1,22))
# #q <- ggplot() + geom_logo(sequences2, col_scheme=colors_scheme, method = "bits") + theme(panel.grid.minor.x = element_blank()) + facet_grid(.~seq_group) + scale_y_continuous(limits = c(0, 2)) + scale_x_continuous(limits = c(0, 42), breaks = pretty_base1(1,45)+1, labels = pretty_base1(-9,35))
#
# sequences <- list("plus_seq"=as.character(mcols(gr[width(gr)==22 & strand(gr)=="+"])$seq),
# "minus_seq"=as.character(mcols(gr[width(gr)==22 & strand(gr)=="-"])$seq))
# sequences2 <- list("plus_int"=as.character(mcols(gr[width(gr)==22 & strand(gr)=="+"])$with_flanks),
# "minus_int"=as.character(mcols(gr[width(gr)==22 & strand(gr)=="-"])$with_flanks))
# sequences3 <- c(sequences, sequences2)
# }
seq_logo_comparisons <- function(gr, shuffled_gr, length, five_prime_base=NULL){
# Set the color scheme for each base
colors_scheme = make_col_scheme(chars=c('A', 'C', 'G', 'T'),
groups=c('A', 'C', 'G', 'T'),
cols=c('blue', 'red', 'green', 'purple'))
# If the reads should be limited to a 5' base, filter the alignments here
if(!is.null(five_prime_base)){
gr <- c(gr[strand(gr)=="+" & mcols(gr)$five == five_prime_base],
gr[strand(gr)=="-" & mcols(gr)$five == five_prime_base])
shuffled_gr <- c(shuffled_gr[strand(shuffled_gr)=="+" & mcols(shuffled_gr)$five == five_prime_base],
shuffled_gr[strand(shuffled_gr)=="-" & mcols(shuffled_gr)$five == five_prime_base])
}
### Process the RNA sequences
# Get the alignments
interval_plus_data <- as.character(mcols(gr[width(gr)==length & strand(gr)=="+"])$seq)
interval_minus_data <- as.character(mcols(gr[width(gr)==length & strand(gr)=="-"])$seq)
# Run ggplot with geom_logo for the + strand
interval_plus <- ggplot() +
geom_logo(interval_plus_data,
col_scheme=colors_scheme,
method = "bits") +
theme(panel.grid.minor.x = element_blank(),
plot.title = element_text(lineheight=.5, hjust = 0.5)) +
ggtitle(label = "+ Strand") +
annotate("text", x = -5, y = 1.8,
label = paste("N = ", length(interval_plus_data), sep = "")) +
geom_rect(mapping = aes(xmin=0.5, xmax=length+0.5, ymin=0, ymax=2), fill="yellow", alpha=0.1, inherit.aes = FALSE) +
scale_y_continuous(limits = c(0, 2),
breaks = c(0,1,2)) +
scale_x_continuous(limits = c(0-10, length+1+10),
breaks = pretty_base1(0,length))
# Run ggplot with geom_logo for the - strand
interval_minus <- ggplot() +
geom_logo(interval_minus_data,
col_scheme=colors_scheme,
method = "bits") +
theme(panel.grid.minor.x = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_text(family = "Sans",
size = 12),
plot.title = element_text(lineheight=.5,
hjust = 0.5,
family = "Sans",
size = 12)) +
ylab("RNA Sequence") +
ggtitle(label = "- Strand") +
annotate("text", x = -5, y = 1.8,
label = paste("N = ", length(interval_minus_data), sep = "")) +
geom_rect(mapping = aes(xmin=0.5, xmax=length+0.5, ymin=0, ymax=2), fill="yellow", alpha=0.1, inherit.aes = FALSE) +
scale_y_continuous(limits = c(0, 2),
breaks = c(0,1,2),
position = "right",
labels = NULL) +
scale_x_continuous(limits = c(0-10, length+1+10),
breaks = pretty_base1(0,length))
# Build the plots
ip <- ggplot_gtable(ggplot_build(interval_plus))
im <- ggplot_gtable(ggplot_build(interval_minus))
### Process the DNA sequences with the flanks
# Get the alignments
flanks_plus_data <- as.character(mcols(gr[width(gr)==length & strand(gr)=="+"])$with_flanks)
flanks_minus_data <- as.character(mcols(gr[width(gr)==length & strand(gr)=="-"])$with_flanks)
# Run ggplot with geom_logo for the + strand
flanks_plus <- ggplot() +
geom_logo(flanks_plus_data,
col_scheme=colors_scheme,
method = "bits") +
theme(panel.grid.minor.x = element_blank()) +
geom_rect(mapping = aes(xmin=10.5, xmax=length+10.5, ymin=0, ymax=2), fill="yellow", alpha=0.1, inherit.aes = FALSE) +
annotate("text", x = 5, y = 1.8,
label = paste("N = ", length(flanks_plus_data), sep = "")) +
scale_y_continuous(limits = c(0, 2),
breaks = c(0,1,2)) +
scale_x_continuous(limits = c(0, length+20+1),
breaks = pretty_base1(0,length+20+1, c(0,10), c(1,11)),
labels = pretty_base1(0-10,length+20+1-10, c(-9, 0), c(-10, 1)))
# Run ggplot with geom_logo for the - strand
flanks_minus <- ggplot() +
geom_logo(flanks_minus_data,
col_scheme=colors_scheme,
method = "bits") +
theme(panel.grid.minor.x = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.y = element_text(family = "Sans",
size = 12)) +
ylab("DNA Sequence") +
annotate("text", x = 5, y = 1.8,
label = paste("N = ", length(flanks_minus_data), sep = "")) +
geom_rect(mapping = aes(xmin=10.5, xmax=length+10.5, ymin=0, ymax=2), fill="yellow", alpha=0.1, inherit.aes = FALSE) +
scale_y_continuous(limits = c(0, 2),
breaks = c(0,1,2),
position = "right",
labels = NULL) +
scale_x_continuous(limits = c(0, length+20+1),
breaks = pretty_base1(0,length+20+1, c(0,10), c(1,11)),
labels = pretty_base1(0-10,length+20+1-10, c(-9, 0), c(-10, 1)))
# Build the plots
fp <- ggplot_gtable(ggplot_build(flanks_plus))
fm <- ggplot_gtable(ggplot_build(flanks_minus))
### Process the shuffled DNA sequences with flanks
# Get the alignments
shuffled_flanks_plus_data <- as.character(mcols(shuffled_gr[width(shuffled_gr)==length & strand(shuffled_gr)=="+"])$with_flanks)
shuffled_flanks_minus_data <- as.character(mcols(shuffled_gr[width(shuffled_gr)==length & strand(shuffled_gr)=="-"])$with_flanks)
# Run ggplot with geom_logo for the + strand
shuffled_flanks_plus <- ggplot() +
geom_logo(shuffled_flanks_plus_data,
col_scheme=colors_scheme,
method = "bits") +
theme(panel.grid.minor.x = element_blank()) +
geom_rect(mapping = aes(xmin=10.5, xmax=length+10.5, ymin=0, ymax=2), fill="yellow", alpha=0.1, inherit.aes = FALSE) +
annotate("text", x = 5, y = 1.8,
label = paste("N = ", length(shuffled_flanks_plus_data), sep = "")) +
scale_y_continuous(limits = c(0, 2),
breaks = c(0,1,2)) +
scale_x_continuous(limits = c(0, length+20+1),
breaks = pretty_base1(0,length+20+1, c(0,10), c(1,11)),
labels = pretty_base1(0-10,length+20+1-10, c(-9, 0), c(-10, 1)))
# Run ggplot with geom_logo for the - strand
shuffled_flanks_minus <- ggplot() +
geom_logo(shuffled_flanks_minus_data,
col_scheme=colors_scheme,
method = "bits") +
theme(panel.grid.minor.x = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.title.y = element_text(family = "Sans",
size = 12)) +
ylab("Shuffled DNA Sequence") +
annotate("text", x = 5, y = 1.8,
label = paste("N = ", length(shuffled_flanks_minus_data), sep = "")) +
geom_rect(mapping = aes(xmin=10.5, xmax=length+10.5, ymin=0, ymax=2), fill="yellow", alpha=0.1, inherit.aes = FALSE) +
scale_y_continuous(limits = c(0, 2),
breaks = c(0,1,2),
position = "right",
labels = NULL) +
scale_x_continuous(limits = c(0, length+20+1),
breaks = pretty_base1(0,length+20+1, c(0,10), c(1,11)),
labels = pretty_base1(0-10,length+20+1-10, c(-9, 0), c(-10, 1)))
# Build the plots
sfp <- ggplot_gtable(ggplot_build(shuffled_flanks_plus))
sfm <- ggplot_gtable(ggplot_build(shuffled_flanks_minus))
# Arrange the plots with the labels
arranged_grobs <- list(textGrob(label = paste(as.character(length),
as.character(five_prime_base),
" Reads",
sep = ""),
gp=gpar(fontfamily = "Sans", size=24), vjust = 0),
arrangeGrob(ip, im, ncol = 2),
arrangeGrob(fp, fm, ncol = 2),
arrangeGrob(sfp, sfm, ncol = 2),
textGrob(label = "Position along RNA or Genome (1 is 5' end)",
gp=gpar(fontfamily = "Sans", size=12), vjust = 0))
# Arrange all the plots
full <- grid.arrange(grobs = arranged_grobs,
nrow = 5, ncol = 1, heights = c(.15,1,1,1,.07))
return(full)
}
offset_plot <- function(gr, overlap_type=c('sense', 'antisense'), primary_length, maximum_offset=10){
offset_data <- calculate_offsets(gr = gr,
overlap_type = overlap_type,
primary_length = primary_length,
maximum_offset = maximum_offset)
## Make the plot for the sense strand offsets
p <- ggplot() +
geom_line(data = offset_data,
mapping = aes(x = as.numeric(offset_data$offsets),
y = as.numeric(offset_data$ratio),
group=factor(widths),
color=factor(widths), linetype=factor(widths)
)) +
#geom_line(aes(linetype=, color=offset_data$widths)) +
facet_grid(.~strands,
labeller = as_labeller(c("+"="Plus Strand",
"-"="Minus Strand"))) +
# scale_color_distiller(palette = "Spectral") +
# scale_color_discrete() +
theme(legend.title=element_blank()) +
xlab("Offset from 5' end") +
ylab("Percentage of each length")
#
# ## Make the plot for the sense strand offsets
# q <- ggplot() +
# geom_line(data = antisense_offsets,
# mapping = aes(x = as.numeric(antisense_offsets$offsets),
# y = as.numeric(antisense_offsets$ratio),
# group=widths,
# color=widths)) +
# facet_grid(.~strands,
# labeller = as_labeller(c("+"="Plus Strand",
# "-"="Minus Strand"))) +
# scale_color_distiller(palette = "Spectral") +
# theme(legend.title=element_blank(),
# axis.title.x=element_blank()) +
# #xlab(NA) +
# ylab("Opposite Strand")
# same_strand <- ggplot_gtable(ggplot_build(p))
# opposite_strand <- ggplot_gtable(ggplot_build(q))
# Arrange the plots with the labels
# arranged_grobs <- list(textGrob(label = paste(as.character(length),
# as.character(five_prime_base),
# " Reads",
# sep = ""),
# gp=gpar(fontfamily = "Sans", size=24), vjust = 0),
# arrangeGrob(ip, im, ncol = 2),
# arrangeGrob(fp, fm, ncol = 2),
# arrangeGrob(sfp, sfm, ncol = 2),
# textGrob(label = "Position along RNA or Genome (1 is 5' end)",
# gp=gpar(fontfamily = "Sans", size=12), vjust = 0))
# arranged_grobs <- list(textGrob(label = paste("Offset from ",
# as.character(primary_length),
# #as.character(five_prime_base),
# "nt Reads",
# sep = ""),
# gp=gpar(fontfamily = "Sans", size=24), vjust = 0),
# arrangeGrob(same_strand),
# arrangeGrob(opposite_strand),
# textGrob(label = "Offset from 5' end",
# gp=gpar(fontfamily = "Sans",
# size=12),
# vjust = 0)
# )
# # Arrange all the plots
# full <- grid.arrange(grobs = arranged_grobs
# ,nrow = 4
# ,ncol = 1
# ,heights = c(.12,1,1,.09)
# )
return(p)
}
## Make plots and save
save_plot <- function(p, path, label){
print('ggsaving the plot')
triedOut <- try({
ggsave(filename = paste(label, ".svg", sep = ""),
plot = p,
device = "svg",
path = path)
})
#return(p)
}
# full <- grid.arrange(ip, im, fp, fm, nrow = 2, ncol = 2)
# gtable::gtable_show_layout(full)
# grid.draw(full)
# maxWidth <- unit.pmax(pgt$widths[2:3], qgt$widths[2:3])
# pgt$widths[2:3] <- maxWidth
# qgt$widths[2:3] <- maxWidth
# #grid.arrange(pgt, qgt, heights = c(7, 3), nrow = 2, ncol = 2)
# gtable::gtable_show_layout(arrangeGrob(pgt, qgt,
# heights = c(5, 5), #,
# widths = c(6, 6)))
# nrow = 2,
# ncol = 2))
# gtable::gtable_show_layout(arrangeGrob(pgt, qgt))
# q + annotate(geom = "text",
# x = c(11),
# y = c(),
# label = c("5'"),
# color=c("black"),
# size=c(5)) +
# annotate(geom = "text",
# x = c(33),
# y = c(2),
# label = c("3'"),
# color=c("black"),
# size=c(5))
# vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y)
# grid.newpage()
# pushViewport(viewport(layout = grid.layout(2, 100)))
# # print(interval_plus, vp = vplayout(1, (20-5):42))
# # print(interval_minus, vp = vplayout(1, 62:(62+22)))
# print(interval_plus, vp = vplayout(1, (10-5):52))
# print(interval_minus, vp = vplayout(1, 52:94))
# print(flanks_plus, vp = vplayout(2, (10-5):52))
# print(flanks_minus, vp = vplayout(2, 52:94))
#
#
# }
# }
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