Error in density.default(x = neg) : object 'neg' not found - r

Question

I tried to rewrite the below plot_densities fuction in order to use ggplot2.
plot_densities <- function(density) {
neg_density <- density[[1]]
pos_density <- density[[2]]
plot(
pos_density,
ylim = range(c(neg_density$y, pos_density$y)),
main = "Coverage plot of Sample 5",
xlab = "lenght 21",
col = 'blue',
type = 'h'
)
lines(neg_density, type = 'h', col = 'red')
}
Unfurtunately the new function below caused Error in density.default(x = neg) : object 'neg' not found
plot_densities2 <- function(density) {
neg_density <- density[[1]]
pos_density <- density[[2]]
densities = append(neg_density, pos_density)
ggplot(as.data.frame(densities), aes(x=x, y=y)) +
theme_bw() +
geom_density(alpha=0.5)
}
The full code can be found below and the data can be downloaded from here
#apt update && apt install zlib1g-dev
#install if necessary
source("http://bioconductor.org/biocLite.R")
biocLite("Rsamtools")
#load library
library(Rsamtools)
extracting_pos_neg_reads <- function(bam_fn) {
#read in entire BAM file
bam <- scanBam(bam_fn)
#names of the BAM fields
names(bam[[1]])
# [1] "qname" "flag" "rname" "strand" "pos" "qwidth" "mapq" "cigar"
# [9] "mrnm" "mpos" "isize" "seq" "qual"
#distribution of BAM flags
table(bam[[1]]$flag)
# 0 4 16
#1472261 775200 1652949
#function for collapsing the list of lists into a single list
#as per the Rsamtools vignette
.unlist <- function (x) {
## do.call(c, ...) coerces factor to integer, which is undesired
x1 <- x[[1L]]
if (is.factor(x1)) {
structure(unlist(x), class = "factor", levels = levels(x1))
} else {
do.call(c, x)
}
}
#store names of BAM fields
bam_field <- names(bam[[1]])
#go through each BAM field and unlist
list <- lapply(bam_field, function(y)
.unlist(lapply(bam, "[[", y)))
#store as data frame
bam_df <- do.call("DataFrame", list)
names(bam_df) <- bam_field
dim(bam_df)
#[1] 3900410 13
#---------
#use chr22 as an example
#how many entries on the negative strand of chr22?
###table(bam_df$rname == 'chr22' & bam_df$flag == 16)
# FALSE TRUE
#3875997 24413
#function for checking negative strand
check_neg <- function(x) {
if (intToBits(x)[5] == 1) {
return(T)
} else {
return(F)
}
}
#test neg function with subset of chr22
test <- subset(bam_df)#, rname == 'chr22')
dim(test)
#[1] 56426 13
table(apply(as.data.frame(test$flag), 1, check_neg))
#number same as above
#FALSE TRUE
#32013 24413
#function for checking positive strand
check_pos <- function(x) {
if (intToBits(x)[3] == 1) {
return(F)
} else if (intToBits(x)[5] != 1) {
return(T)
} else {
return(F)
}
}
#check pos function
table(apply(as.data.frame(test$flag), 1, check_pos))
#looks OK
#FALSE TRUE
#24413 32013
#store the mapped positions on the plus and minus strands
neg <- bam_df[apply(as.data.frame(bam_df$flag), 1, check_neg),
'pos']
length(neg)
#[1] 24413
pos <- bam_df[apply(as.data.frame(bam_df$flag), 1, check_pos),
'pos']
length(pos)
#[1] 32013
#calculate the densities
neg_density <- density(neg)
pos_density <- density(pos)
#display the negative strand with negative values
neg_density$y <- neg_density$y * -1
return (list(neg_density, pos_density))
}
plot_densities <- function(density) {
neg_density <- density[[1]]
pos_density <- density[[2]]
plot(
pos_density,
ylim = range(c(neg_density$y, pos_density$y)),
main = "Coverage plot of Sample 5",
xlab = "lenght 21",
col = 'blue',
type = 'h'
)
lines(neg_density, type = 'h', col = 'red')
}
plot_densities2 <- function(density) {
neg_density <- density[[1]]
pos_density <- density[[2]]
densities = append(neg_density, pos_density)
densities
ggplot(as.data.frame(densities), aes(x=x, y=y)) +
theme_bw() +
geom_density(alpha=0.5)
}
filenames <- c("~/sample5-21.sam-uniq.sorted.bam", "~/sample5-24.sam-uniq.sorted.bam")
for ( i in filenames){
print(i)
density <- extracting_pos_neg_reads(i)
plot_densities2(density)
}

Answer 1

Density objects seem to be not the best ones to be used with append and as.data.frame. In particular, they contain some elements that caused problems but at the same time are unnecessary. What we may do is to pick only x and y elements as to construct the relevant data frame:
plot_densities2 <- function(density) {
densities <- cbind(rbind(data.frame(density[[1]][1:2]), data.frame(density[[2]][1:2])),
id = rep(c("neg", "pos"), each = length(density[[1]]$x)))
print(ggplot(data = densities, aes(x = x, y = y, fill = id)) +
theme_bw() + geom_area(alpha = 0.5))
}