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rsem-plot-model
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rsem-plot-model
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#!/usr/bin/env Rscript
argv <- commandArgs(TRUE)
if (length(argv) != 2) {
cat("Usage: rsem-plot-model sample_name output_plot_file\n")
q(status = 1)
}
strvec <- strsplit(argv[1], split = "/")[[1]]
token <- strvec[length(strvec)]
stat.dir <- paste(argv[1], ".stat", sep = "")
if (!file.exists(stat.dir)) {
cat("Error: directory does not exist: ", stat.dir, "\n", sep = "")
q(status = 1)
}
modelF <- paste(stat.dir, "/", token, ".model", sep = "")
cntF <- paste(stat.dir, "/", token, ".cnt", sep = "")
pdf(argv[2])
con <- file(modelF, open = "r")
# model type and forward probability
model_type <- as.numeric(readLines(con, n = 4)[1])
# fragment length distribution
strvec <- readLines(con, n = 3)
vec <- as.numeric(strsplit(strvec[1], split = " ")[[1]])
maxL <- vec[2] # maxL used for Profile
x <- (vec[1] + 1) : vec[2]
y <- as.numeric(strsplit(strvec[2], split = " ")[[1]])
mode_len = which(y == max(y)) + vec[1]
mean <- weighted.mean(x, y)
std <- sqrt(weighted.mean((x - mean)^2, y))
plot(x, y, type = "h",
main = "Fragment Length Distribution",
sub = sprintf("Mode = %d, Mean = %.1f, and Std = %.1f", mode_len, mean, std),
xlab = "Fragment Length",
ylab = "Probability")
abline(v = mode_len, col = "red", lty = "dashed")
# mate length distribution
if (model_type == 0 || model_type == 1) bval <- as.numeric(readLines(con, n = 1)[1]) else bval <- 1
if (bval == 1) {
list <- strsplit(readLines(con, n = 2), split = " ")
vec <- as.numeric(list[[1]])
maxL <- vec[2]
x <- (vec[1] + 1) : vec[2]
y <- as.numeric(list[[2]])
mode_len = which(y == max(y)) + vec[1]
mean <- weighted.mean(x, y)
std <- sqrt(weighted.mean((x - mean)^2, y))
plot(x, y, type = "h",
main = "Read Length Distribution",
sub = sprintf("Mode = %d, Mean = %.1f, and Std = %.1f", mode_len, mean, std),
xlab = "Read Length",
ylab = "Probability")
}
strvec <- readLines(con, n = 1)
# RSPD
bval <- as.numeric(readLines(con, n = 1)[1])
if (bval == 1) {
bin_size <- as.numeric(readLines(con, n = 1)[1])
y <- as.numeric(strsplit(readLines(con, n = 1), split = " ")[[1]])
par(cex.axis = 0.7)
barplot(y, space = 0, names.arg = 1:bin_size, main = "Read Start Position Distribution", xlab = "Bin #", ylab = "Probability")
par(cex.axis = 1.0)
}
strvec <- readLines(con, n = 1)
# plot sequencing errors
if (model_type == 1 || model_type == 3) {
# skip QD
N <- as.numeric(readLines(con, n = 1)[1])
readLines(con, n = N + 1)
readLines(con, n = 1) # for the blank line
# QProfile
readLines(con, n = 1)
x <- c()
peA <- c() # probability of sequencing error given reference base is A
peC <- c()
peG <- c()
peT <- c()
for (i in 1 : N) {
strvec <- readLines(con, n = 6)
list <- strsplit(strvec[1:4], split = " ")
vecA <- as.numeric(list[[1]])
vecC <- as.numeric(list[[2]])
vecG <- as.numeric(list[[3]])
vecT <- as.numeric(list[[4]])
if (sum(c(vecA, vecC, vecG, vecT)) < 1e-8) next
x <- c(x, (i - 1))
peA <- c(peA, ifelse(sum(vecA) < 1e-8, NA, -10 * log10(1.0 - vecA[1])))
peC <- c(peC, ifelse(sum(vecC) < 1e-8, NA, -10 * log10(1.0 - vecC[2])))
peG <- c(peG, ifelse(sum(vecG) < 1e-8, NA, -10 * log10(1.0 - vecG[3])))
peT <- c(peT, ifelse(sum(vecT) < 1e-8, NA, -10 * log10(1.0 - vecT[4])))
}
matplot(x, cbind(peA, peC, peG, peT), type = "b", lty = 1:4, pch = 0:3, col = 1:4,
main = "Observed Quality vs. Phred Quality Score",
xlab = "Phred Quality Score",
ylab = "Observed Quality")
legend("topleft", c("A", "C", "G", "T"), lty = 1:4, pch = 0:3, col = 1:4)
} else {
# Profile
readLines(con, n = 1)
x <- c()
peA <- c() # probability of sequencing error given reference base is A
peC <- c()
peG <- c()
peT <- c()
for (i in 1: maxL) {
strvec <- readLines(con, n = 6)
list <- strsplit(strvec[1:4], split = " ")
vecA <- as.numeric(list[[1]])
vecC <- as.numeric(list[[2]])
vecG <- as.numeric(list[[3]])
vecT <- as.numeric(list[[4]])
if (sum(c(vecA, vecC, vecG, vecT)) < 1e-8) next
x <- c(x, i)
peA <- c(peA, ifelse(sum(vecA) < 1e-8, NA, (1.0 - vecA[1]) * 100))
peC <- c(peC, ifelse(sum(vecC) < 1e-8, NA, (1.0 - vecC[2]) * 100))
peG <- c(peG, ifelse(sum(vecG) < 1e-8, NA, (1.0 - vecG[3]) * 100))
peT <- c(peT, ifelse(sum(vecT) < 1e-8, NA, (1.0 - vecT[4]) * 100))
}
matplot(x, cbind(peA, peC, peG, peT), type = "b", lty = 1:4, pch = 0:3, col = 1:4, main = "Position vs. Percentage Sequence Error", xlab = "Position", ylab = "Percentage of Sequencing Error")
legend("topleft", c("A", "C", "G", "T"), lty = 1:4, pch = 0:3, col = 1:4)
}
close(con)
# Alignment statistics
pair <- read.table(file = cntF, skip = 3, sep = "\t")
stat_len = dim(pair)[1]
upper_bound = pair[stat_len - 1, 1]
my_labels = append(0:upper_bound, pair[stat_len, 1])
my_heights = rep(0, upper_bound + 2)
dummy = sapply(1:(stat_len - 1), function(id) { my_heights[pair[id, 1] + 1] <<- pair[id, 2] })
my_heights[upper_bound + 2] = pair[stat_len, 2]
my_colors = c("green", "blue", rep("dimgrey", upper_bound - 1), "red")
barplot(my_heights, names.arg = my_labels,
col = my_colors, border = NA,
xlab = "Number of alignments per read",
ylab = "Number of reads",
main = "Alignment statistics")
pie_values = c(my_heights[1], my_heights[2], sum(my_heights[3:(upper_bound + 1)]), my_heights[upper_bound + 2])
pie_names = c("Unalignable", "Unique", "Multi", "Filtered")
pie_labels = sprintf("%s %.0f%%", pie_names, pie_values * 100.0 / sum(pie_values))
par(fig = c(0.4, 1, 0.35, 0.95), new = T)
pie(pie_values, labels = pie_labels, col = c("green", "blue", "dimgrey", "red"), clockwise = T, init.angle = 270, cex = 0.8)
par(fig = c(0, 1, 0, 1))
dev.off.output <- dev.off()