3 argv <- commandArgs(TRUE)
4 if (length(argv) != 2) {
5 cat("Usage: rsem-plot-model modelF outF\n")
9 con <- file(argv[1], open = "r")
12 # model type and forward probability
13 model_type <- as.numeric(readLines(con, n = 4)[1])
15 # fragment length distribution
16 strvec <- readLines(con, n = 3)
17 vec <- as.numeric(strsplit(strvec[1], split = " ")[[1]])
18 maxL <- vec[2] # maxL used for Profile
19 x <- (vec[1] + 1) : vec[2]
20 y <- as.numeric(strsplit(strvec[2], split = " ")[[1]])
21 mean <- weighted.mean(x, y)
22 std <- sqrt(weighted.mean((x - mean)^2, y))
23 plot(x, y, type = "h", main = "Fragment Length Distribution", sub = paste("Mean = ", mean, ", Std = ", std), xlab = "Fragment Length", ylab = "Probability")
25 # mate length distribution
26 if (model_type == 0 || model_type == 1) bval <- as.numeric(readLines(con, n = 1)[1]) else bval <- 1
29 list <- strsplit(readLines(con, n = 2), split = " ")
30 vec <- as.numeric(list[[1]])
32 x <- (vec[1] + 1) : vec[2]
33 y <- as.numeric(list[[2]])
34 mean <- weighted.mean(x, y)
35 std <- sqrt(weighted.mean((x - mean)^2, y))
36 plot(x, y, type = "h", main = "Mate Length Distribution", sub = paste("Mean = ", mean, ", Std = ", std), xlab = "Mate Length", ylab = "Probability")
38 strvec <- readLines(con, n = 1)
41 bval <- as.numeric(readLines(con, n = 1)[1])
43 bin_size <- as.numeric(readLines(con, n = 1)[1])
44 y <- as.numeric(strsplit(readLines(con, n = 1), split = " ")[[1]])
46 barplot(y, space = 0, names.arg = 1:bin_size, main = "Read Start Position Distribution", xlab = "Bin #", ylab = "Probability")
49 strvec <- readLines(con, n = 1)
51 # plot sequencing errors
52 if (model_type == 1 || model_type == 3) {
54 N <- as.numeric(readLines(con, n = 1)[1])
55 readLines(con, n = N + 1)
56 readLines(con, n = 1) # for the blank line
61 peA <- c() # probability of sequencing error given reference base is A
67 strvec <- readLines(con, n = 6)
68 list <- strsplit(strvec[1:4], split = " ")
69 vecA <- as.numeric(list[[1]])
70 vecC <- as.numeric(list[[2]])
71 vecG <- as.numeric(list[[3]])
72 vecT <- as.numeric(list[[4]])
73 if (sum(c(vecA, vecC, vecG, vecT)) < 1e-8) break
74 peA <- c(peA, ifelse(sum(vec) < 1e-8, NA, -10 * log(1.0 - vecA[1])))
75 peC <- c(peC, ifelse(sum(vec) < 1e-8, NA, -10 * log(1.0 - vecC[2])))
76 peG <- c(peG, ifelse(sum(vec) < 1e-8, NA, -10 * log(1.0 - vecG[3])))
77 peT <- c(peT, ifelse(sum(vec) < 1e-8, NA, -10 * log(1.0 - vecT[4])))
80 x <- 0 : (length(peA) - 1)
81 matplot(x, cbind(peA, peC, peG, peT), type = "b", lty = 1:4, pch = 0:3, col = 1:4, main = "Quality Score vs. Observed Quality", xlab = "Quality Score", ylab = "Observed Quality")
82 legend("topleft", c("A", "C", "G", "T"), lty = 1:4, pch = 0:3, col = 1:4)
87 peA <- c() # probability of sequencing error given reference base is A
93 strvec <- readLines(con, n = 6)
94 list <- strsplit(strvec[1:4], split = " ")
95 vecA <- as.numeric(list[[1]])
96 vecC <- as.numeric(list[[2]])
97 vecG <- as.numeric(list[[3]])
98 vecT <- as.numeric(list[[4]])
99 if (sum(c(vecA, vecC, vecG, vecT)) < 1e-8) break
100 peA <- c(peA, ifelse(sum(vec) < 1e-8, NA, (1.0 - vecA[1]) * 100))
101 peC <- c(peC, ifelse(sum(vec) < 1e-8, NA, (1.0 - vecC[2]) * 100))
102 peG <- c(peG, ifelse(sum(vec) < 1e-8, NA, (1.0 - vecG[3]) * 100))
103 peT <- c(peT, ifelse(sum(vec) < 1e-8, NA, (1.0 - vecT[4]) * 100))
107 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")
108 legend("topleft", c("A", "C", "G", "T"), lty = 1:4, pch = 0:3, col = 1:4)