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4_Plot_bCT_stats.R
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4_Plot_bCT_stats.R
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### Plot ipyrad stats
args = commandArgs(trailingOnly=TRUE)
workdirectory <- args[1]
setwd(workdirectory)
#setwd("/home/lois/Documents/Sim_IQTREE_RADsea/stats_bCT")
loci <- read.table("./loci_all_CT.txt", sep=" ", header=T)
missing <- read.table("./missing_all_CT.txt", sep=" ", header=T)
var <- read.table("./var_all_CT.txt", header=T, sep=" ")
## loci statistics ##
#number of loci
Nsamples <- max(unique(loci[,1]))
loci_4 <- loci[which(loci[,1] == Nsamples), 3]
loci_50 <- loci_4 - loci[which(loci[,1] == floor((Nsamples*0.5)-1)), 3]
loci_80 <- loci_4 - loci[which(loci[,1] == floor((Nsamples*0.8)-1)), 3]
index <- unique(loci[,4])
max_c <- max(loci_4, loci_50, loci_80)
min_c <- min(loci_4, loci_50, loci_80)
#proportion of the loci
prop_50 <- loci_50/loci_4
prop_80 <- loci_80/loci_4
max_p <- max(!is.na(prop_50), !is.na(prop_80))
min_p <- min(!is.na(prop_50), !is.na(prop_80))
#Change in the proportion of the loci
delta_p50 <- diff(prop_50)
delta_p50[is.na(delta_p50)] <- 0
delta_p80 <- diff(prop_80)
delta_p80[is.na(delta_p80)] <- 0
index_diff <- unique(loci[,4])[-1]
#Percentage of missing data
max_m <- max(missing[,1], missing[,2])
min_m <- min(missing[,1], missing[,2])
delta_m_SNP <- diff(missing[,1])
delta_m_SNP[is.na(delta_m_SNP)] <- 0
delta_m_SEQ <- diff(missing[,2])
delta_m_SEQ[is.na(delta_m_SEQ)] <- 0
max_dm <- max(delta_m_SNP, delta_m_SEQ)
min_dm <- min(delta_m_SNP, delta_m_SEQ)
## plots ##
pdf(file="./bCT_plots.pdf")
#par(mar=c(5.1, 4.1, 4.1, 5.7), xpd=TRUE)
plot(index, loci_4, pch=19, cex=0.3, ylim=c(min_c, max_c), ylab="number of loci", xlab="bCT",
main="Number of loci in the assembly", type="b")
points(index, loci_50, pch=19, cex=0.3, col="red", type="b")
points(index, loci_80, pch=19, cex=0.3, col="blue", type="b")
legend("topright", inset=c(0,0), legend=c("all","50%", "80%"), pch=c(19,19,19), col=c("black", "red", "blue"))
#par(mar=c(5.1, 4.1, 4.1, 5.7), xpd=TRUE)
plot(index, prop_50, pch=19, cex=0.3, ylim=c(0, max_p), col="blue",
ylab="proportion of the loci", xlab="bCT", main="Proportion of shared loci", type="b")
points(index, prop_80, pch=19, cex=0.3, ylim=c(min_p, max_p), col="red", type="b")
legend("topright", inset=c(0,0), legend=c("50%", "80%"), pch=c(19,19), col=c("red", "blue"))
plot(delta_p50~index_diff, pch=19, cex=0.5, ylim=c(min(delta_p50), max(delta_p50)), col="blue",
ylab="delta loci 50%", xlab="delta bCT", main="50%", type="b")
plot(delta_p80~index_diff, pch=19, cex=0.5, ylim=c(min(delta_p80), max(delta_p80)), col="red",
ylab="delta loci 80%", xlab="delta bCT", main="80%", type="b")
#par(mar=c(5.1, 4.1, 4.1, 4.1), xpd=TRUE)
plot(missing[,2]~missing[,3], pch=19, cex=0.5, ylab="% of missing data", xlab="bCT", col="blue",
ylim=c(min_m, max_m), type="b")
points(missing[,1]~missing[,3], pch=19, cex=0.5, col="red", type="b")
legend("topleft", inset=c(0,0), legend=c("Concatenation", "SNPs"), pch=c(19,19), col=c("blue", "red"))
#par(mar=c(5.1, 4.1, 4.1, 4.1), xpd=TRUE)
plot(delta_m_SNP~index_diff, pch=19, cex=0.5, ylim=c(min_dm, max_dm), ylab="delta % missing",
xlab="delta bCT", col="red", type="b")
points(delta_m_SEQ~index_diff, pch=19, cex=0.5, col="blue", type="b")
legend("topleft", inset=c(0,0), legend=c("Concatenation", "SNPs"), pch=c(19,19), col=c("blue", "red"))
dev.off()