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plot-linear-flasso-oct-2018-update-color.R
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plot-linear-flasso-oct-2018-update-color.R
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compare_linear_flasso_plot_v1 <- function(dat, dat.name, dat.varname, chr, centr, save=F) {
dat <- dat[dat$chr== chr, ]
dat$Tamp.pos <- dat$start
dat$Tamp.pos[dat$arm == "R"] <- dat$stop[dat$arm == "R"]
# Pull out data from left and right arms
dat.L <- dat[dat$arm == "L", ]
dat.L <- dat.L[order(dat.L$Tamp.pos), ]
dat.R <- dat[dat$arm == "R", ]
dat.R <- dat.R[order(dat.R$Tamp.pos), ]
# If there are fewer than 7 data points on either side,
# then too few data to do a fused lasso analysis ...
if(nrow(dat.R) < 7 || nrow(dat.L) < 7) {
return()
}
flasso.L <- fusedlasso1d(y=dat.L[, dat.varname], pos=dat.L$Tamp.pos)
cv.flasso.L <- cv.trendfilter(flasso.L)
cv.flasso.L$i.2se <- which(cv.flasso.L$lambda == max(cv.flasso.L$lambda[cv.flasso.L$err <= min(cv.flasso.L$err) +
2*cv.flasso.L$se[which.min(cv.flasso.L$err)]]))
flasso.fit.L.1se <- data.frame(Tamp.pos=dat.L$Tamp.pos, fit=flasso.L$fit[, cv.flasso.L$i.1se])
flasso.fit.L.2se <- data.frame(Tamp.pos=dat.L$Tamp.pos, fit=flasso.L$fit[, cv.flasso.L$i.2se])
flasso.R <- fusedlasso1d(y=dat.R[, dat.varname], pos=dat.R$Tamp.pos)
cv.flasso.R <- cv.trendfilter(flasso.R)
cv.flasso.R$i.2se <- which(cv.flasso.R$lambda == max(cv.flasso.R$lambda[cv.flasso.R$err <= min(cv.flasso.R$err) +
2*cv.flasso.R$se[which.min(cv.flasso.R$err)]]))
flasso.fit.R.1se <- data.frame(Tamp.pos=dat.R$Tamp.pos, fit=flasso.R$fit[, cv.flasso.R$i.1se])
flasso.fit.R.2se <- data.frame(Tamp.pos=dat.R$Tamp.pos, fit=flasso.R$fit[, cv.flasso.R$i.2se])
lmfit <- lm(as.formula(paste(dat.varname, "~", "Tamp.length", sep="")), data=dat)
# Create data frame for use in plotting
lmfitplot <- data.frame(Tamp.pos= c(dat$Tamp.pos,centr[chr], centr[chr]+1e-16,dat$chrlength[1], 0),
fit=predict(lmfit, data.frame(Tamp.length= c(dat$Tamp.length, centr[chr], dat$chrlength[1]-centr[chr], 0, 0))))
g <- ggplot() + geom_segment(aes(x=0,y=0,xend=chrom.lengths$end[chr],yend=0)) + geom_errorbar(aes_string(x="Tamp.pos",
ymin=paste(dat.varname, " - ", dat.varname, ".se", sep=""),
ymax=paste(dat.varname, " + ", dat.varname, ".se", sep="")), width=.1,
data=dat[dat$chr == chr,],color='#C00000') +
geom_point(aes_string(x="Tamp.pos", y=dat.varname),
data=dat[dat$chr == chr,], color='#C00000') +
geom_point(aes(x=centr[chr], y=0),size=4) +
geom_line(data=lmfitplot, aes(x=Tamp.pos, y=fit), linetype="dashed")+
# geom_line(data=flasso.fit.L.1se, aes(x=Tamp.pos, y=fit), lty="dotted") +
geom_line(data=flasso.fit.L.2se, aes(x=Tamp.pos, y=fit)) +
#geom_line(data=flasso.fit.R.1se, aes(x=Tamp.pos, y=fit), lty="dotted") +
geom_line(data=flasso.fit.R.2se, aes(x=Tamp.pos, y=fit)) +
ggtitle(paste(dat.name, ", Chr ", chr, sep="")) +
labs(x= "starting nucleotide position of copied DNA", y = "relative fitness")+
theme_minimal() +
theme(axis.line.y = element_line(colour="black",size=0.4),axis.ticks.y= element_line(size=0.3))
if(save) {
ggsave(paste(dat.varname, "-chr", chr, "-fit-v1-col.pdf", sep=""), width =12, height=6)
} else {
print(g)
}
}
####################################################################
compare_linear_flasso_plot_v2 <- function(dat, dat.name, dat.varname, chr, centr, col, save=F) {
dat <- dat[dat$chr== chr, ]
dat$Tamp.pos <- dat$start
dat$Tamp.pos[dat$arm == "R"] <- dat$stop[dat$arm == "R"]
dat$armL <- ifelse(dat$arm == "R", 1, 0)
dat$Tamp.pos <- dat$Tamp.pos/1000
dat$Tamp.length <- dat$Tamp.length/1000
dat$chrlength <- dat$chrlength/1000
centr = centr/1000
chrom.lengths$end <- chrom.lengths$end/1000
# Pull out data from left and right arms
dat.L <- dat[dat$arm == "L", ]
dat.L <- dat.L[order(dat.L$Tamp.pos), ]
dat.R <- dat[dat$arm == "R", ]
dat.R <- dat.R[order(dat.R$Tamp.pos), ]
# If there are fewer than 7 data points on either side,
# then too few data to do a fused lasso analysis ...
if(nrow(dat.R) < 7 || nrow(dat.L) < 7) {
return()
}
flasso.L <- fusedlasso1d(y=dat.L[, dat.varname], pos=dat.L$Tamp.pos)
cv.flasso.L <- cv.trendfilter(flasso.L)
cv.flasso.L$i.2se <- which(cv.flasso.L$lambda == max(cv.flasso.L$lambda[cv.flasso.L$err <= min(cv.flasso.L$err) +
2*cv.flasso.L$se[which.min(cv.flasso.L$err)]]))
flasso.fit.L.1se <- data.frame(Tamp.pos=dat.L$Tamp.pos, fit=flasso.L$fit[, cv.flasso.L$i.1se])
flasso.fit.L.2se <- data.frame(Tamp.pos=dat.L$Tamp.pos, fit=flasso.L$fit[, cv.flasso.L$i.2se])
flasso.R <- fusedlasso1d(y=dat.R[, dat.varname], pos=dat.R$Tamp.pos)
cv.flasso.R <- cv.trendfilter(flasso.R)
cv.flasso.R$i.2se <- which(cv.flasso.R$lambda == max(cv.flasso.R$lambda[cv.flasso.R$err <= min(cv.flasso.R$err) +
2*cv.flasso.R$se[which.min(cv.flasso.R$err)]]))
flasso.fit.R.1se <- data.frame(Tamp.pos=dat.R$Tamp.pos, fit=flasso.R$fit[, cv.flasso.R$i.1se])
flasso.fit.R.2se <- data.frame(Tamp.pos=dat.R$Tamp.pos, fit=flasso.R$fit[, cv.flasso.R$i.2se])
lmfit <- lm(as.formula(paste(dat.varname, "~", "Tamp.pos*armL", sep="")), data=dat)
# Create data frame for use in plotting
lmfitplot <- data.frame(Tamp.pos= c(dat$Tamp.pos,centr[chr], centr[chr]+1e-16,
dat$chrlength[1], 0),
fit=predict(lmfit,
data.frame(Tamp.pos= c(dat$Tamp.pos,centr[chr], centr[chr]+1e-16,
dat$chrlength[1], 0),
armL=c(dat$armL, 0, 1, 1, 0))))
g <- ggplot() + geom_segment(aes(x=0,y=0,xend=chrom.lengths$end[chr],yend=0)) + geom_errorbar(aes_string(x="Tamp.pos",
ymin=paste(dat.varname, " - ", dat.varname, ".se", sep=""),
ymax=paste(dat.varname, " + ", dat.varname, ".se", sep="")), width=.1,
data=dat[dat$chr == chr, ], color=col) +
geom_point(aes_string(x="Tamp.pos", y=dat.varname),
data=dat[dat$chr == chr,],color=col) +
geom_point(aes(x=centr[chr], y=0),size=4) +
geom_line(data=lmfitplot, aes(x=Tamp.pos, y=fit), linetype="dashed")+
# geom_line(data=flasso.fit.L.1se, aes(x=Tamp.pos, y=fit), lty="dotted") +
geom_line(data=flasso.fit.L.2se, aes(x=Tamp.pos, y=fit), size=1.4) +
#geom_line(data=flasso.fit.R.1se, aes(x=Tamp.pos, y=fit), lty="dotted") +
geom_line(data=flasso.fit.R.2se, aes(x=Tamp.pos, y=fit), size=1.4) +
ggtitle(paste("Chromosome ", chr, sep="")) +
labs(x= "starting nucleotide position of copied DNA (kb)", y = "Relative fitness")+
theme_minimal() +
theme(axis.line.y = element_line(colour="black",size=0.4),axis.ticks.y= element_line(size=0.3),text = element_text(size=20))
# coord_cartesian(ylim=c(-0.5,0.5))
if(save) {
ggsave(paste(dat.varname, "-chr", chr, "-fit-v2-col.pdf", sep=""), width =12, height=6)
} else {
print(g)
}
}
compare_linear_flasso_plot_v3 <- function(dat, dat.name, dat.varname, chr, centr, save=F) {
dat <- dat[dat$chr== chr, ]
dat$Tamp.pos <- dat$start
dat$Tamp.pos[dat$arm == "R"] <- dat$stop[dat$arm == "R"]
dat$armL <- ifelse(dat$arm == "R", 1, 0)
dat$Tamp.posfit <- dat$Tamp.pos
dat$Tamp.posfit[dat$arm == "R"] <- -dat$Tamp.posfit[dat$arm == "R"]
# Pull out data from left and right arms
dat.L <- dat[dat$arm == "L", ]
dat.L <- dat.L[order(dat.L$Tamp.pos), ]
dat.R <- dat[dat$arm == "R", ]
dat.R <- dat.R[order(dat.R$Tamp.pos), ]
# If there are fewer than 7 data points on either side,
# then too few data to do a fused lasso analysis ...
if(nrow(dat.R) < 7 || nrow(dat.L) < 7) {
return()
}
flasso.L <- fusedlasso1d(y=dat.L[, dat.varname], pos=dat.L$Tamp.pos)
cv.flasso.L <- cv.trendfilter(flasso.L)
cv.flasso.L$i.2se <- which(cv.flasso.L$lambda == max(cv.flasso.L$lambda[cv.flasso.L$err <= min(cv.flasso.L$err) +
2*cv.flasso.L$se[which.min(cv.flasso.L$err)]]))
flasso.fit.L.1se <- data.frame(Tamp.pos=dat.L$Tamp.pos, fit=flasso.L$fit[, cv.flasso.L$i.1se])
flasso.fit.L.2se <- data.frame(Tamp.pos=dat.L$Tamp.pos, fit=flasso.L$fit[, cv.flasso.L$i.2se])
flasso.R <- fusedlasso1d(y=dat.R[, dat.varname], pos=dat.R$Tamp.pos)
cv.flasso.R <- cv.trendfilter(flasso.R)
cv.flasso.R$i.2se <- which(cv.flasso.R$lambda == max(cv.flasso.R$lambda[cv.flasso.R$err <= min(cv.flasso.R$err) +
2*cv.flasso.R$se[which.min(cv.flasso.R$err)]]))
flasso.fit.R.1se <- data.frame(Tamp.pos=dat.R$Tamp.pos, fit=flasso.R$fit[, cv.flasso.R$i.1se])
flasso.fit.R.2se <- data.frame(Tamp.pos=dat.R$Tamp.pos, fit=flasso.R$fit[, cv.flasso.R$i.2se])
# Fit the linear model
lmfit <- lm(as.formula(paste(dat.varname, "~", "Tamp.posfit + armL", sep="")), data=dat)
# Create data frame for use in plotting
lmfitplot <- data.frame(Tamp.pos= c(dat$Tamp.pos,centr[chr], centr[chr]+1e-16,
0, dat$chrlength[1]),
fit=predict(lmfit,
data.frame(Tamp.posfit= c(dat$Tamp.posfit,centr[chr], -(centr[chr]+1e-16), 0,
-dat$chrlength[1]),
armL=c(dat$armL, 0, 1, 0, 1))))
g <- ggplot() + geom_segment(aes(x=0,y=0,xend=chrom.lengths$end[chr],yend=0)) + geom_errorbar(aes_string(x="Tamp.pos",
ymin=paste(dat.varname, " - ", dat.varname, ".se", sep=""),
ymax=paste(dat.varname, " + ", dat.varname, ".se", sep="")), width=.1,
data=dat[dat$chr == chr, ],color='#C00000') +
geom_point(aes_string(x="Tamp.pos", y=dat.varname),
data=dat[dat$chr == chr,],color='#C00000') +
geom_point(aes(x=centr[chr], y=0)) +
geom_line(data=lmfitplot, aes(x=Tamp.pos, y=fit), linetype="dashed")+
# geom_line(data=flasso.fit.L.1se, aes(x=Tamp.pos, y=fit), lty="dotted") +
geom_line(data=flasso.fit.L.2se, aes(x=Tamp.pos, y=fit)) +
#geom_line(data=flasso.fit.R.1se, aes(x=Tamp.pos, y=fit), lty="dotted") +
geom_line(data=flasso.fit.R.2se, aes(x=Tamp.pos, y=fit)) +
ggtitle(paste(dat.name, ", Chr ", chr, sep="")) +
labs(x= "starting nucleotide position of copied DNA", y = "relative fitness")+
theme_minimal() +
theme(axis.line.y = element_line(colour="black",size=0.4),axis.ticks.y= element_line(size=0.3))
if(save) {
ggsave(paste(dat.varname, "-chr", chr, "-fit-v3-col.pdf", sep=""), width =12, height=6)
} else {
print(g)
}
}
compare_linear_flasso_plot_v4 <- function(dat, dat.name, dat.varname, chr, centr, save=F) {
dat <- dat[dat$chr== chr, ]
dat$Tamp.pos <- dat$start
dat$Tamp.pos[dat$arm == "R"] <- dat$stop[dat$arm == "R"]
# Pull out data from left and right arms
dat.L <- dat[dat$arm == "L", ]
dat.L <- dat.L[order(dat.L$Tamp.pos), ]
dat.R <- dat[dat$arm == "R", ]
dat.R <- dat.R[order(dat.R$Tamp.pos), ]
# If there are fewer than 7 data points on either side,
# then too few data to do a fused lasso analysis ...
if(nrow(dat.R) < 7 || nrow(dat.L) < 7) {
return()
}
flasso.L <- fusedlasso1d(y=dat.L[, dat.varname], pos=dat.L$Tamp.pos)
cv.flasso.L <- cv.trendfilter(flasso.L)
cv.flasso.L$i.2se <- which(cv.flasso.L$lambda == max(cv.flasso.L$lambda[cv.flasso.L$err <= min(cv.flasso.L$err) +
2*cv.flasso.L$se[which.min(cv.flasso.L$err)]]))
flasso.fit.L.1se <- data.frame(Tamp.pos=dat.L$Tamp.pos, fit=flasso.L$fit[, cv.flasso.L$i.1se])
flasso.fit.L.2se <- data.frame(Tamp.pos=dat.L$Tamp.pos, fit=flasso.L$fit[, cv.flasso.L$i.2se])
flasso.R <- fusedlasso1d(y=dat.R[, dat.varname], pos=dat.R$Tamp.pos)
cv.flasso.R <- cv.trendfilter(flasso.R)
cv.flasso.R$i.2se <- which(cv.flasso.R$lambda == max(cv.flasso.R$lambda[cv.flasso.R$err <= min(cv.flasso.R$err) +
2*cv.flasso.R$se[which.min(cv.flasso.R$err)]]))
flasso.fit.R.1se <- data.frame(Tamp.pos=dat.R$Tamp.pos,
fit=flasso.R$fit[, cv.flasso.R$i.1se])
flasso.fit.R.2se <- data.frame(Tamp.pos=dat.R$Tamp.pos,
fit=flasso.R$fit[, cv.flasso.R$i.2se])
# Fit the linear model
lmfit <- lm(as.formula(paste(dat.varname, "~", "Tamp.length:(arm == \"R\") + Tamp.length", sep="")),
data=dat)
# Create data frame for use in plotting
lmfitplot <- data.frame(Tamp.pos=c(0, centr[chr],
centr[chr]+1e-16, dat$chrlength[1]),
fit=predict(lmfit, data.frame(Tamp.length= c(0, centr[chr],
dat$chrlength[1] - (centr[chr] +1e-16), 0),
arm=c("L", "L", "R", "R"))))
g <- ggplot() + geom_segment(aes(x=0,y=0,xend=chrom.lengths$end[chr],yend=0)) + geom_errorbar(aes_string(x="Tamp.pos",
ymin=paste(dat.varname, " - ", dat.varname, ".se", sep=""),
ymax=paste(dat.varname, " + ", dat.varname, ".se", sep="")), width=.1,
data=dat[dat$chr == chr, ],color='#C00000') +
geom_point(aes_string(x="Tamp.pos", y=dat.varname),
data=dat[dat$chr == chr,],color='#C00000') +
geom_point(aes(x=centr[chr], y=0)) +
geom_line(data=lmfitplot, aes(x=Tamp.pos, y=fit), linetype="dashed")+
# geom_line(data=flasso.fit.L.1se, aes(x=Tamp.pos, y=fit), lty="dotted") +
geom_line(data=flasso.fit.L.2se, aes(x=Tamp.pos, y=fit)) +
#geom_line(data=flasso.fit.R.1se, aes(x=Tamp.pos, y=fit), lty="dotted") +
geom_line(data=flasso.fit.R.2se, aes(x=Tamp.pos, y=fit)) +
ggtitle(paste(dat.name, ", Chr ", chr, sep="")) +
labs(x= "starting nucleotide position of copied DNA", y = "relative fitness")+
theme_minimal() +
theme(axis.line.y = element_line(colour="black",size=0.4),axis.ticks.y= element_line(size=0.3))
if(save) {
ggsave(paste(dat.varname, "-chr", chr, "-fit-v4-col.pdf", sep=""), width =12, height=6)
write.table(flasso.fit.R.2se, file = paste(dat.varname, "-chr", chr, "-fit-v4-R-arm.csv", sep=""), quote=FALSE,sep = ',',row.names = FALSE)
write.table(flasso.fit.L.2se, file = paste(dat.varname, "-chr", chr, "-fit-v4-L-arm.csv", sep=""), quote=FALSE,sep = ',',row.names = FALSE)
} else {
print(g)
}
}