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3-melanoma_dropseq.R
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# Mo Huang, mohuang@wharton.upenn.edu
# Perform Melanoma Drop-seq, FISH analysis
library(SAVER)
dropseq <- readRDS("SAVER-data/melanoma_dropseq.rds")
fish <- read.table("SAVER-data/fishSubset.txt", header = TRUE, row.names = 1)
fish.filt <- fish[fish[, "GAPDH"] < quantile(fish[, "GAPDH"], 0.9) &
fish[, "GAPDH"] > quantile(fish[, "GAPDH"], 0.1), ]
fish.norm <-
sweep(fish.filt, 1, fish.filt[, "GAPDH"]/mean(fish.filt[, "GAPDH"]), "/")
n.genes <- nrow(dropseq)
n.cells <- ncol(dropseq)
gene.names <- rownames(dropseq)
cell.names <- colnames(dropseq)
genes <- which(gene.names %in% colnames(fish.norm))
sf <- colSums(dropseq)/mean(colSums(dropseq))
normalize.gapdh <- function(x) {
x.filt <- x[, x["GAPDH", ] < quantile(x["GAPDH", ], 0.9) &
x["GAPDH", ] > quantile(x["GAPDH", ], 0.1)]
x.norm <- sweep(x.filt, 2, x.filt["GAPDH", ]/mean(x.filt["GAPDH", ]), "/")
}
dropseq.filt <- dropseq[genes, dropseq["GAPDH", ] < quantile(dropseq["GAPDH", ], 0.9) &
dropseq["GAPDH", ] > quantile(dropseq["GAPDH", ], 0.1)]
dropseq.norm <- normalize.gapdh(dropseq[genes, ])
saver <- readRDS("SAVER-data/melanoma_dropseq_saver.rds")
saver.filt <- saver$estimate[, saver$estimate["GAPDH", ] <
quantile(saver$estimate["GAPDH", ], 0.9) &
saver$estimate["GAPDH", ] >
quantile(saver$estimate["GAPDH", ], 0.1)]
magic <- readRDS("SAVER-data/melanoma_dropseq_magic.rds")
magic.filt <- magic[, magic["GAPDH", ] < quantile(magic["GAPDH", ], 0.9) &
magic["GAPDH", ] > quantile(magic["GAPDH", ], 0.1)]
magic.norm <- normalize.gapdh(magic)
scimpute <- readRDS("SAVER-data/melanoma_dropseq_scimpute.rds")
scimpute.filt <-
scimpute[, scimpute["GAPDH", ] < quantile(scimpute["GAPDH", ], 0.9) &
scimpute["GAPDH", ] > quantile(scimpute["GAPDH", ], 0.1)]
scimpute.norm <- normalize.gapdh(scimpute)
mult.factor <- sapply(1:16, function(x)
mean(fish.filt[, gene.names[genes[x]]], na.rm = TRUE)/mean(dropseq.filt[x, ]))
names(mult.factor) <- gene.names[genes]
mult.factor2 <- sapply(1:16, function(x)
mean(fish.filt[, gene.names[genes[x]]], na.rm = TRUE)/mean(saver.filt[x, ]))
names(mult.factor2) <- gene.names[genes]
mult.factor3 <- sapply(1:16, function(x)
mean(fish.filt[, gene.names[genes[x]]], na.rm = TRUE)/mean(magic.filt[x, ]))
names(mult.factor3) <- gene.names[genes]
mult.factor4 <- sapply(1:16, function(x)
mean(fish.filt[, gene.names[genes[x]]], na.rm = TRUE)/mean(scimpute.filt[x, ]))
names(mult.factor4) <- gene.names[genes]
set.seed(5)
lambda.samp <- sample.saver(saver)
lambda.samp.filt <-
lambda.samp[, lambda.samp["GAPDH", ] < quantile(lambda.samp["GAPDH", ], 0.9) &
lambda.samp["GAPDH", ] > quantile(lambda.samp["GAPDH", ], 0.1)]
lambda.samp.norm <-
sweep(lambda.samp.filt, 2,
lambda.samp.filt["GAPDH", ]/mean(lambda.samp.filt["GAPDH", ]), "/")
###############################################################################
## Gini analysis
library(reldist)
fish.gini <- apply(fish.norm[, gene.names[genes]], 2, function(x)
gini(x[complete.cases(x)]))
dropseq.gini <- apply(dropseq.norm, 1, gini)
saver.lambda.gini <- apply(lambda.samp.norm, 1, gini)
names(saver.lambda.gini) <- gene.names[genes]
magic.gini <- apply(magic.norm, 1, gini)
scimpute.gini <- apply(scimpute.norm, 1, gini)
pdf("plots/fig1b_gini.pdf", 7.5, 4.25)
par(mfrow = c(1, 2), cex.main = 1.5, mar = c(4, 4, 1, 2) + 0.1, oma = c(3, 2, 3, 0),
mgp = c(3.5, 1, 0),
cex.lab = 1.5, font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)
plot(0, type = "n", xlim = c(0, 1), ylim = c(0, 1), pch = 21, xlab = "",
ylab = "", bg = "#bdd7e7", cex = 1.5)
abline(0, 1, col = "gray", lty = 2)
points(fish.gini[-6], dropseq.gini[-6], pch = 21, bg = "#bdd7e7", cex = 3)
par(las = 0)
mtext("Drop-seq Gini", side = 2, line = 3.5, cex = 1.5)
mtext("FISH Gini", side = 1, line = 3, cex = 1.5)
points(fish.gini["LMNA"], dropseq.gini["LMNA"], pch = 21, cex = 3,
bg = "#a63603")
points(fish.gini["CCNA2"], dropseq.gini["CCNA2"], pch = 21, cex = 3,
bg = "#31a354")
text(0.42, 0.82, "LMNA", cex = 1.2)
text(0.72, 0.9, "CCNA2", cex = 1.2)
text(.7, .1, paste("RMSE = ", round(sqrt(mean((fish.gini[-6]-dropseq.gini[-6])^2)), 2)),
cex = 1.2)
text(0.78, 0.22, paste("r = ", round(cor(fish.gini[-6], dropseq.gini[-6]), 2)),
cex = 1.2)
par(las = 1)
plot(0, type = "n", xlim = c(0, 1), ylim = c(0, 1), pch = 21, xlab = "",
ylab = "", bg = "#9ecae1", cex = 1.5)
abline(0, 1, col = "gray", lty = 2)
points(fish.gini[-6], saver.lambda.gini[-6], pch = 21, bg = "#bdd7e7", cex = 3)
par(las = 0)
mtext("SAVER Gini", side = 2, line = 3.5, cex = 1.5)
mtext("FISH Gini", side = 1, line = 3, cex = 1.5)
points(fish.gini["LMNA"], saver.lambda.gini["LMNA"], pch = 21, cex = 3,
bg = "#a63603")
points(fish.gini["CCNA2"], saver.lambda.gini["CCNA2"], pch = 21, cex = 3,
bg = "#31a354")
text(0.13, 0.40, "LMNA", cex = 1.2)
text(0.76, 0.48, "CCNA2", cex = 1.2)
text(.7, .1, paste("RMSE = ", round(sqrt(mean((fish.gini[-6]-saver.lambda.gini[-6])^2)), 2)),
cex = 1.2)
text(0.8, 0.22, paste("r = ", round(cor(fish.gini[-6], saver.lambda.gini[-6]), 2)),
cex = 1.2)
dev.off()
### supplementary gini
pdf("plots/suppfig3a_gini.pdf", 8, 4.5)
par(mfrow = c(1, 2), cex.main = 1.5, mar = c(4, 4, 1, 2) + 0.1, oma = c(3, 2, 3, 0),
mgp = c(3.5, 1, 0),
cex.lab = 1.5, font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)
plot(0, type = "n", xlim = c(0, 1), ylim = c(0, 1), pch = 21, xlab = "",
ylab = "", bg = "#bdd7e7", cex = 1.5)
abline(0, 1, col = "gray", lty = 2)
points(fish.gini[-6], magic.gini[-6], pch = 21, bg = "#bdd7e7", cex = 3)
par(las = 0)
mtext("MAGIC Gini", side = 2, line = 3.5, cex = 1.5)
mtext("FISH Gini", side = 1, line = 3, cex = 1.5)
points(fish.gini["LMNA"], magic.gini["LMNA"], pch = 21, cex = 3,
bg = "#a63603")
points(fish.gini["CCNA2"], magic.gini["CCNA2"], pch = 21, cex = 3,
bg = "#31a354")
text(0.1, 0.18, "LMNA", cex = 1.2)
text(0.7, 0.07, "CCNA2", cex = 1.2)
text(.7, .8, paste("RMSE = ", round(sqrt(mean((fish.gini[-6]-magic.gini[-6])^2)), 2)),
cex = 1.2)
text(0.78, 0.92, paste("r = ", round(cor(fish.gini[-6], magic.gini[-6]), 2)),
cex = 1.2)
par(las = 1)
plot(0, type = "n", xlim = c(0, 1), ylim = c(0, 1), pch = 21, xlab = "",
ylab = "", bg = "#9ecae1", cex = 1.5)
abline(0, 1, col = "gray", lty = 2)
points(fish.gini[-6], scimpute.gini[-6], pch = 21, bg = "#bdd7e7", cex = 3)
par(las = 0)
mtext("scImpute Gini", side = 2, line = 3.5, cex = 1.5)
mtext("FISH Gini", side = 1, line = 3, cex = 1.5)
points(fish.gini["LMNA"], scimpute.gini["LMNA"], pch = 21, cex = 3,
bg = "#a63603")
points(fish.gini["CCNA2"], scimpute.gini["CCNA2"], pch = 21, cex = 3,
bg = "#31a354")
text(0.13, 0.40, "LMNA", cex = 1.2)
text(0.76, 0.54, "CCNA2", cex = 1.2)
text(.7, .1, paste("RMSE = ", round(sqrt(mean((fish.gini[-6]-scimpute.gini[-6])^2)), 2)),
cex = 1.2)
text(0.8, 0.22, paste("r = ", round(cor(fish.gini[-6], scimpute.gini[-6]), 2)),
cex = 1.2)
dev.off()
###############################################################################
## LMNA and CCNA2 density
set.seed(5)
Y.samp <- t(sapply(1:16, function(i)
rnbinom(n.cells, mu = saver$estimate[i, ]*mult.factor2[i],
size = saver$estimate[i, ]^2/saver$se[i, ]^2)))
rownames(Y.samp) <- gene.names[genes]
Y.samp.filt <- Y.samp[, Y.samp["GAPDH", ] < quantile(Y.samp["GAPDH", ], 0.9) &
Y.samp["GAPDH", ] > quantile(Y.samp["GAPDH", ], 0.1)]
Y.samp.norm <- sweep(Y.samp.filt, 2, Y.samp.filt["GAPDH", ]/
mean(Y.samp.filt["GAPDH", ]), "/")
pdf("plots/fig1c_dens.pdf", 4.75, 6.5)
par(mfrow = c(2, 1), cex.main = 1.5, mar = c(1, 2, 1.5, 0) + 0.1, oma = c(3, 2, 0, 2),
mgp = c(3.5, 1, 0),
cex.lab = 1.5, font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)
plot(0, type = "n", ylab = "", xlab = "", cex = 1.5, ylim = c(0, 0.04),
xlim = c(-10, 500), lwd = 2, pch = 5, axes = FALSE, main = "")
axis(1, seq(0, 500, by = 100))
axis(2, labels = FALSE, lwd.ticks = 0)
par(las = 0)
mtext("Density", side = 2, line = 1, cex = 1.5)
mtext("LMNA", side = 3, line = -2, cex = 1.5, font = 3)
dens.bw <- density(fish.norm[, "LMNA"], na.rm = TRUE)$bw
lines(density(fish.norm[, "LMNA"], na.rm = TRUE), lwd = 4, col = "#bdbdbd")
lines(density(dropseq.norm["LMNA", ]*mult.factor["LMNA"], bw = dens.bw), lwd = 3)
lines(density(Y.samp.norm["LMNA", ], bw = dens.bw), lwd = 3, lty = 2)
legend(250, 0.03, c("RNA FISH", "Drop-seq", "SAVER"), lty = c(1, 1, 2),
lwd = c(4, 3, 3), col = c("#bdbdbd", "black", "black"), box.lty = 0,
cex = 1.1)
plot(0, type = "n", ylab = "", xlab = "", cex = 1.5, ylim = c(0, 0.25),
xlim = c(-2, 60), lwd = 2, pch = 5, axes = FALSE, main = "")
axis(1, seq(0, 60, by = 20))
par(las = 1)
axis(2, labels = FALSE, lwd.ticks = 0)
par(las = 0)
mtext("Density", side = 2, line = 1, cex = 1.5)
mtext("CCNA2", side = 3, line = -2, cex = 1.5, font = 3)
dens.bw <- density(fish.norm[, "CCNA2"], na.rm = TRUE)$bw
lines(density(fish.norm[, "CCNA2"], na.rm = TRUE), lwd = 4, col = "#bdbdbd")
lines(density(dropseq.norm["CCNA2", ]*mult.factor["CCNA2"], bw = dens.bw), lwd = 3)
lines(density(Y.samp.norm["CCNA2", ], bw = dens.bw), lwd = 3, lty = 2)
mtext("Expression Count", side = 1, line = 3, cex = 1.5)
dev.off()
pdf("plots/suppfig3c_dens.pdf", 5.5, 8)
par(mfrow = c(2, 1), cex.main = 1.5, mar = c(3, 2, 4, 0) + 0.1, oma = c(3, 2, 0, 2),
mgp = c(3.5, 1, 0),
cex.lab = 1.5, font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)
plot(0, type = "n", ylab = "", xlab = "", cex = 1.5, ylim = c(0, 0.04),
xlim = c(-10, 500), lwd = 2, pch = 5, axes = FALSE, main = "")
axis(1, seq(0, 500, by = 100))
axis(2, labels = FALSE, lwd.ticks = 0)
par(las = 0)
mtext("Density", side = 2, line = 1, cex = 1.5)
mtext("LMNA", side = 3, line = 0.5, cex = 1.5, font = 3)
mtext("Expression Count", side = 1, line = 3, cex = 1.5)
dens.bw <- density(fish.norm[, "LMNA"], na.rm = TRUE)$bw
lines(density(fish.norm[, "LMNA"], na.rm = TRUE), lwd = 4, col = "#bdbdbd")
lines(density(magic.norm["LMNA", ]*mult.factor3["LMNA"], bw = dens.bw), lwd = 3)
lines(density(scimpute.norm["LMNA", ]*mult.factor4["LMNA"], bw = dens.bw), lwd = 3, lty = 2)
legend("topright", c("RNA FISH", "MAGIC", "scImpute"), lty = c(1, 1, 2),
lwd = c(4, 3, 3), col = c("#bdbdbd", "black", "black"), box.lty = 0,
cex = 1.1)
plot(0, type = "n", ylab = "", xlab = "", cex = 1.5, ylim = c(0, 0.25),
xlim = c(-2, 100), lwd = 2, pch = 5, axes = FALSE, main = "")
axis(1, seq(0, 100, by = 20))
par(las = 1)
axis(2, labels = FALSE, lwd.ticks = 0)
par(las = 0)
mtext("Density", side = 2, line = 1, cex = 1.5)
mtext("CCNA2", side = 3, line = 0, cex = 1.5, font = 3)
dens.bw <- density(fish.norm[, "CCNA2"], na.rm = TRUE)$bw
lines(density(fish.norm[, "CCNA2"], na.rm = TRUE), lwd = 4, col = "#bdbdbd")
lines(density(magic.norm["CCNA2", ]*mult.factor3["CCNA2"], bw = dens.bw), lwd = 3)
lines(density(scimpute.norm["CCNA2", ]*mult.factor4["CCNA2"], bw = dens.bw), lwd = 3, lty = 2)
mtext("Expression Count", side = 1, line = 3, cex = 1.5)
dev.off()
###############################################################################
## BABAM1 and LMNA correlation
library(reshape2)
adj.vec <- rep(0, 16)
for (i in 1:16) {
adj.vec[i] <-
sqrt(var(saver$estimate[i, ]*sf, na.rm = TRUE)/
(var(saver$estimate[i, ]*sf, na.rm = TRUE) +
mean(saver$se[i, ]^2*sf^2, na.rm = TRUE)))
}
scale.factor <- outer(adj.vec, adj.vec)
fish.cor <- cor(fish[, gene.names[genes]], use = "pairwise.complete.obs")
dropseq.cor <- cor(t(dropseq[genes, ]))
saver.cor <- cor(t(sweep(saver$estimate, 2, sf, "*")))*scale.factor
magic.cor <- cor(t(sweep(magic, 2, sf, "*")))
scimpute.cor <- cor(t(scimpute))
fish.cor[lower.tri(fish.cor, diag = TRUE)] <- NA
fish.cor.melt <- melt(fish.cor, na.rm = TRUE, value.name = "FISH")
dropseq.cor[lower.tri(dropseq.cor, diag = TRUE)] <- NA
dropseq.cor.melt <- melt(dropseq.cor, na.rm = TRUE, value.name = "Dropseq")
saver.cor[lower.tri(saver.cor, diag = TRUE)] <- NA
saver.cor.melt <- melt(saver.cor, na.rm= TRUE, value.name = "SAVER")
magic.cor[lower.tri(magic.cor, diag = TRUE)] <- NA
magic.cor.melt <- melt(magic.cor, na.rm= TRUE, value.name = "MAGIC")
scimpute.cor[lower.tri(scimpute.cor, diag = TRUE)] <- NA
scimpute.cor.melt <- melt(scimpute.cor, na.rm= TRUE, value.name = "scImpute")
unnorm.cor <- Reduce(function(x, y) merge(x, y, by = c("Var1", "Var2")),
list(fish.cor.melt, dropseq.cor.melt, saver.cor.melt,
magic.cor.melt, scimpute.cor.melt))
png("plots/fig1d_corplot.png", 8.5, 3.5, units = "in", res = 300)
par(mfrow = c(1, 3), cex.main = 1.5, mar = c(4, 4, 3, 0) + 0.1, oma = c(3, 4, 3, 2),
mgp = c(3.5, 1, 0),
cex.lab = 1.5, font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)
smoothScatter(fish[, "BABAM1"], fish[, "LMNA"], ylab = "", xlab = "", cex = 1.5,
lwd = 2, axes = FALSE, main = "", nrpoints = 0, bandwidth = c(1, 20))
axis(1)
axis(2)
par(las = 0)
mtext("LMNA", side = 2, line = 4.5, cex = 1.3, font = 3)
mtext("FISH", side = 3, cex = 1.3, line = 2)
text(52, 1150, paste("r = ", round(unnorm.cor[3, "FISH"], 2)),
cex = 1.8)
smoothScatter(dropseq["BABAM1", ], bandwidth = c(0.1, 0.2),
dropseq["LMNA", ], nrpoints = 0,
ylab = "", xlab = "", cex = 1.5,
lwd = 2, axes = FALSE, main = "")
axis(1)
par(las = 1)
axis(2)
mtext("Drop-seq", side = 3, cex = 1.3, line = 2)
mtext("BABAM1", side = 1, cex = 1.3, font = 3, line = 4)
text(2.4, 15.8, paste("r = ", round(unnorm.cor[3, "Dropseq"], 2)),
cex = 1.8)
library(scales)
babam1 <- matrix(0, ncol(saver$estimate), 5)
lmna <- matrix(0, ncol(saver$estimate), 5)
for (i in 1:ncol(saver$estimate)) {
babam1[i, ] <- rgamma(5, saver$estimate["BABAM1", i]^2/saver$se["BABAM1", i]^2,
saver$estimate["BABAM1", i]/saver$se["BABAM1", i]^2/sf[i])
lmna[i, ] <- rgamma(5, saver$estimate["LMNA", i]^2/saver$se["LMNA", i]^2,
saver$estimate["LMNA", i]/saver$se["LMNA", i]^2/sf[i])
}
smoothScatter(c(babam1), c(lmna), nrpoints = 0, bandwidth = c(0.08, 0.6), ylim = c(0, 15),
xlim = c(0, 2), ylab = "",
xlab = "", cex = 1.5,
lwd = 2, axes = FALSE, main = "")
points(saver$estimate["BABAM1", ]*sf, saver$estimate["LMNA", ]*sf, pch = 20, cex = 0.8)
axis(1)
par(las = 1)
axis(2)
mtext("SAVER", side = 3, cex = 1.3, line = 2)
text(1.6, 14.8, paste("r = ", round(unnorm.cor[3, "SAVER"], 2)),
cex = 1.8)
dev.off()
png("plots/suppfig3e_smooth.png", 8.5, 3.5, units = "in", res = 300)
par(mfrow = c(1, 3), cex.main = 1.5, mar = c(4, 4, 3, 0) + 0.1, oma = c(3, 4, 3, 2),
mgp = c(3.5, 1, 0),
cex.lab = 1.5, font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)
smoothScatter(fish[, "BABAM1"], fish[, "LMNA"], ylab = "", xlab = "", cex = 1.5,
lwd = 2, axes = FALSE, main = "", nrpoints = 0, bandwidth = c(1, 20))
axis(1)
axis(2)
par(las = 0)
mtext("LMNA", side = 2, line = 4.5, cex = 1.3, font = 3)
mtext("FISH", side = 3, cex = 1.3, line = 2)
text(52, 1150, paste("r = ", round(unnorm.cor[3, "FISH"], 2)),
cex = 1.8)
smoothScatter(magic["BABAM1", ]*sf, magic["LMNA", ]*sf, ylab = "", xlab = "", cex = 1.5,
lwd = 2, axes = FALSE, main = "", nrpoints = 0, xlim = c(0, 20),
ylim = c(0, 35))
axis(1)
axis(2)
par(las = 0)
mtext("MAGIC", side = 3, cex = 1.3, line = 2)
mtext("BABAM1", side = 1, cex = 1.3, font = 3, line = 4)
text(17, 34.3, paste("r = ", round(unnorm.cor[3, "MAGIC"], 2)),
cex = 1.8)
smoothScatter(scimpute["BABAM1", ], scimpute["LMNA", ], ylab = "", xlab = "", cex = 1.5,
lwd = 2, axes = FALSE, main = "", nrpoints = 0, bandwidth = c(0.05, 0.2))
axis(1)
axis(2)
par(las = 0)
mtext("scImpute", side = 3, cex = 1.3, line = 2)
text(2.4, 15.6, paste("r = ", round(unnorm.cor[3, "scImpute"], 2)),
cex = 1.8)
dev.off()
###############################################################################
## K-S
dropseq.ks <- sapply(1:16, function(i)
ks.test(dropseq.norm[i, ]*mult.factor[i],
fish.norm[, gene.names[genes[i]]])$statistic)
Y.ks <- sapply(1:16, function(i)
ks.test(Y.samp.norm[i, ], fish.norm[, gene.names[genes[i]]])$statistic)
magic.ks <- sapply(1:16, function(i) ks.test(magic.norm[i, ]*mult.factor3[i],
fish.norm[, gene.names[genes[i]]])$statistic)
scimpute.ks <- sapply(1:16, function(i) ks.test(scimpute.norm[i, ]*mult.factor4[i],
fish.norm[, gene.names[genes[i]]])$statistic)
ks <- cbind(dropseq.ks, Y.ks)[-6, ]
rownames(ks) <- gene.names[genes[c(1:5, 7:16)]]
pdf("plots/suppfig3b_ks.pdf", 7, 4.5)
par(cex.main = 1.5, mar = c(5, 6, 4, 2) + 0.1, mgp = c(3.5, 1, 0), cex.lab = 1.5,
font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)
x <- c(1.5, 2.5, 3.5, 4.5)
plot(x, c(-10, -10, -10, -10), type = "p", ylab = " ", xlab = " ", cex = 1.5,
ylim = c(0, 1), xlim = c(1, 5), lwd = 2, pch = 5, axes = FALSE, main = " ")
axis(1, at = c(1.5, 2.5, 3.5, 4.5), labels = c("Drop-seq", "MAGIC", "scImpute",
"SAVER"), cex.axis = 1.5)
par(las = 1)
axis(2, pos = 1.3)
par(las = 0)
mtext("Kolmogorov-Smirnov", side = 2, line = 0.5, cex = 1.5)
for (i in 1:length(dropseq.ks)) {
if (i == 6) next
lines(x, c(dropseq.ks[i], magic.ks[i], scimpute.ks[i], Y.ks[i]),
lwd = 1.5, type = "c")
}
points(rep(1.55, length(dropseq.ks)-1), dropseq.ks[-6], pch = 21,
bg = "#9ecae1", cex = 3)
points(rep(2.53, length(dropseq.ks)-1), magic.ks[-6], pch = 21,
bg = "#9ecae1", cex = 3)
points(rep(3.53, length(dropseq.ks)-1), scimpute.ks[-6], pch = 21,
bg = "#9ecae1", cex = 3)
points(rep(4.5, length(dropseq.ks)-1), Y.ks[-6], pch = 21,
bg = "#9ecae1", cex = 3)
dev.off()
###############################################################################
## Correlation
pdf("plots/suppfig3d_cor.pdf", 7.5, 4)
par(mfrow = c(2, 2), cex.main = 1.5, mar = c(0.5, 4, 0, 2) + 0.1, oma = c(4, 2, 1, 0),
mgp = c(3.5, 1, 0),
cex.lab = 1.5, font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)
plot(0, type = "n", ylab = "", xlab = "", cex = 1.5, ylim = c(-0.2, 1),
xlim = c(-0.2, 1), lwd = 2, pch = 5, axes = FALSE, main = "")
axis(1, labels = FALSE)
axis(2)
par(las = 0)
mtext("Drop-seq Cor", side = 2, line = 3.5, cex = 1.5)
abline(0, 1, lty = 2, col = "gray")
points(unnorm.cor[, "FISH"], unnorm.cor[, "Dropseq"], pch = 21, bg = "#9ecae1",
cex = 3)
points(unnorm.cor[unnorm.cor$Var1 == "BABAM1" & unnorm.cor$Var2 == "LMNA",
"FISH"],
unnorm.cor[unnorm.cor$Var1 == "BABAM1" & unnorm.cor$Var2 == "LMNA",
"Dropseq"], pch = 21, cex = 3,
bg = "#e6550d")
text(0.93, 0.21, "BABAM1", xpd = TRUE, cex = 1.2)
text(0.93, 0.13, "LMNA", xpd = TRUE, cex = 1.2)
plot(0, type = "n", ylab = "", xlab = "", cex = 1.5, ylim = c(-0.2, 1),
xlim = c(-0.2, 1), lwd = 2, pch = 5, axes = FALSE, main = "")
axis(1, labels = FALSE)
par(las = 1)
axis(2)
par(las = 0)
mtext("SAVER Cor", side = 2, cex = 1.5, line = 3.5)
abline(0, 1, lty = 2, col = "gray")
points(unnorm.cor[, "FISH"], unnorm.cor[, "SAVER"], pch = 21, bg = "#9ecae1",
cex = 3)
points(unnorm.cor[unnorm.cor$Var1 == "BABAM1" & unnorm.cor$Var2 == "LMNA",
"FISH"],
unnorm.cor[unnorm.cor$Var1 == "BABAM1" & unnorm.cor$Var2 == "LMNA",
"SAVER"], pch = 21, cex = 3,
bg = "#e6550d")
text(0.93, 0.74, "BABAM1", xpd = TRUE, cex = 1.2)
text(0.93, 0.66, "LMNA", xpd = TRUE, cex = 1.2)
plot(0, type = "n", ylab = "", xlab = "", cex = 1.5, ylim = c(-0.2, 1),
xlim = c(-0.2, 1), lwd = 2, pch = 5, axes = FALSE, main = "")
axis(1)
par(las = 1)
axis(2)
par(las = 0)
mtext("MAGIC Cor", side = 2, line = 3.5, cex = 1.5)
mtext("FISH Cor", side = 1, line = 3, cex = 1.5)
abline(0, 1, lty = 2, col = "gray")
points(unnorm.cor[, "FISH"], unnorm.cor[, "MAGIC"], pch = 21, bg = "#9ecae1",
cex = 3)
points(unnorm.cor[unnorm.cor$Var1 == "BABAM1" & unnorm.cor$Var2 == "LMNA",
"FISH"],
unnorm.cor[unnorm.cor$Var1 == "BABAM1" & unnorm.cor$Var2 == "LMNA",
"MAGIC"], pch = 21, cex = 3,
bg = "#e6550d")
text(0.93, 0.98, "BABAM1", xpd = TRUE, cex = 1.2)
text(0.93, 0.9, "LMNA", xpd = TRUE, cex = 1.2)
plot(0, type = "n", ylab = "", xlab = "", cex = 1.5, ylim = c(-0.2, 1),
xlim = c(-0.2, 1), lwd = 2, pch = 5, axes = FALSE, main = "")
axis(1)
par(las = 1)
axis(2)
par(las = 0)
mtext("scImpute Cor", side = 2, cex = 1.5, line = 3.5)
mtext("FISH Cor", side = 1, line = 3, cex = 1.5)
abline(0, 1, lty = 2, col = "gray")
points(unnorm.cor[, "FISH"], unnorm.cor[, "scImpute"], pch = 21, bg = "#9ecae1",
cex = 3)
points(unnorm.cor[unnorm.cor$Var1 == "BABAM1" & unnorm.cor$Var2 == "LMNA",
"FISH"],
unnorm.cor[unnorm.cor$Var1 == "BABAM1" & unnorm.cor$Var2 == "LMNA",
"scImpute"], pch = 21, cex = 3,
bg = "#e6550d")
text(0.90, 0.24, "BABAM1", xpd = TRUE, cex = 1.2)
text(0.90, 0.16, "LMNA", xpd = TRUE, cex = 1.2)
dev.off()