Gladstone-Bioinformatics-Wo.../intermediate-r-rna-seq/tbd.R
2020-07-21 13:47:22 -07:00

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R

cnts_casein <- cpm(y)[y$genes$Symbol == "Csn1s2b", ]
cnts_rndm <- cpm(y)[100, ]
df <- data.frame(Casein = cnts_casein,
Random = cnts_rndm,
targets[, c("CellType", "Status")])
p <- ggplot(df, aes(x=Random, y=1,
color=CellType, shape=Status)) +
geom_point(size=2.5) +
scale_x_log10() +
scale_y_log10() +
theme_classic() +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.line.y = element_blank(),
legend.position = "top",
legend.box = "vertical",
plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"))
ggsave("Random.png",
p,
width = 3,
height = 3)
p <- ggplot(df, aes(x=1, y=Random,
color=CellType, shape=Status)) +
geom_point(size=2.5) +
scale_x_log10() +
scale_y_log10() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
ggsave("Random.png",
p)
xlab(paste("PC1:", pc1_var, "% variance")) +
ylab(paste("PC2:", pc2_var, "% variance"))