library(ggplot2)
setwd("~/hydrogen/sorghum/mrna")
# express
express <- read.table("express_BS-1/results.xprs", sep="\t", header=T)
express <- express[,c(2,15)]
names(express) <- c("target_id", "express.tpm")
# rna-skim
skim <- read.table("rnaskim_BS-1/expression", sep="\t", header=F)
skim <- skim[,c(1,5)]
names(skim) <- c("target_id", "skim.tpm")
# merge
data <- merge(x=express, y=skim, by="target_id", all.x=F, all.y=F)
correlation <- cor(data[,c(2,3)])
head(correlation)
## express.tpm skim.tpm
## express.tpm 1.0000 0.9714
## skim.tpm 0.9714 1.0000
Scatter:
ggplot(data, aes(x=express.tpm, y=skim.tpm)) +
geom_point(shape=19,
size = 3,
alpha = I(0.4)) +
coord_fixed(ratio = 1, xlim=c(-1,1000), ylim=c(-1,1000))
Density:
library(reshape2)
data2 <- data[data$express.tpm>0.001,]
data2 <- data2[data2$skim.tpm>0.001,]
melt <- melt(data2, id = "target_id")
ggplot(data = melt, aes(x = log(value), colour = variable)) + geom_density()
filter <- data[data$skim.tpm>600,]
filter <- filter[filter$express.tpm<20,]
head(filter)
## target_id express.tpm skim.tpm
## 601 Sb01g004330.2 13.625 812.7
## 1077 Sb01g008420.2 6.446 643.8
## 7824 Sb02g032090.1 17.454 1282.8