| Veja se seria algo assim library(reshape2) library(ggplot2) library(dplyr) t <- read.delim('teste.txt') t1 <- t[,c(1,seq(2,ncol(t),2))] t2 <- t[,c(1,seq(3,ncol(t),2))] t1l <- melt(t1, idvar = 'gene') colnames(t1l)[3] <- 'exprs' t2l <- melt(t2, idvar = 'gene') colnames(t2l)[3] <- 'percentage' tl <- left_join(t1l, t2l, by='gene') ggplot(data = "" aes(x=gene, y=exprs)) + geom_point(aes(color=percentage)) + scale_color_gradient2(midpoint=mean(tl$percentage), low="blue", mid="white", high="red", space ="Lab" ) + theme_classic() |
gene BE_exp BE_perc CE_exp CE_perc E_exp E_perc F_exp F_perc HE_exp HE_perc L_exp L_perc LE_exp LE_perc N_exp N_perc SM_exp SM_perc ACTB 4.006 99.8 3.690 99.6 2.691 95.2 2.377 80.1 2.901 97.7 3.182 94.1 2.864 94.0 3.243 96.0 3.214 93.5 ACTG1 3.850 5.9 3.592 7.1 2.469 1.5 2.252 1.1 3.129 6.4 2.451 0.6 2.979 1.1 3.104 0 2.500 2.4 AJUBA 1.185 5.9 0.972 7.1 1.042 1.5 0.912 1.1 1.043 6.4 0.865 0.6 1.024 1.1 0 0 1.149 2.4
daniel
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