library(MASS) library(RColorBrewer) rf <- colorRampPalette(rev(brewer.pal(11,'Spectral'))) r <- rf(32) # Default call k <- kde2d(log2(1+(all_m0[,33])),log2(1+(all_m0[,17]))) image(k, col=r) # Adjust binning (interpolate - can be computationally intensive for large datasets) a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] k <- kde2d(log2(1+(a$RPKM_KO_0h.y)),log2(1+(a$RPKM_KO_0h.x)),n=400,lims=c(0,7,0,7)) par(mfrow=c(2,1)) image(k, col=r) contour(k, add = TRUE,vfont = c("sans serif", "plain"),labcex=0.65,levels=c(0.01,0.03,0.05)) # from base graphics package k2 <- kde2d(log2(1+(a$RPKM_WT_0h.y)),log2(1+(a$RPKM_WT_0h.x)),n=400,lims=c(0,7,0,7)) image(k2, col=r) contour(k2, add = TRUE,vfont = c("sans serif", "plain"),labcex=0.65,levels=c(0.01,0.03,0.05)) # from base graphics package # Contour plot overlayed on heat map image of results #contour(k, add = TRUE) # from base graphics package a2=a[ ((log2(1+a$RPKM_WT_m0.y))>0.5)|((log2(1+a$RPKM_WT_m0.y))<4),] png("dens_m0_ko_rna_vs_poly.png",width=1024,height=1024,pointsize = 22) k <- kde2d(log2(1+(all_m0[,32])),log2(1+(all_m0[,16])),n=200,lims=c(0,13,0,13)) k2=k k2$z=log10(k$z+1e-3) image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") #contour(k2, add = TRUE,labcex=1) # from base graphics package contour(k2, add = TRUE,vfont = c("sans serif", "plain"),labcex=0.65) # from base graphics package dev.off() png("dens_m0_wt_rna_vs_poly.png",width=1024,height=1024,pointsize = 22) k <- kde2d(log2(1+(all_m0[,33])),log2(1+(all_m0[,17])),n=200,lims=c(0,13,0,13)) k2=k k2$z=log10(k$z+1e-3) image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") #contour(k2, add = TRUE,labcex=1) # from base graphics package contour(k2, add = TRUE,vfont = c("sans serif", "plain"),labcex=0.65) # from base graphics package dev.off() png("dens_m1_ko_rna_vs_poly.png",width=1024,height=1024,pointsize = 22) k <- kde2d(log2(1+(all_m1[,32])),log2(1+(all_m1[,16])),n=200,lims=c(0,13,0,13)) k2=k k2$z=log10(k$z+1e-3) image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") contour(k2, add = TRUE,vfont = c("sans serif", "bold"),labcex=0.65) # from base graphics package dev.off() png("dens_m1_wt_rna_vs_poly.png",width=1024,height=1024,pointsize = 22) k <- kde2d(log2(1+(all_m1[,33])),log2(1+(all_m1[,17])),n=200,lims=c(0,13,0,13)) k2=k k2$z=log10(k$z+1e-3) image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") contour(k2, add = TRUE,vfont = c("sans serif", "bold"),labcex=0.65) # from base graphics package dev.off() png("dens_m2_ko_rna_vs_poly.png",width=1024,height=1024,pointsize = 22) k <- kde2d(log2(1+(all_m2[,32])),log2(1+(all_m2[,16])),n=200,lims=c(0,13,0,13)) k2=k k2$z=log10(k$z+1e-3) image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") contour(k2, add = TRUE,vfont = c("sans serif", "bold"),labcex=0.65) # from base graphics package dev.off() png("dens_m2_wt_rna_vs_poly.png",width=1024,height=1024,pointsize = 22) k <- kde2d(log2(1+(all_m2[,33])),log2(1+(all_m2[,17])),n=200,lims=c(0,13,0,13)) k2=k k2$z=log10(k$z+1e-3) image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") contour(k2, add = TRUE,vfont = c("sans serif", "bold"),labcex=0.65) # from base graphics package dev.off() png("dens_2h_ko_rna_vs_poly.png",width=1024,height=1024,pointsize = 22) k <- kde2d(log2(1+(all_2h[,32])),log2(1+(all_2h[,16])),n=200,lims=c(0,13,0,13)) k2=k k2$z=log10(k$z+1e-3) image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") contour(k2, add = TRUE,vfont = c("sans serif", "bold"),labcex=0.65) # from base graphics package dev.off() png("dens_2h_wt_rna_vs_poly.png",width=1024,height=1024,pointsize = 22) k <- kde2d(log2(1+(all_2h[,33])),log2(1+(all_2h[,17])),n=200,lims=c(0,13,0,13)) k2=k k2$z=log10(k$z+1e-3) image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") contour(k2, add = TRUE,vfont = c("sans serif", "bold"),labcex=0.65) # from base graphics package dev.off() png("dens_6h_ko_rna_vs_poly.png",width=1024,height=1024,pointsize = 22) k <- kde2d(log2(1+(all_6h[,32])),log2(1+(all_6h[,16])),n=200,lims=c(0,13,0,13)) k2=k k2$z=log10(k$z+1e-3) image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") contour(k2, add = TRUE,vfont = c("sans serif", "bold"),labcex=0.65) # from base graphics package dev.off() png("dens_6h_wt_rna_vs_poly.png",width=1024,height=1024,pointsize = 22) k <- kde2d(log2(1+(all_6h[,33])),log2(1+(all_6h[,17])),n=200,lims=c(0,13,0,13)) k2=k k2$z=log10(k$z+1e-3) image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") #image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") contour(k2, add = TRUE,vfont = c("sans serif", "bold"),labcex=0.65) # from base graphics package dev.off() #@ diff of log2RPKM png("dens_m0_diff_ko_minus_wt_rna_vs_poly.png",width=1024,height=1024,pointsize = 22) k <- kde2d(log2(1+(all_m0[,32]))-log2(1+(all_m0[,33])),log2(1+(all_m0[,16]))-log2(1+(all_m0[,17])),n=200,lims=c(-3,3,-2,2)) k2=k k2$z=log10(k$z+1e-8) image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") contour(k2, add = TRUE,vfont = c("sans serif", "plain"),labcex=0.65) # from base graphics package dev.off() png("dens_m1_diff_ko_minus_wt_rna_vs_poly.png",width=1024,height=1024,pointsize = 22) k <- kde2d(log2(1+(all_m1[,32]))-log2(1+(all_m1[,33])),log2(1+(all_m1[,16]))-log2(1+(all_m1[,17])),n=200,lims=c(-3,3,-2,2)) k2=k k2$z=log10(k$z+1e-8) image(k2, col=r,xlab="log2(RPKM_abundance)",ylab="log2(RPKM_translation)") contour(k2, add = TRUE,vfont = c("sans serif", "plain"),labcex=0.65) # from base graphics package dev.off() #ENSMUSG00000022150 -0.516267828715218 7.12196903296183 0.246853512009843 0.775919770432084 Dab2 2776.27 101.463217537883 130.091033543044 53.5945686645799 79.472002337196 36.5465958058412 46.8582067100981 19.3045232144496 28.6254587403948 41.7024012579696 23.9649909774222 g=all_m0[all_m0$Row.names=="ENSMUSG00000022150",] print(c(log2(1+(g[,32]))-log2(1+(g[,33])),log2(1+(g[,16]))-log2(1+(g[,17])))) print(c(log2(1+(g[,32])),log2(1+(g[,16])),log2(1+(g[,33])),log2(1+(g[,17])) )) 5.416245 3.114211 4.641834 2.778624 g=all_m0[all_m0$geneID.x=="Fcgr2b",] 1.651245 5.497498 2.977216 5.716757 # change in cor, RPKMs < 17 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)<4)&((log2(1+a$RPKM_KO_0h.y))<4)&(log2(1+a$RPKM_WT_0h.x)<4)&((log2(1+a$RPKM_WT_0h.y))<4),] cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) Pearson's product-moment correlation data: log2(1 + a2$RPKM_WT_0h.x) and log2(1 + a2$RPKM_WT_0h.y) t = 0.50981, df = 8346, p-value = 0.6102 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.01587353 0.02702918 sample estimates: cor 0.005580392 > > Pearson's product-moment correlation data: log2(1 + a2$RPKM_KO_0h.x) and log2(1 + a2$RPKM_KO_0h.y) t = 0.51662, df = 8346, p-value = 0.6054 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.01579900 0.02710368 sample estimates: cor 0.005654941 #m1 a=all_m1[(!is.na(all_m1$RPKM_KO_m1.x))&(!is.na(all_m1$RPKM_KO_m1.y)),] a2=a[ (log2(1+a$RPKM_WT_m1.x)<4)&((log2(1+a$RPKM_WT_m1.y))<4)&(log2(1+a$RPKM_KO_m1.x)<4)&((log2(1+a$RPKM_KO_m1.y))<4),] cor.test(log2(1+a2$RPKM_WT_m1.x),log2(1+a2$RPKM_WT_m1.y)) cor.test(log2(1+a2$RPKM_KO_m1.x),log2(1+a2$RPKM_KO_m1.y)) Pearson's product-moment correlation data: log2(1 + a2$RPKM_WT_m1.x) and log2(1 + a2$RPKM_WT_m1.y) t = 0.34566, df = 8194, p-value = 0.7296 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.01783294 0.02546652 sample estimates: cor 0.003818581 Pearson's product-moment correlation data: log2(1 + a2$RPKM_KO_m1.x) and log2(1 + a2$RPKM_KO_m1.y) t = 0.021911, df = 8194, p-value = 0.9825 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.02140810 0.02189198 sample estimates: cor 0.0002420528 #m2 a=all_m2[(!is.na(all_m2$RPKM_KO_m2.x))&(!is.na(all_m2$RPKM_KO_m2.y)),] a2=a[ (log2(1+a$RPKM_WT_m2.x)<4)&((log2(1+a$RPKM_WT_m2.y))<4)&(log2(1+a$RPKM_KO_m2.x)<4)&((log2(1+a$RPKM_KO_m2.y))<4),] cor.test(log2(1+a2$RPKM_WT_m2.x),log2(1+a2$RPKM_WT_m2.y)) cor.test(log2(1+a2$RPKM_KO_m2.x),log2(1+a2$RPKM_KO_m2.y)) Pearson's product-moment correlation data: log2(1 + a2$RPKM_WT_m2.x) and log2(1 + a2$RPKM_WT_m2.y) t = 0.14771, df = 7808, p-value = 0.8826 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.02050772 0.02384942 sample estimates: cor 0.001671674 Pearson's product-moment correlation data: log2(1 + a2$RPKM_KO_m2.x) and log2(1 + a2$RPKM_KO_m2.y) t = 0.44245, df = 7808, p-value = 0.6582 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.01717340 0.02718275 sample estimates: cor 0.005007139 cor.diff.test = function(x1, x2, y1, y2, method="pearson") { cor1 = cor.test(x1, x2, method=method) cor2 = cor.test(y1, y2, method=method) r1 = cor1$estimate r2 = cor2$estimate n1 = sum(complete.cases(x1, x2)) n2 = sum(complete.cases(y1, y2)) fisher = ((0.5*log((1+r1)/(1-r1)))-(0.5*log((1+r2)/(1-r2))))/((1/(n1-3))+(1/(n2-3)))^0.5 p.value = (2*(1-pnorm(abs(fisher)))) result= list( "cor1" = list( "estimate" = as.numeric(cor1$estimate), "p.value" = cor1$p.value, "n" = n1 ), "cor2" = list( "estimate" = as.numeric(cor2$estimate), "p.value" = cor2$p.value, "n" = n2 ), "p.value.twosided" = as.numeric(p.value), "p.value.onesided" = as.numeric(p.value) / 2 ) cat(paste(sep="", "cor1: r=", format(result$cor1$estimate, digits=3), ", p=", format(result$cor1$p.value, digits=3), ", n=", result$cor1$n, "\n", "cor2: r=", format(result$cor2$estimate, digits=3), ", p=", format(result$cor2$p.value, digits=3), ", n=", result$cor2$n, "\n", "diffence: p(one-sided)=", format(result$p.value.onesided, digits=3), ", p(two-sided)=", format(result$p.value.twosided, digits=3), "\n" )) return(result); } cor.diff.test(log2(1+a2$RPKM_WT_m2.x),log2(1+a2$RPKM_WT_m2.y),log2(1+a2$RPKM_KO_m2.x),log2(1+a2$RPKM_KO_m2.y)) cor1: r=0.00167, p=0.883, n=7810 cor2: r=0.00501, p=0.658, n=7810 diffence: p(one-sided)=0.417, p(two-sided)=0.835 #@RPKMs >2 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>=1)&((log2(1+a$RPKM_KO_0h.y))>=1)&(log2(1+a$RPKM_WT_0h.x)>=1)&((log2(1+a$RPKM_WT_0h.y))>=1),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.187, p=0, n=7185 cor2: r=0.161, p=0, n=7185 diffence: p(one-sided)=0.0531, p(two-sided)=0.106 #m1 a=all_m1[(!is.na(all_m1$RPKM_KO_m1.x))&(!is.na(all_m1$RPKM_KO_m1.y)),] a2=a[ (log2(1+a$RPKM_WT_m1.x)>=1)&((log2(1+a$RPKM_WT_m1.y))>=1)&(log2(1+a$RPKM_KO_m1.x)>=1)&((log2(1+a$RPKM_KO_m1.y))>=1),] #cor.test(log2(1+a2$RPKM_WT_m1.x),log2(1+a2$RPKM_WT_m1.y)) #cor.test(log2(1+a2$RPKM_KO_m1.x),log2(1+a2$RPKM_KO_m1.y)) cor.diff.test(log2(1+a2$RPKM_WT_m1.x),log2(1+a2$RPKM_WT_m1.y),log2(1+a2$RPKM_KO_m1.x),log2(1+a2$RPKM_KO_m1.y)) cor1: r=0.14, p=0, n=6981 cor2: r=0.136, p=0, n=6981 diffence: p(one-sided)=0.393, p(two-sided)=0.786 #m2 a=all_m2[(!is.na(all_m2$RPKM_KO_m2.x))&(!is.na(all_m2$RPKM_KO_m2.y)),] a2=a[ (log2(1+a$RPKM_WT_m2.x)>=1)&((log2(1+a$RPKM_WT_m2.y))>=1)&(log2(1+a$RPKM_KO_m2.x)>=1)&((log2(1+a$RPKM_KO_m2.y))>=1),] #cor.test(log2(1+a2$RPKM_WT_m2.x),log2(1+a2$RPKM_WT_m2.y)) #cor.test(log2(1+a2$RPKM_KO_m2.x),log2(1+a2$RPKM_KO_m2.y)) cor.diff.test(log2(1+a2$RPKM_WT_m2.x),log2(1+a2$RPKM_WT_m2.y),log2(1+a2$RPKM_KO_m2.x),log2(1+a2$RPKM_KO_m2.y)) cor1: r=0.106, p=0, n=7289 cor2: r=0.131, p=0, n=7289 diffence: p(one-sided)=0.0649, p(two-sided)=0.13 #@RPKMs >2 & <65 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>=1)&((log2(1+a$RPKM_KO_0h.y))>=1)&(log2(1+a$RPKM_WT_0h.x)>=1)&((log2(1+a$RPKM_WT_0h.y))>=1)&(log2(1+a$RPKM_KO_0h.x)<=6)&((log2(1+a$RPKM_KO_0h.y))<=6)&(log2(1+a$RPKM_WT_0h.x)<=6)&((log2(1+a$RPKM_WT_0h.y))<=6),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.0686, p=2.62e-07, n=5617 cor2: r=0.0518, p=0.000102, n=5617 diffence: p(one-sided)=0.186, p(two-sided)=0.372 #@RPKMs >1 & <129 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>=1)&((log2(1+a$RPKM_KO_0h.y))>=1)&(log2(1+a$RPKM_WT_0h.x)>=1)&((log2(1+a$RPKM_WT_0h.y))>=1)&(log2(1+a$RPKM_KO_0h.x)<=7)&((log2(1+a$RPKM_KO_0h.y))<=7)&(log2(1+a$RPKM_WT_0h.x)<=7)&((log2(1+a$RPKM_WT_0h.y))<=7),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.0783, p=3.61e-10, n=6397 cor2: r=0.0577, p=3.94e-06, n=6397 diffence: p(one-sided)=0.121, p(two-sided)=0.241 #@RPKMs >2 & <129 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>1)&((log2(1+a$RPKM_KO_0h.y))>1)&(log2(1+a$RPKM_WT_0h.x)>1)&((log2(1+a$RPKM_WT_0h.y))>1)&(log2(1+a$RPKM_KO_0h.x)<=7)&((log2(1+a$RPKM_KO_0h.y))<=7)&(log2(1+a$RPKM_WT_0h.x)<=7)&((log2(1+a$RPKM_WT_0h.y))<=7),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.0783, p=3.61e-10, n=6397 cor2: r=0.0577, p=3.94e-06, n=6397 diffence: p(one-sided)=0.121, p(two-sided)=0.241 (same) #@RPKMs >2 & <33 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>1)&((log2(1+a$RPKM_KO_0h.y))>1)&(log2(1+a$RPKM_WT_0h.x)>1)&((log2(1+a$RPKM_WT_0h.y))>1)&(log2(1+a$RPKM_KO_0h.x)<=5)&((log2(1+a$RPKM_KO_0h.y))<=5)&(log2(1+a$RPKM_WT_0h.x)<=5)&((log2(1+a$RPKM_WT_0h.y))<=5),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.0447, p=0.00336, n=4299 cor2: r=0.0289, p=0.0583, n=4299 diffence: p(one-sided)=0.231, p(two-sided)=0.462 #@RPKMs >2 & <257 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>1)&((log2(1+a$RPKM_KO_0h.y))>1)&(log2(1+a$RPKM_WT_0h.x)>1)&((log2(1+a$RPKM_WT_0h.y))>1)&(log2(1+a$RPKM_KO_0h.x)<=8)&((log2(1+a$RPKM_KO_0h.y))<=8)&(log2(1+a$RPKM_WT_0h.x)<=8)&((log2(1+a$RPKM_WT_0h.y))<=8),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.111, p=0, n=6810 cor2: r=0.0804, p=3.02e-11, n=6810 diffence: p(one-sided)=0.0367, p(two-sided)=0.0733 #sig #m1 a=all_m1[(!is.na(all_m1$RPKM_KO_m1.x))&(!is.na(all_m1$RPKM_KO_m1.y)),] a2=a[ (log2(1+a$RPKM_KO_m1.x)>1)&((log2(1+a$RPKM_KO_m1.y))>1)&(log2(1+a$RPKM_WT_m1.x)>1)&((log2(1+a$RPKM_WT_m1.y))>1)&(log2(1+a$RPKM_KO_m1.x)<=8)&((log2(1+a$RPKM_KO_m1.y))<=8)&(log2(1+a$RPKM_WT_m1.x)<=8)&((log2(1+a$RPKM_WT_m1.y))<=8),] #cor.test(log2(1+a2$RPKM_WT_m1.x),log2(1+a2$RPKM_WT_m1.y)) #cor.test(log2(1+a2$RPKM_KO_m1.x),log2(1+a2$RPKM_KO_m1.y)) cor.diff.test(log2(1+a2$RPKM_WT_m1.x),log2(1+a2$RPKM_WT_m1.y),log2(1+a2$RPKM_KO_m1.x),log2(1+a2$RPKM_KO_m1.y)) cor1: r=0.0631, p=3.28e-07, n=6549 cor2: r=0.0546, p=9.98e-06, n=6549 diffence: p(one-sided)=0.313, p(two-sided)=0.626 #m2 a=all_m2[(!is.na(all_m2$RPKM_KO_m2.x))&(!is.na(all_m2$RPKM_KO_m2.y)),] a2=a[ (log2(1+a$RPKM_KO_m2.x)>1)&((log2(1+a$RPKM_KO_m2.y))>1)&(log2(1+a$RPKM_WT_m2.x)>1)&((log2(1+a$RPKM_WT_m2.y))>1)&(log2(1+a$RPKM_KO_m2.x)<=8)&((log2(1+a$RPKM_KO_m2.y))<=8)&(log2(1+a$RPKM_WT_m2.x)<=8)&((log2(1+a$RPKM_WT_m2.y))<=8),] #cor.test(log2(1+a2$RPKM_WT_m2.x),log2(1+a2$RPKM_WT_m2.y)) #cor.test(log2(1+a2$RPKM_KO_m2.x),log2(1+a2$RPKM_KO_m2.y)) cor.diff.test(log2(1+a2$RPKM_WT_m2.x),log2(1+a2$RPKM_WT_m2.y),log2(1+a2$RPKM_KO_m2.x),log2(1+a2$RPKM_KO_m2.y)) cor1: r=0.06, p=5.33e-07, n=6962 cor2: r=0.081, p=1.3e-11, n=6962 diffence: p(one-sided)=0.107, p(two-sided)=0.214 #@RPKMs >5 & <257 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>2)&((log2(1+a$RPKM_KO_0h.y))>2)&(log2(1+a$RPKM_WT_0h.x)>2)&((log2(1+a$RPKM_WT_0h.y))>2)&(log2(1+a$RPKM_KO_0h.x)<=8)&((log2(1+a$RPKM_KO_0h.y))<=8)&(log2(1+a$RPKM_WT_0h.x)<=8)&((log2(1+a$RPKM_WT_0h.y))<=8),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.119, p=1.14e-12, n=3548 cor2: r=0.0955, p=1.22e-08, n=3548 diffence: p(one-sided)=0.158, p(two-sided)=0.316 #m1 a=all_m1[(!is.na(all_m1$RPKM_KO_m1.x))&(!is.na(all_m1$RPKM_KO_m1.y)),] a2=a[ (log2(1+a$RPKM_KO_m1.x)>2)&((log2(1+a$RPKM_KO_m1.y))>2)&(log2(1+a$RPKM_WT_m1.x)>2)&((log2(1+a$RPKM_WT_m1.y))>2)&(log2(1+a$RPKM_KO_m1.x)<=8)&((log2(1+a$RPKM_KO_m1.y))<=8)&(log2(1+a$RPKM_WT_m1.x)<=8)&((log2(1+a$RPKM_WT_m1.y))<=8),] #cor.test(log2(1+a2$RPKM_WT_m1.x),log2(1+a2$RPKM_WT_m1.y)) #cor.test(log2(1+a2$RPKM_KO_m1.x),log2(1+a2$RPKM_KO_m1.y)) cor.diff.test(log2(1+a2$RPKM_WT_m1.x),log2(1+a2$RPKM_WT_m1.y),log2(1+a2$RPKM_KO_m1.x),log2(1+a2$RPKM_KO_m1.y)) cor1: r=0.0631, p=3.28e-07, n=6549 cor2: r=0.0546, p=9.98e-06, n=6549 diffence: p(one-sided)=0.313, p(two-sided)=0.626 #m2 a=all_m2[(!is.na(all_m2$RPKM_KO_m2.x))&(!is.na(all_m2$RPKM_KO_m2.y)),] a2=a[ (log2(1+a$RPKM_KO_m2.x)>2)&((log2(1+a$RPKM_KO_m2.y))>2)&(log2(1+a$RPKM_WT_m2.x)>2)&((log2(1+a$RPKM_WT_m2.y))>2)&(log2(1+a$RPKM_KO_m2.x)<=8)&((log2(1+a$RPKM_KO_m2.y))<=8)&(log2(1+a$RPKM_WT_m2.x)<=8)&((log2(1+a$RPKM_WT_m2.y))<=8),] #cor.test(log2(1+a2$RPKM_WT_m2.x),log2(1+a2$RPKM_WT_m2.y)) #cor.test(log2(1+a2$RPKM_KO_m2.x),log2(1+a2$RPKM_KO_m2.y)) cor.diff.test(log2(1+a2$RPKM_WT_m2.x),log2(1+a2$RPKM_WT_m2.y),log2(1+a2$RPKM_KO_m2.x),log2(1+a2$RPKM_KO_m2.y)) cor1: r=0.06, p=5.33e-07, n=6962 cor2: r=0.081, p=1.3e-11, n=6962 diffence: p(one-sided)=0.107, p(two-sided)=0.214 #@ RPKMs >3 & <1025 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>1)&((log2(1+a$RPKM_KO_0h.y))>1)&(log2(1+a$RPKM_WT_0h.x)>1)&((log2(1+a$RPKM_WT_0h.y))>1)&(log2(1+a$RPKM_KO_0h.x)<=10)&((log2(1+a$RPKM_KO_0h.y))<=10)&(log2(1+a$RPKM_WT_0h.x)<=10)&((log2(1+a$RPKM_WT_0h.y))<=10),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.168, p=0, n=7127 cor2: r=0.142, p=0, n=7127 diffence: p(one-sided)=0.0598, p(two-sided)=0.12 #@ RPKMs >3 & <513 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>1)&((log2(1+a$RPKM_KO_0h.y))>1)&(log2(1+a$RPKM_WT_0h.x)>1)&((log2(1+a$RPKM_WT_0h.y))>1)&(log2(1+a$RPKM_KO_0h.x)<=9)&((log2(1+a$RPKM_KO_0h.y))<=9)&(log2(1+a$RPKM_WT_0h.x)<=9)&((log2(1+a$RPKM_WT_0h.y))<=9),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.14, p=0, n=7025 cor2: r=0.113, p=0, n=7025 diffence: p(one-sided)=0.0539, p(two-sided)=0.108 #@RPKMs >2 & <257 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>1)&((log2(1+a$RPKM_KO_0h.y))>1)&(log2(1+a$RPKM_WT_0h.x)>1)&((log2(1+a$RPKM_WT_0h.y))>1)&(log2(1+a$RPKM_KO_0h.x)<=8)&((log2(1+a$RPKM_KO_0h.y))<=8)&(log2(1+a$RPKM_WT_0h.x)<=7)&((log2(1+a$RPKM_WT_0h.y))<=7),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.0851, p=6.25e-12, n=6506 cor2: r=0.0603, p=1.14e-06, n=6506 diffence: p(one-sided)=0.0775, p(two-sided)=0.155 #@RPKMs >2 & <257 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>1)&((log2(1+a$RPKM_KO_0h.y))>1)&(log2(1+a$RPKM_WT_0h.x)>1)&((log2(1+a$RPKM_WT_0h.y))>1)&(log2(1+a$RPKM_KO_0h.x)<=7)&((log2(1+a$RPKM_KO_0h.y))<=7)&(log2(1+a$RPKM_WT_0h.x)<=8)&((log2(1+a$RPKM_WT_0h.y))<=8),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.0916, p=1.65e-13, n=6461 cor2: r=0.0642, p=2.37e-07, n=6461 diffence: p(one-sided)=0.059, p(two-sided)=0.118 #@RPKMs >2 & <257 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>1)&((log2(1+a$RPKM_KO_0h.y))>1)&(log2(1+a$RPKM_WT_0h.x)>1)&((log2(1+a$RPKM_WT_0h.y))>1)&(log2(1+a$RPKM_KO_0h.x)<=8)&((log2(1+a$RPKM_KO_0h.y))<=8)&(log2(1+a$RPKM_WT_0h.x)<=9)&((log2(1+a$RPKM_WT_0h.y))<=9),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.113, p=0, n=6842 cor2: r=0.083, p=6.02e-12, n=6842 diffence: p(one-sided)=0.0371, p(two-sided)=0.0743 #@RPKMs >2 & <257 a=all_m0[(!is.na(all_m0$RPKM_KO_0h.x))&(!is.na(all_m0$RPKM_KO_0h.y)),] a2=a[ (log2(1+a$RPKM_KO_0h.x)>3)&((log2(1+a$RPKM_KO_0h.y))>3)&(log2(1+a$RPKM_WT_0h.x)>3)&((log2(1+a$RPKM_WT_0h.y))>3)&(log2(1+a$RPKM_KO_0h.x)<=8)&((log2(1+a$RPKM_KO_0h.y))<=8)&(log2(1+a$RPKM_WT_0h.x)<=8)&((log2(1+a$RPKM_WT_0h.y))<=8),] #cor.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y)) #cor.test(log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor.diff.test(log2(1+a2$RPKM_WT_0h.x),log2(1+a2$RPKM_WT_0h.y),log2(1+a2$RPKM_KO_0h.x),log2(1+a2$RPKM_KO_0h.y)) cor1: r=0.106, p=1.93e-05, n=1623 cor2: r=0.0762, p=0.00214, n=1623 diffence: p(one-sided)=0.197, p(two-sided)=0.394