Figure 2. Weight effect of lung P value
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.2.4
library(reshape)
library(knitr)
library(xtable)
merged.mouse.eQTL.min.variance2<-read.table(file="2016-02-22-merged.mouse.eQTL.min.variance.txt", header=T)
merged.mouse.eQTL.min.variance2$abs_liver.beta<-abs(merged.mouse.eQTL.min.variance2$liver.beta)
merged.mouse.eQTL.min.variance2$abs_lung.beta<-abs(merged.mouse.eQTL.min.variance2$lung.beta)
merged.mouse.eQTL.min.variance2$neg_log_lung_pvalue<--log(merged.mouse.eQTL.min.variance2$lung_pvalue)
merged.mouse.eQTL<-merged.mouse.eQTL.min.variance2
markers<-merged.mouse.eQTL[, 1]
# Yg=Ag + Bg*Xsnp+V
betas.hat<-abs(merged.mouse.eQTL[,2])
se<-(merged.mouse.eQTL[,3])
liver_residual_variance<-merged.mouse.eQTL[,4]
Z<-as.matrix(merged.mouse.eQTL[,10])
Z<-replace(Z,is.na(Z),0)
Z<-data.frame(1,Z)
Z<-as.matrix(Z)
rowLength<-length(markers)
lmsummary<-summary(lm(abs_liver.beta~-1+Z, data=merged.mouse.eQTL))
tau<-lmsummary$sigma**2
tau
## [1] 0.009699749
gamma<-as.matrix(lmsummary$coefficients[,1])
Z_transpose<-t(Z)
identity<-diag(nrow=rowLength)
betas.hat<-as.matrix(betas.hat)
V <- matrix(0, rowLength, rowLength)
diag(V) <-liver_residual_variance
Tau<- diag(tau, rowLength, rowLength)
s<-V + Tau
diag.inverse <- function(x){diag(1/diag(x), nrow(x), ncol(x))}
diag.multi <- function(x,y){diag(diag(x)*diag(y), nrow(x), ncol(x))}
S <-diag.inverse(s)
omega<-diag.multi(S, V)
omega.diag<-diag(omega )
# betas.thea<- S %*% Z %*% gamma + (identity-S) %*% betas.hat
betas.tieda<- omega %*% Z %*% gamma + (identity-omega) %*% betas.hat
# crbetas.tieda<- cromega %*% Z %*% gamma + (identity-cromega) %*% betas.hat
regbeta <-Z %*% gamma
liver.mouse.eQTL.bayesian.4tau <- read.table(file="2016-03-15_liver.mouse.eQTL.bayesian.4tau.txt", head=TRUE)
liver.mouse.eQTL.bayesian.4tau$fzm <- -log10(exp(-liver.mouse.eQTL.bayesian.4tau$neg_log_lung_pvalue))
range(liver.mouse.eQTL.bayesian.4tau$fzm)
## [1] 8.678947e-16 1.727677e+01
liver.mouse.eQTL.bayesian.4tau$ratio_fzm <- max(liver.mouse.eQTL.bayesian.4tau$fzm)/liver.mouse.eQTL.bayesian.4tau$fzm
range(liver.mouse.eQTL.bayesian.4tau$ratio_fzm)
## [1] 1.000000e+00 1.990653e+16
rho.optimization <- matrix(0, nrow=nrow(liver.mouse.eQTL.bayesian.4tau), ncol=7)
colnames(rho.optimization)<-c("rho","tmm","tau", "omega","beta_tieda", "n.betas.tieda.se","p.below.0" )
nomega.diag<-diag(nomega )
rho <- seq(1,51, by=10)*tau
rho
result <- NULL
for (i in 1:length(rho)) {
rho.optimization[ ,1] <- rho[i]
rho.optimization[ ,2] <- (rho[i]/tau)^liver.mouse.eQTL.bayesian.4tau$ratio_fzm
rho.optimization[ ,3] <-tau*((rho[i]/tau)^liver.mouse.eQTL.bayesian.4tau$ratio_fzm)
nTau<- diag(rho.optimization[i,3], rowLength, rowLength)
ns<-V + nTau
nS <- diag.inverse(ns)
nomega<-diag.multi(nS, V)
rho.optimization[ ,4] <- diag(nomega )
rho.optimization[ ,5] <-nomega %*% Z %*% gamma + (identity-nomega) %*% betas.hat
nTau_invert<-diag.inverse(nTau)
V_invert<-diag.inverse(V)
nPS_invert<-nTau_invert+ diag.multi(diag.multi(V_invert, Z), Z_transpose)
nPS<-diag.inverse(nPS_invert)
nps<-diag(nPS)
nps.long <- melt(nps)
rho.optimization[ ,6] <-(nps.long$value)^0.5
rho.optimization[ ,7] <- pnorm(0, rho.optimization[ ,5], rho.optimization[ ,6])
result <- rbind(result,rho.optimization)
}
result$p.below.0 <- pnorm(0, rho.optimization[ ,5], rho.optimization[ ,6])
write.table(result, file="2016-03-31_liver.mouse.eQTL.bayesian.result.txt",col.names=TRUE,row.names=FALSE,quote=FALSE)
liver.mouse.eQTL.bayesian.result <- read.table(file="2016-03-31_liver.mouse.eQTL.bayesian.result.txt", header=T)
knitr::kable(head(liver.mouse.eQTL.bayesian.result))
| 0.0096997 |
1 |
0.0096997 |
0.6609725 |
0.0335649 |
0.0800704 |
0.3375376 |
| 0.0096997 |
1 |
0.0096997 |
0.2791983 |
0.0236274 |
0.0983765 |
0.4050981 |
| 0.0096997 |
1 |
0.0096997 |
0.3255088 |
0.0140638 |
0.0561903 |
0.4011821 |
| 0.0096997 |
1 |
0.0096997 |
0.1586587 |
0.0416051 |
0.0982601 |
0.3359952 |
| 0.0096997 |
1 |
0.0096997 |
0.8895190 |
0.0401108 |
0.0928876 |
0.3329360 |
| 0.0096997 |
1 |
0.0096997 |
0.3335208 |
0.0306453 |
0.0984015 |
0.3777363 |
knitr::kable(tail(liver.mouse.eQTL.bayesian.result))
| 66049 |
0.4946872 |
3.505540e+40 |
3.400286e+38 |
0 |
0.0620446 |
4.9723544 |
0.4950222 |
| 66050 |
0.4946872 |
2.253892e+17 |
2.186218e+15 |
0 |
0.0301131 |
0.0815362 |
0.3559438 |
| 66051 |
0.4946872 |
3.460209e+72 |
3.356316e+70 |
0 |
0.0094306 |
2.5394473 |
0.4985185 |
| 66052 |
0.4946872 |
1.956586e+51 |
1.897839e+49 |
0 |
0.0393708 |
0.1246289 |
0.3760376 |
| 66053 |
0.4946872 |
1.315110e+25 |
1.275623e+23 |
0 |
0.0078469 |
3.5286804 |
0.4991129 |
| 66054 |
0.4946872 |
7.056647e+25 |
6.844771e+23 |
0 |
0.0437639 |
0.1516059 |
0.3864174 |
result.df <-liver.mouse.eQTL.bayesian.result
result.df$rho.class <- factor(result.df$rho/tau)
a <-liver.mouse.eQTL.bayesian.4tau[, c(1:2, 6, 7, 9)]
a <-rbind(a, a, a, a, a,a)
dim(a)
## [1] 66054 5
new.result.df<-cbind(a, result.df)
knitr::kable(head(new.result.df))
| ENSMUSG00000000001 |
0.0159861 |
0.0189108 |
0.5377718 |
2.0960974 |
0.0096997 |
1 |
0.0096997 |
0.6609725 |
0.0335649 |
0.0800704 |
0.3375376 |
1 |
| ENSMUSG00000000003 |
0.0168807 |
0.0037571 |
0.1929225 |
2.7653413 |
0.0096997 |
1 |
0.0096997 |
0.2791983 |
0.0236274 |
0.0983765 |
0.4050981 |
1 |
| ENSMUSG00000000037 |
0.0038651 |
0.0046811 |
0.7788138 |
1.1814994 |
0.0096997 |
1 |
0.0096997 |
0.3255088 |
0.0140638 |
0.0561903 |
0.4011821 |
1 |
| ENSMUSG00000000049 |
0.0422333 |
0.0018292 |
0.0000091 |
0.2321777 |
0.0096997 |
1 |
0.0096997 |
0.1586587 |
0.0416051 |
0.0982601 |
0.3359952 |
1 |
| ENSMUSG00000000056 |
0.0636041 |
0.0780959 |
0.2269175 |
1.5218109 |
0.0096997 |
1 |
0.0096997 |
0.8895190 |
0.0401108 |
0.0928876 |
0.3329360 |
1 |
| ENSMUSG00000000058 |
0.0256111 |
0.0048540 |
0.0584692 |
1.2292565 |
0.0096997 |
1 |
0.0096997 |
0.3335208 |
0.0306453 |
0.0984015 |
0.3777363 |
1 |
knitr::kable(tail(new.result.df))
| 66049 |
ENSMUSG00000099041 |
0.0620446 |
0.0215887 |
0.0286190 |
1.675414 |
0.4946872 |
3.505540e+40 |
3.400286e+38 |
0 |
0.0620446 |
4.9723544 |
0.4950222 |
51 |
| 66050 |
ENSMUSG00000099083 |
0.0301131 |
0.0066482 |
0.0547402 |
3.914568 |
0.4946872 |
2.253892e+17 |
2.186218e+15 |
0 |
0.0301131 |
0.0815362 |
0.3559438 |
51 |
| 66051 |
ENSMUSG00000099116 |
0.0094306 |
0.0056309 |
0.5055630 |
0.936450 |
0.4946872 |
3.460209e+72 |
3.356316e+70 |
0 |
0.0094306 |
2.5394473 |
0.4985185 |
51 |
| 66052 |
ENSMUSG00000099164 |
0.0393708 |
0.0155324 |
0.1065382 |
1.324376 |
0.4946872 |
1.956586e+51 |
1.897839e+49 |
0 |
0.0393708 |
0.1246289 |
0.3760376 |
51 |
| 66053 |
ENSMUSG00000099262 |
0.0078469 |
0.0108725 |
0.6941870 |
2.704301 |
0.4946872 |
1.315110e+25 |
1.275623e+23 |
0 |
0.0078469 |
3.5286804 |
0.4991129 |
51 |
| 66054 |
ENSMUSG00000099305 |
0.0437639 |
0.0229843 |
0.1325735 |
2.627966 |
0.4946872 |
7.056647e+25 |
6.844771e+23 |
0 |
0.0437639 |
0.1516059 |
0.3864174 |
51 |
knitr::kable(head(liver.mouse.eQTL.bayesian.4tau))
| ENSMUSG00000000001 |
0.0159861 |
0.0256247 |
0.0335649 |
0.0800559 |
0.0189108 |
0.5377718 |
0.0326900 |
2.0960974 |
0.3375098 |
0.0328346 |
0.0277012 |
0.0161111 |
0.9103235 |
18.97871 |
| ENSMUSG00000000003 |
0.0168807 |
0.0126533 |
0.0236274 |
0.0520236 |
0.0037571 |
0.1929225 |
0.0295496 |
2.7653413 |
0.3248544 |
0.0230580 |
0.0201492 |
0.0169033 |
1.2009724 |
14.38565 |
| ENSMUSG00000000037 |
0.0038651 |
0.0136293 |
0.0140638 |
0.0561845 |
0.0046811 |
0.7788138 |
0.0175948 |
1.1814994 |
0.4011720 |
0.0132541 |
0.0089752 |
0.0039017 |
0.5131187 |
33.67012 |
| ENSMUSG00000000049 |
0.0422333 |
0.0078085 |
0.0416051 |
0.0392200 |
0.0018292 |
0.0000091 |
0.0238839 |
0.2321777 |
0.1443878 |
0.0416661 |
0.0419531 |
0.0422315 |
0.1008335 |
171.33960 |
| ENSMUSG00000000056 |
0.0636041 |
0.0514812 |
0.0401108 |
0.0928852 |
0.0780959 |
0.2269175 |
0.0216750 |
1.5218109 |
0.3329319 |
0.0404380 |
0.0434054 |
0.0630989 |
0.6609141 |
26.14072 |
| ENSMUSG00000000058 |
0.0256111 |
0.0129823 |
0.0306453 |
0.0568619 |
0.0048540 |
0.0584692 |
0.0288547 |
1.2292565 |
0.2949631 |
0.0302500 |
0.0281486 |
0.0256294 |
0.5338593 |
32.36202 |
new.result.df2 <- new.result.df
by(new.result.df2, new.result.df2[, "rho.class"], head)
## new.result.df2[, "rho.class"]: 1
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 1 ENSMUSG00000000001 0.015986111 0.018910763 5.377718e-01
## 2 ENSMUSG00000000003 0.016880682 0.003757142 1.929225e-01
## 3 ENSMUSG00000000037 0.003865079 0.004681090 7.788138e-01
## 4 ENSMUSG00000000049 0.042233333 0.001829162 9.084753e-06
## 5 ENSMUSG00000000056 0.063604072 0.078095912 2.269175e-01
## 6 ENSMUSG00000000058 0.025611111 0.004853968 5.846922e-02
## neg_log_lung_pvalue rho tmm tau omega beta_tieda
## 1 2.0960974 0.009699749 1 0.009699749 0.6609725 0.03356494
## 2 2.7653413 0.009699749 1 0.009699749 0.2791983 0.02362737
## 3 1.1814994 0.009699749 1 0.009699749 0.3255088 0.01406380
## 4 0.2321777 0.009699749 1 0.009699749 0.1586587 0.04160507
## 5 1.5218109 0.009699749 1 0.009699749 0.8895190 0.04011077
## 6 1.2292565 0.009699749 1 0.009699749 0.3335208 0.03064532
## n.betas.tieda.se p.below.0 rho.class
## 1 0.08007039 0.3375376 1
## 2 0.09837648 0.4050981 1
## 3 0.05619033 0.4011821 1
## 4 0.09826008 0.3359952 1
## 5 0.09288763 0.3329360 1
## 6 0.09840149 0.3777363 1
## --------------------------------------------------------
## new.result.df2[, "rho.class"]: 11
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 11010 ENSMUSG00000000001 0.015986111 0.018910763 5.377718e-01
## 11011 ENSMUSG00000000003 0.016880682 0.003757142 1.929225e-01
## 11012 ENSMUSG00000000037 0.003865079 0.004681090 7.788138e-01
## 11013 ENSMUSG00000000049 0.042233333 0.001829162 9.084753e-06
## 11014 ENSMUSG00000000056 0.063604072 0.078095912 2.269175e-01
## 11015 ENSMUSG00000000058 0.025611111 0.004853968 5.846922e-02
## neg_log_lung_pvalue rho tmm tau
## 11010 2.0960974 0.1066972 5.811545e+19 5.637053e+17
## 11011 2.7653413 0.1066972 9.574358e+14 9.286887e+12
## 11012 1.1814994 0.1066972 1.158295e+35 1.123517e+33
## 11013 0.2321777 0.1066972 2.702759e+178 2.621608e+176
## 11014 1.5218109 0.1066972 1.670136e+27 1.619991e+25
## 11015 1.2292565 0.1066972 5.030091e+33 4.879062e+31
## omega beta_tieda n.betas.tieda.se p.below.0 rho.class
## 11010 2.036286e-15 0.015986111 0.13751641 0.4537277 11
## 11011 4.045642e-16 0.016880682 2.07432714 0.4967535 11
## 11012 5.040537e-16 0.003865079 0.06841849 0.4774750 11
## 11013 1.969618e-16 0.042233333 1.44735324 0.4883606 11
## 11014 8.409267e-15 0.063604072 0.27945646 0.4099789 11
## 11015 5.226690e-16 0.025611111 2.35774553 0.4956666 11
## --------------------------------------------------------
## new.result.df2[, "rho.class"]: 21
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 22019 ENSMUSG00000000001 0.015986111 0.018910763 5.377718e-01
## 22020 ENSMUSG00000000003 0.016880682 0.003757142 1.929225e-01
## 22021 ENSMUSG00000000037 0.003865079 0.004681090 7.788138e-01
## 22022 ENSMUSG00000000049 0.042233333 0.001829162 9.084753e-06
## 22023 ENSMUSG00000000056 0.063604072 0.078095912 2.269175e-01
## 22024 ENSMUSG00000000058 0.025611111 0.004853968 5.846922e-02
## neg_log_lung_pvalue rho tmm tau
## 22019 2.0960974 0.2036947 1.241706e+25 1.204424e+23
## 22020 2.7653413 0.2036947 1.049501e+19 1.017989e+17
## 22021 1.1814994 0.2036947 3.305866e+44 3.206608e+42
## 22022 0.2321777 0.2036947 3.536116e+226 3.429944e+224
## 22023 1.5218109 0.2036947 3.662360e+34 3.552397e+32
## 22024 1.2292565 0.2036947 6.161600e+42 5.976597e+40
## omega beta_tieda n.betas.tieda.se p.below.0 rho.class
## 22019 5.897436e-45 0.015986111 0.13751641 0.4537277 21
## 22020 1.171687e-45 0.016880682 2.07432714 0.4967535 21
## 22021 1.459826e-45 0.003865079 0.06841849 0.4774750 21
## 22022 5.704352e-46 0.042233333 1.44735324 0.4883606 21
## 22023 2.435468e-44 0.063604072 0.27945646 0.4099789 21
## 22024 1.513739e-45 0.025611111 2.35774553 0.4956666 21
## --------------------------------------------------------
## new.result.df2[, "rho.class"]: 31
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 33028 ENSMUSG00000000001 0.015986111 0.018910763 5.377718e-01
## 33029 ENSMUSG00000000003 0.016880682 0.003757142 1.929225e-01
## 33030 ENSMUSG00000000037 0.003865079 0.004681090 7.788138e-01
## 33031 ENSMUSG00000000049 0.042233333 0.001829162 9.084753e-06
## 33032 ENSMUSG00000000056 0.063604072 0.078095912 2.269175e-01
## 33033 ENSMUSG00000000058 0.025611111 0.004853968 5.846922e-02
## neg_log_lung_pvalue rho tmm tau
## 33028 2.0960974 0.3006922 2.014298e+28 1.953819e+26
## 33029 2.7653413 0.3006922 2.845810e+21 2.760364e+19
## 33030 1.1814994 0.3006922 1.638063e+50 1.588880e+48
## 33031 0.2321777 0.3006922 3.383123e+255 3.281544e+253
## 33032 1.5218109 0.3006922 9.666398e+38 9.376164e+36
## 33033 1.2292565 0.3006922 1.834357e+48 1.779281e+46
## omega beta_tieda n.betas.tieda.se p.below.0 rho.class
## 33028 5.762763e-256 0.015986111 0.13751641 0.4537277 31
## 33029 1.144931e-256 0.016880682 2.07432714 0.4967535 31
## 33030 1.426490e-256 0.003865079 0.06841849 0.4774750 31
## 33031 5.574089e-257 0.042233333 1.44735324 0.4883606 31
## 33032 2.379852e-255 0.063604072 0.27945646 0.4099789 31
## 33033 1.479172e-256 0.025611111 2.35774553 0.4956666 31
## --------------------------------------------------------
## new.result.df2[, "rho.class"]: 41
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 44037 ENSMUSG00000000001 0.015986111 0.018910763 5.377718e-01
## 44038 ENSMUSG00000000003 0.016880682 0.003757142 1.929225e-01
## 44039 ENSMUSG00000000037 0.003865079 0.004681090 7.788138e-01
## 44040 ENSMUSG00000000049 0.042233333 0.001829162 9.084753e-06
## 44041 ENSMUSG00000000056 0.063604072 0.078095912 2.269175e-01
## 44042 ENSMUSG00000000058 0.025611111 0.004853968 5.846922e-02
## neg_log_lung_pvalue rho tmm tau
## 44037 2.0960974 0.3976897 4.060318e+30 3.938406e+28
## 44038 2.7653413 0.3976897 1.588336e+23 1.540647e+21
## 44039 1.1814994 0.3976897 2.007380e+54 1.947108e+52
## 44040 0.2321777 0.3976897 2.156466e+276 2.091718e+274
## 44041 1.5218109 0.3976897 1.443202e+42 1.399870e+40
## 44042 1.2292565 0.3976897 1.559377e+52 1.512556e+50
## omega beta_tieda n.betas.tieda.se p.below.0 rho.class
## 44037 1.350894e-42 0.015986111 0.13751641 0.4537277 41
## 44038 2.683922e-43 0.016880682 2.07432714 0.4967535 41
## 44039 3.343946e-43 0.003865079 0.06841849 0.4774750 41
## 44040 1.306666e-43 0.042233333 1.44735324 0.4883606 41
## 44041 5.578798e-42 0.063604072 0.27945646 0.4099789 41
## 44042 3.467443e-43 0.025611111 2.35774553 0.4956666 41
## --------------------------------------------------------
## new.result.df2[, "rho.class"]: 51
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 55046 ENSMUSG00000000001 0.015986111 0.018910763 5.377718e-01
## 55047 ENSMUSG00000000003 0.016880682 0.003757142 1.929225e-01
## 55048 ENSMUSG00000000037 0.003865079 0.004681090 7.788138e-01
## 55049 ENSMUSG00000000049 0.042233333 0.001829162 9.084753e-06
## 55050 ENSMUSG00000000056 0.063604072 0.078095912 2.269175e-01
## 55051 ENSMUSG00000000058 0.025611111 0.004853968 5.846922e-02
## neg_log_lung_pvalue rho tmm tau
## 55046 2.0960974 0.4946872 2.555537e+32 2.478807e+30
## 55047 2.7653413 0.4946872 3.668641e+24 3.558490e+22
## 55048 1.1814994 0.4946872 3.119583e+57 3.025918e+55
## 55049 0.2321777 0.4946872 3.753124e+292 3.640436e+290
## 55050 1.5218109 0.4946872 4.336202e+44 4.206008e+42
## 55051 1.2292565 0.4946872 1.821496e+55 1.766805e+53
## omega beta_tieda n.betas.tieda.se p.below.0 rho.class
## 55046 1.070336e-55 0.015986111 0.13751641 0.4537277 51
## 55047 2.126517e-56 0.016880682 2.07432714 0.4967535 51
## 55048 2.649465e-56 0.003865079 0.06841849 0.4774750 51
## 55049 1.035293e-56 0.042233333 1.44735324 0.4883606 51
## 55050 4.420176e-55 0.063604072 0.27945646 0.4099789 51
## 55051 2.747313e-56 0.025611111 2.35774553 0.4956666 51
by(new.result.df2, new.result.df2[, "rho.class"], summary)
## new.result.df2[, "rho.class"]: 1
## ensembl_id betas.hat liver_residual_variance
## ENSMUSG00000000001: 1 Min. :0.00000 Min. :0.000975
## ENSMUSG00000000003: 1 1st Qu.:0.01186 1st Qu.:0.005551
## ENSMUSG00000000037: 1 Median :0.02430 Median :0.010731
## ENSMUSG00000000049: 1 Mean :0.05436 Mean :0.029009
## ENSMUSG00000000056: 1 3rd Qu.:0.05360 3rd Qu.:0.023547
## ENSMUSG00000000058: 1 Max. :2.76340 Max. :4.004622
## (Other) :11003
## liver_pvalue neg_log_lung_pvalue rho tmm
## Min. :0.00000 Min. : 0.0000 Min. :0.0097 Min. :1
## 1st Qu.:0.03867 1st Qu.: 0.8062 1st Qu.:0.0097 1st Qu.:1
## Median :0.21634 Median : 1.6936 Median :0.0097 Median :1
## Mean :0.29881 Mean : 2.9708 Mean :0.0097 Mean :1
## 3rd Qu.:0.50673 3rd Qu.: 3.5428 3rd Qu.:0.0097 3rd Qu.:1
## Max. :1.00000 Max. :39.7812 Max. :0.0097 Max. :1
##
## tau omega beta_tieda n.betas.tieda.se
## Min. :0.0097 Min. :0.09131 Min. :0.004582 Min. :0.02976
## 1st Qu.:0.0097 1st Qu.:0.36400 1st Qu.:0.024829 1st Qu.:0.07093
## Median :0.0097 Median :0.52523 Median :0.035162 Median :0.09819
## Mean :0.0097 Mean :0.54385 Mean :0.045850 Mean :0.08474
## 3rd Qu.:0.0097 3rd Qu.:0.70825 3rd Qu.:0.051346 3rd Qu.:0.09845
## Max. :0.0097 Max. :0.99758 Max. :1.223416 Max. :0.09849
##
## p.below.0 rho.class
## Min. :0.0000 1 :11009
## 1st Qu.:0.2707 11: 0
## Median :0.3346 21: 0
## Mean :0.3117 31: 0
## 3rd Qu.:0.3794 41: 0
## Max. :0.4779 51: 0
##
## --------------------------------------------------------
## new.result.df2[, "rho.class"]: 11
## ensembl_id betas.hat liver_residual_variance
## ENSMUSG00000000001: 1 Min. :0.00000 Min. :0.000975
## ENSMUSG00000000003: 1 1st Qu.:0.01186 1st Qu.:0.005551
## ENSMUSG00000000037: 1 Median :0.02430 Median :0.010731
## ENSMUSG00000000049: 1 Mean :0.05436 Mean :0.029009
## ENSMUSG00000000056: 1 3rd Qu.:0.05360 3rd Qu.:0.023547
## ENSMUSG00000000058: 1 Max. :2.76340 Max. :4.004622
## (Other) :11003
## liver_pvalue neg_log_lung_pvalue rho
## Min. :0.00000 Min. : 0.0000 Min. :0.1067
## 1st Qu.:0.03867 1st Qu.: 0.8062 1st Qu.:0.1067
## Median :0.21634 Median : 1.6936 Median :0.1067
## Mean :0.29881 Mean : 2.9708 Mean :0.1067
## 3rd Qu.:0.50673 3rd Qu.: 3.5428 3rd Qu.:0.1067
## Max. :1.00000 Max. :39.7812 Max. :0.1067
##
## tmm tau omega
## Min. :1.100e+01 Min. :0.000e+00 Min. :1.050e-16
## 1st Qu.:4.938e+11 1st Qu.:4.790e+09 1st Qu.:5.978e-16
## Median :2.892e+24 Median :2.805e+22 Median :1.155e-15
## Mean : Inf Mean : Inf Mean :3.124e-15
## 3rd Qu.:2.434e+51 3rd Qu.:2.361e+49 3rd Qu.:2.536e-15
## Max. : Inf Max. : Inf Max. :4.312e-13
##
## beta_tieda n.betas.tieda.se p.below.0 rho.class
## Min. :0.00000 Min. : 0.03122 Min. :0.0000 1 : 0
## 1st Qu.:0.01186 1st Qu.: 0.10225 1st Qu.:0.4062 11:11009
## Median :0.02430 Median : 1.26303 Median :0.4865 21: 0
## Mean :0.05436 Mean : 2.35767 Mean :0.4358 31: 0
## 3rd Qu.:0.05360 3rd Qu.: 3.54499 3rd Qu.:0.4973 41: 0
## Max. :2.76340 Max. :46.84959 Max. :0.5000 51: 0
##
## --------------------------------------------------------
## new.result.df2[, "rho.class"]: 21
## ensembl_id betas.hat liver_residual_variance
## ENSMUSG00000000001: 1 Min. :0.00000 Min. :0.000975
## ENSMUSG00000000003: 1 1st Qu.:0.01186 1st Qu.:0.005551
## ENSMUSG00000000037: 1 Median :0.02430 Median :0.010731
## ENSMUSG00000000049: 1 Mean :0.05436 Mean :0.029009
## ENSMUSG00000000056: 1 3rd Qu.:0.05360 3rd Qu.:0.023547
## ENSMUSG00000000058: 1 Max. :2.76340 Max. :4.004622
## (Other) :11003
## liver_pvalue neg_log_lung_pvalue rho
## Min. :0.00000 Min. : 0.0000 Min. :0.2037
## 1st Qu.:0.03867 1st Qu.: 0.8062 1st Qu.:0.2037
## Median :0.21634 Median : 1.6936 Median :0.2037
## Mean :0.29881 Mean : 2.9708 Mean :0.2037
## 3rd Qu.:0.50673 3rd Qu.: 3.5428 3rd Qu.:0.2037
## Max. :1.00000 Max. :39.7812 Max. :0.2037
##
## tmm tau omega
## Min. :2.100e+01 Min. :0.000e+00 Min. :3.040e-46
## 1st Qu.:7.029e+14 1st Qu.:6.818e+12 1st Qu.:1.731e-45
## Median :1.142e+31 Median :1.107e+29 Median :3.346e-45
## Mean : Inf Mean : Inf Mean :9.047e-45
## 3rd Qu.:1.752e+65 3rd Qu.:1.699e+63 3rd Qu.:7.343e-45
## Max. : Inf Max. : Inf Max. :1.249e-42
##
## beta_tieda n.betas.tieda.se p.below.0 rho.class
## Min. :0.00000 Min. : 0.03122 Min. :0.0000 1 : 0
## 1st Qu.:0.01186 1st Qu.: 0.10225 1st Qu.:0.4062 11: 0
## Median :0.02430 Median : 1.26303 Median :0.4865 21:11009
## Mean :0.05436 Mean : 2.35767 Mean :0.4358 31: 0
## 3rd Qu.:0.05360 3rd Qu.: 3.54499 3rd Qu.:0.4973 41: 0
## Max. :2.76340 Max. :46.84959 Max. :0.5000 51: 0
##
## --------------------------------------------------------
## new.result.df2[, "rho.class"]: 31
## ensembl_id betas.hat liver_residual_variance
## ENSMUSG00000000001: 1 Min. :0.00000 Min. :0.000975
## ENSMUSG00000000003: 1 1st Qu.:0.01186 1st Qu.:0.005551
## ENSMUSG00000000037: 1 Median :0.02430 Median :0.010731
## ENSMUSG00000000049: 1 Mean :0.05436 Mean :0.029009
## ENSMUSG00000000056: 1 3rd Qu.:0.05360 3rd Qu.:0.023547
## ENSMUSG00000000058: 1 Max. :2.76340 Max. :4.004622
## (Other) :11003
## liver_pvalue neg_log_lung_pvalue rho
## Min. :0.00000 Min. : 0.0000 Min. :0.3007
## 1st Qu.:0.03867 1st Qu.: 0.8062 1st Qu.:0.3007
## Median :0.21634 Median : 1.6936 Median :0.3007
## Mean :0.29881 Mean : 2.9708 Mean :0.3007
## 3rd Qu.:0.50673 3rd Qu.: 3.5428 3rd Qu.:0.3007
## Max. :1.00000 Max. :39.7812 Max. :0.3007
##
## tmm tau omega
## Min. :3.100e+01 Min. :0.000e+00 Min. :2.970e-257
## 1st Qu.:5.574e+16 1st Qu.:5.406e+14 1st Qu.:1.692e-256
## Median :1.073e+35 Median :1.041e+33 Median :3.270e-256
## Mean : Inf Mean : Inf Mean :8.840e-256
## 3rd Qu.:3.887e+73 3rd Qu.:3.771e+71 3rd Qu.:7.176e-256
## Max. : Inf Max. : Inf Max. :1.220e-253
##
## beta_tieda n.betas.tieda.se p.below.0 rho.class
## Min. :0.00000 Min. : 0.03122 Min. :0.0000 1 : 0
## 1st Qu.:0.01186 1st Qu.: 0.10225 1st Qu.:0.4062 11: 0
## Median :0.02430 Median : 1.26303 Median :0.4865 21: 0
## Mean :0.05436 Mean : 2.35767 Mean :0.4358 31:11009
## 3rd Qu.:0.05360 3rd Qu.: 3.54499 3rd Qu.:0.4973 41: 0
## Max. :2.76340 Max. :46.84959 Max. :0.5000 51: 0
##
## --------------------------------------------------------
## new.result.df2[, "rho.class"]: 41
## ensembl_id betas.hat liver_residual_variance
## ENSMUSG00000000001: 1 Min. :0.00000 Min. :0.000975
## ENSMUSG00000000003: 1 1st Qu.:0.01186 1st Qu.:0.005551
## ENSMUSG00000000037: 1 Median :0.02430 Median :0.010731
## ENSMUSG00000000049: 1 Mean :0.05436 Mean :0.029009
## ENSMUSG00000000056: 1 3rd Qu.:0.05360 3rd Qu.:0.023547
## ENSMUSG00000000058: 1 Max. :2.76340 Max. :4.004622
## (Other) :11003
## liver_pvalue neg_log_lung_pvalue rho
## Min. :0.00000 Min. : 0.0000 Min. :0.3977
## 1st Qu.:0.03867 1st Qu.: 0.8062 1st Qu.:0.3977
## Median :0.21634 Median : 1.6936 Median :0.3977
## Mean :0.29881 Mean : 2.9708 Mean :0.3977
## 3rd Qu.:0.50673 3rd Qu.: 3.5428 3rd Qu.:0.3977
## Max. :1.00000 Max. :39.7812 Max. :0.3977
##
## tmm tau omega
## Min. :4.100e+01 Min. :0.000e+00 Min. :6.963e-44
## 1st Qu.:1.287e+18 1st Qu.:1.248e+16 1st Qu.:3.966e-43
## Median :7.631e+37 Median :7.402e+35 Median :7.665e-43
## Mean : Inf Mean : Inf Mean :2.072e-42
## 3rd Qu.:3.811e+79 3rd Qu.:3.697e+77 3rd Qu.:1.682e-42
## Max. : Inf Max. : Inf Max. :2.861e-40
##
## beta_tieda n.betas.tieda.se p.below.0 rho.class
## Min. :0.00000 Min. : 0.03122 Min. :0.0000 1 : 0
## 1st Qu.:0.01186 1st Qu.: 0.10225 1st Qu.:0.4062 11: 0
## Median :0.02430 Median : 1.26303 Median :0.4865 21: 0
## Mean :0.05436 Mean : 2.35767 Mean :0.4358 31: 0
## 3rd Qu.:0.05360 3rd Qu.: 3.54499 3rd Qu.:0.4973 41:11009
## Max. :2.76340 Max. :46.84959 Max. :0.5000 51: 0
##
## --------------------------------------------------------
## new.result.df2[, "rho.class"]: 51
## ensembl_id betas.hat liver_residual_variance
## ENSMUSG00000000001: 1 Min. :0.00000 Min. :0.000975
## ENSMUSG00000000003: 1 1st Qu.:0.01186 1st Qu.:0.005551
## ENSMUSG00000000037: 1 Median :0.02430 Median :0.010731
## ENSMUSG00000000049: 1 Mean :0.05436 Mean :0.029009
## ENSMUSG00000000056: 1 3rd Qu.:0.05360 3rd Qu.:0.023547
## ENSMUSG00000000058: 1 Max. :2.76340 Max. :4.004622
## (Other) :11003
## liver_pvalue neg_log_lung_pvalue rho
## Min. :0.00000 Min. : 0.0000 Min. :0.4947
## 1st Qu.:0.03867 1st Qu.: 0.8062 1st Qu.:0.4947
## Median :0.21634 Median : 1.6936 Median :0.4947
## Mean :0.29881 Mean : 2.9708 Mean :0.4947
## 3rd Qu.:0.50673 3rd Qu.: 3.5428 3rd Qu.:0.4947
## Max. :1.00000 Max. :39.7812 Max. :0.4947
##
## tmm tau omega
## Min. :5.100e+01 Min. :0.000e+00 Min. :5.517e-57
## 1st Qu.:1.492e+19 1st Qu.:1.448e+17 1st Qu.:3.142e-56
## Median :1.285e+40 Median :1.247e+38 Median :6.074e-56
## Mean : Inf Mean : Inf Mean :1.642e-55
## 3rd Qu.:1.812e+84 3rd Qu.:1.758e+82 3rd Qu.:1.333e-55
## Max. : Inf Max. : Inf Max. :2.267e-53
##
## beta_tieda n.betas.tieda.se p.below.0 rho.class
## Min. :0.00000 Min. : 0.03122 Min. :0.0000 1 : 0
## 1st Qu.:0.01186 1st Qu.: 0.10225 1st Qu.:0.4062 11: 0
## Median :0.02430 Median : 1.26303 Median :0.4865 21: 0
## Mean :0.05436 Mean : 2.35767 Mean :0.4358 31: 0
## 3rd Qu.:0.05360 3rd Qu.: 3.54499 3rd Qu.:0.4973 41: 0
## Max. :2.76340 Max. :46.84959 Max. :0.5000 51:11009
##
sorted.new.result.df2 <- new.result.df2[order(new.result.df2$rho.class, new.result.df2$neg_log_lung_pvalue),]
by(sorted.new.result.df2[c(1, 2,5, 10)], sorted.new.result.df2[, "rho.class"], tail)
## sorted.new.result.df2[, "rho.class"]: 1
## ensembl_id betas.hat neg_log_lung_pvalue beta_tieda
## 4868 ENSMUSG00000028656 1.064626984 29.46768 0.7888386
## 8589 ENSMUSG00000044827 0.043061086 29.63010 0.1112840
## 8658 ENSMUSG00000045594 0.272539683 29.80771 0.1810885
## 1614 ENSMUSG00000020022 0.581488038 30.48351 0.2558550
## 993 ENSMUSG00000011884 0.003957014 32.50629 0.1357632
## 8387 ENSMUSG00000042684 0.058696429 39.78123 0.1519445
## --------------------------------------------------------
## sorted.new.result.df2[, "rho.class"]: 11
## ensembl_id betas.hat neg_log_lung_pvalue beta_tieda
## 15877 ENSMUSG00000028656 1.064626984 29.46768 1.064626984
## 19598 ENSMUSG00000044827 0.043061086 29.63010 0.043061086
## 19667 ENSMUSG00000045594 0.272539683 29.80771 0.272539683
## 12623 ENSMUSG00000020022 0.581488038 30.48351 0.581488038
## 12002 ENSMUSG00000011884 0.003957014 32.50629 0.003957014
## 19396 ENSMUSG00000042684 0.058696429 39.78123 0.058696429
## --------------------------------------------------------
## sorted.new.result.df2[, "rho.class"]: 21
## ensembl_id betas.hat neg_log_lung_pvalue beta_tieda
## 26886 ENSMUSG00000028656 1.064626984 29.46768 1.064626984
## 30607 ENSMUSG00000044827 0.043061086 29.63010 0.043061086
## 30676 ENSMUSG00000045594 0.272539683 29.80771 0.272539683
## 23632 ENSMUSG00000020022 0.581488038 30.48351 0.581488038
## 23011 ENSMUSG00000011884 0.003957014 32.50629 0.003957014
## 30405 ENSMUSG00000042684 0.058696429 39.78123 0.058696429
## --------------------------------------------------------
## sorted.new.result.df2[, "rho.class"]: 31
## ensembl_id betas.hat neg_log_lung_pvalue beta_tieda
## 37895 ENSMUSG00000028656 1.064626984 29.46768 1.064626984
## 41616 ENSMUSG00000044827 0.043061086 29.63010 0.043061086
## 41685 ENSMUSG00000045594 0.272539683 29.80771 0.272539683
## 34641 ENSMUSG00000020022 0.581488038 30.48351 0.581488038
## 34020 ENSMUSG00000011884 0.003957014 32.50629 0.003957014
## 41414 ENSMUSG00000042684 0.058696429 39.78123 0.058696429
## --------------------------------------------------------
## sorted.new.result.df2[, "rho.class"]: 41
## ensembl_id betas.hat neg_log_lung_pvalue beta_tieda
## 48904 ENSMUSG00000028656 1.064626984 29.46768 1.064626984
## 52625 ENSMUSG00000044827 0.043061086 29.63010 0.043061086
## 52694 ENSMUSG00000045594 0.272539683 29.80771 0.272539683
## 45650 ENSMUSG00000020022 0.581488038 30.48351 0.581488038
## 45029 ENSMUSG00000011884 0.003957014 32.50629 0.003957014
## 52423 ENSMUSG00000042684 0.058696429 39.78123 0.058696429
## --------------------------------------------------------
## sorted.new.result.df2[, "rho.class"]: 51
## ensembl_id betas.hat neg_log_lung_pvalue beta_tieda
## 59913 ENSMUSG00000028656 1.064626984 29.46768 1.064626984
## 63634 ENSMUSG00000044827 0.043061086 29.63010 0.043061086
## 63703 ENSMUSG00000045594 0.272539683 29.80771 0.272539683
## 56659 ENSMUSG00000020022 0.581488038 30.48351 0.581488038
## 56038 ENSMUSG00000011884 0.003957014 32.50629 0.003957014
## 63432 ENSMUSG00000042684 0.058696429 39.78123 0.058696429
by(sorted.new.result.df2, sorted.new.result.df2[, "rho.class"], tail)
## sorted.new.result.df2[, "rho.class"]: 1
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 4868 ENSMUSG00000028656 1.064626984 0.018407266 4.552986e-26
## 8589 ENSMUSG00000044827 0.043061086 0.003157578 2.733728e-04
## 8658 ENSMUSG00000045594 0.272539683 0.030338790 1.481601e-08
## 1614 ENSMUSG00000020022 0.581488038 0.054747720 1.767689e-13
## 993 ENSMUSG00000011884 0.003957014 0.021513047 8.846174e-01
## 8387 ENSMUSG00000042684 0.058696429 0.004868026 8.328277e-05
## neg_log_lung_pvalue rho tmm tau omega beta_tieda
## 4868 29.46768 0.009699749 1 0.009699749 0.6548994 0.7888386
## 8589 29.63010 0.009699749 1 0.009699749 0.2455859 0.1112840
## 8658 29.80771 0.009699749 1 0.009699749 0.7577397 0.1810885
## 1614 30.48351 0.009699749 1 0.009699749 0.8494937 0.2558550
## 993 32.50629 0.009699749 1 0.009699749 0.6892381 0.1357632
## 8387 39.78123 0.009699749 1 0.009699749 0.3341640 0.1519445
## n.betas.tieda.se p.below.0 rho.class
## 4868 0.09846466 5.671021e-16 1
## 8589 0.04880698 1.130136e-02 1
## 8658 0.09847356 3.296088e-02 1
## 1614 0.09847969 4.687769e-03 1
## 993 0.08176452 4.841523e-02 1
## 8387 0.05693248 3.805517e-03 1
## --------------------------------------------------------
## sorted.new.result.df2[, "rho.class"]: 11
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 15877 ENSMUSG00000028656 1.064626984 0.018407266 4.552986e-26
## 19598 ENSMUSG00000044827 0.043061086 0.003157578 2.733728e-04
## 19667 ENSMUSG00000045594 0.272539683 0.030338790 1.481601e-08
## 12623 ENSMUSG00000020022 0.581488038 0.054747720 1.767689e-13
## 12002 ENSMUSG00000011884 0.003957014 0.021513047 8.846174e-01
## 19396 ENSMUSG00000042684 0.058696429 0.004868026 8.328277e-05
## neg_log_lung_pvalue rho tmm tau omega
## 15877 29.46768 0.1066972 25.46099 0.2469652 1.982071e-15
## 19598 29.63010 0.1066972 25.01316 0.2426214 3.400039e-16
## 19667 29.80771 0.1066972 24.53792 0.2380117 3.266842e-15
## 12623 30.48351 0.1066972 22.85735 0.2217106 5.895164e-15
## 12002 32.50629 0.1066972 18.81296 0.1824810 2.316497e-15
## 19396 39.78123 0.1066972 11.00000 0.1066972 5.241827e-16
## beta_tieda n.betas.tieda.se p.below.0 rho.class
## 15877 1.064626984 4.59137869 0.4083175 11
## 19598 0.043061086 0.05619233 0.2217441 11
## 19667 0.272539683 5.89450845 0.4815610 11
## 12623 0.581488038 7.91829147 0.4707296 11
## 12002 0.003957014 0.14667327 0.4892385 11
## 19396 0.058696429 0.06977124 0.2000984 11
## --------------------------------------------------------
## sorted.new.result.df2[, "rho.class"]: 21
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 26886 ENSMUSG00000028656 1.064626984 0.018407266 4.552986e-26
## 30607 ENSMUSG00000044827 0.043061086 0.003157578 2.733728e-04
## 30676 ENSMUSG00000045594 0.272539683 0.030338790 1.481601e-08
## 23632 ENSMUSG00000020022 0.581488038 0.054747720 1.767689e-13
## 23011 ENSMUSG00000011884 0.003957014 0.021513047 8.846174e-01
## 30405 ENSMUSG00000042684 0.058696429 0.004868026 8.328277e-05
## neg_log_lung_pvalue rho tmm tau omega
## 26886 29.46768 0.2036947 60.95228 0.5912218 5.740417e-45
## 30607 29.63010 0.2036947 59.59434 0.5780502 9.847099e-46
## 30676 29.80771 0.2036947 58.16044 0.5641417 9.461335e-45
## 23632 30.48351 0.2036947 53.15044 0.5155460 1.707341e-44
## 23011 32.50629 0.2036947 41.50812 0.4026183 6.708974e-45
## 30405 39.78123 0.2036947 21.00000 0.2036947 1.518123e-45
## beta_tieda n.betas.tieda.se p.below.0 rho.class
## 26886 1.064626984 4.59137869 0.4083175 21
## 30607 0.043061086 0.05619233 0.2217441 21
## 30676 0.272539683 5.89450845 0.4815610 21
## 23632 0.581488038 7.91829147 0.4707296 21
## 23011 0.003957014 0.14667327 0.4892385 21
## 30405 0.058696429 0.06977124 0.2000984 21
## --------------------------------------------------------
## sorted.new.result.df2[, "rho.class"]: 31
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 37895 ENSMUSG00000028656 1.064626984 0.018407266 4.552986e-26
## 41616 ENSMUSG00000044827 0.043061086 0.003157578 2.733728e-04
## 41685 ENSMUSG00000045594 0.272539683 0.030338790 1.481601e-08
## 34641 ENSMUSG00000020022 0.581488038 0.054747720 1.767689e-13
## 34020 ENSMUSG00000011884 0.003957014 0.021513047 8.846174e-01
## 41414 ENSMUSG00000042684 0.058696429 0.004868026 8.328277e-05
## neg_log_lung_pvalue rho tmm tau omega
## 37895 29.46768 0.3006922 103.11726 1.0002116 5.609331e-256
## 41616 29.63010 0.3006922 100.52979 0.9751138 9.622233e-257
## 41685 29.80771 0.3006922 97.80574 0.9486912 9.245278e-256
## 34641 30.48351 0.3006922 88.35662 0.8570370 1.668352e-255
## 34020 32.50629 0.3006922 66.85435 0.6484704 6.555769e-256
## 41414 39.78123 0.3006922 31.00000 0.3006922 1.483456e-256
## beta_tieda n.betas.tieda.se p.below.0 rho.class
## 37895 1.064626984 4.59137869 0.4083175 31
## 41616 0.043061086 0.05619233 0.2217441 31
## 41685 0.272539683 5.89450845 0.4815610 31
## 34641 0.581488038 7.91829147 0.4707296 31
## 34020 0.003957014 0.14667327 0.4892385 31
## 41414 0.058696429 0.06977124 0.2000984 31
## --------------------------------------------------------
## sorted.new.result.df2[, "rho.class"]: 41
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 48904 ENSMUSG00000028656 1.064626984 0.018407266 4.552986e-26
## 52625 ENSMUSG00000044827 0.043061086 0.003157578 2.733728e-04
## 52694 ENSMUSG00000045594 0.272539683 0.030338790 1.481601e-08
## 45650 ENSMUSG00000020022 0.581488038 0.054747720 1.767689e-13
## 45029 ENSMUSG00000011884 0.003957014 0.021513047 8.846174e-01
## 52423 ENSMUSG00000042684 0.058696429 0.004868026 8.328277e-05
## neg_log_lung_pvalue rho tmm tau omega
## 48904 29.46768 0.3976897 150.40100 1.4588520 1.314927e-42
## 52625 29.63010 0.3976897 146.32401 1.4193062 2.255623e-43
## 52694 29.80771 0.3976897 142.04103 1.3777623 2.167258e-42
## 45650 30.48351 0.3976897 127.26119 1.2344017 3.910915e-42
## 45029 32.50629 0.3976897 94.12959 0.9130335 1.536789e-42
## 52423 39.78123 0.3976897 41.00000 0.3976897 3.477484e-43
## beta_tieda n.betas.tieda.se p.below.0 rho.class
## 48904 1.064626984 4.59137869 0.4083175 41
## 52625 0.043061086 0.05619233 0.2217441 41
## 52694 0.272539683 5.89450845 0.4815610 41
## 45650 0.581488038 7.91829147 0.4707296 41
## 45029 0.003957014 0.14667327 0.4892385 41
## 52423 0.058696429 0.06977124 0.2000984 41
## --------------------------------------------------------
## sorted.new.result.df2[, "rho.class"]: 51
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 59913 ENSMUSG00000028656 1.064626984 0.018407266 4.552986e-26
## 63634 ENSMUSG00000044827 0.043061086 0.003157578 2.733728e-04
## 63703 ENSMUSG00000045594 0.272539683 0.030338790 1.481601e-08
## 56659 ENSMUSG00000020022 0.581488038 0.054747720 1.767689e-13
## 56038 ENSMUSG00000011884 0.003957014 0.021513047 8.846174e-01
## 63432 ENSMUSG00000042684 0.058696429 0.004868026 8.328277e-05
## neg_log_lung_pvalue rho tmm tau omega
## 59913 29.46768 0.4946872 201.9351 1.9587199 1.041839e-55
## 63634 29.63010 0.4946872 196.1441 1.9025485 1.787168e-56
## 63703 29.80771 0.4946872 190.0707 1.8436382 1.717155e-55
## 56659 30.48351 0.4946872 169.1971 1.6411694 3.098684e-55
## 56038 32.50629 0.4946872 122.9492 1.1925765 1.217624e-55
## 63432 39.78123 0.4946872 51.0000 0.4946872 2.755270e-56
## beta_tieda n.betas.tieda.se p.below.0 rho.class
## 59913 1.064626984 4.59137869 0.4083175 51
## 63634 0.043061086 0.05619233 0.2217441 51
## 63703 0.272539683 5.89450845 0.4815610 51
## 56659 0.581488038 7.91829147 0.4707296 51
## 56038 0.003957014 0.14667327 0.4892385 51
## 63432 0.058696429 0.06977124 0.2000984 51
liver.ASE <- read.csv(file= "ASE.genetics.113.153882-6.csv")
liver.ASE.symbol <- unique(liver.ASE$geneID)
length(liver.ASE.symbol)
## [1] 440
#liver.ASE <-liver.ASE[liver.ASE$geneID %in% names(table(liver.ASE$geneID))[table(liver.ASE$geneID) >=2], ]
sub.liver.ASE <-liver.ASE[which(liver.ASE$pvalBH.DxB7 < 0.05 & liver.ASE$pvalBH.BxD7 < 0.05), ]
dim(sub.liver.ASE)
## [1] 1044 19
liver.ASE.symbol <- unique(liver.ASE$geneID)
liver.ASE.symbol <- unique(sub.liver.ASE$geneID)
length(liver.ASE.symbol)
## [1] 367
head(liver.ASE.symbol)
## [1] Rrs1 6330578E17Rik Aox3 Cps1 Ugt1a10
## [6] Heatr7b1
## 440 Levels: 0610010D20Rik 1100001G20Rik 1100001H23Rik ... Zfp180
library(biomaRt)
mouse = useMart("ensembl", dataset = "mmusculus_gene_ensembl")
liver.ASE.ensembl <- getBM( attributes=c("ensembl_gene_id", "mgi_symbol") , filters=
"mgi_symbol" , values =liver.ASE.symbol ,mart=mouse)
dim(liver.ASE.ensembl)
## [1] 327 2
length(unique(liver.ASE.ensembl$ensembl_gene_id))
## [1] 327
new.result.df2.rho1 <- new.result.df2[new.result.df2$rho.class == 21, ]
head(new.result.df2.rho1)
## ensembl_id betas.hat liver_residual_variance liver_pvalue
## 22019 ENSMUSG00000000001 0.015986111 0.018910763 5.377718e-01
## 22020 ENSMUSG00000000003 0.016880682 0.003757142 1.929225e-01
## 22021 ENSMUSG00000000037 0.003865079 0.004681090 7.788138e-01
## 22022 ENSMUSG00000000049 0.042233333 0.001829162 9.084753e-06
## 22023 ENSMUSG00000000056 0.063604072 0.078095912 2.269175e-01
## 22024 ENSMUSG00000000058 0.025611111 0.004853968 5.846922e-02
## neg_log_lung_pvalue rho tmm tau
## 22019 2.0960974 0.2036947 1.241706e+25 1.204424e+23
## 22020 2.7653413 0.2036947 1.049501e+19 1.017989e+17
## 22021 1.1814994 0.2036947 3.305866e+44 3.206608e+42
## 22022 0.2321777 0.2036947 3.536116e+226 3.429944e+224
## 22023 1.5218109 0.2036947 3.662360e+34 3.552397e+32
## 22024 1.2292565 0.2036947 6.161600e+42 5.976597e+40
## omega beta_tieda n.betas.tieda.se p.below.0 rho.class
## 22019 5.897436e-45 0.015986111 0.13751641 0.4537277 21
## 22020 1.171687e-45 0.016880682 2.07432714 0.4967535 21
## 22021 1.459826e-45 0.003865079 0.06841849 0.4774750 21
## 22022 5.704352e-46 0.042233333 1.44735324 0.4883606 21
## 22023 2.435468e-44 0.063604072 0.27945646 0.4099789 21
## 22024 1.513739e-45 0.025611111 2.35774553 0.4956666 21
new.result.df2.rho1$rank.p.below.0 <- rank(new.result.df2.rho1$p.below.0)
new.result.df2.rho1$rank.liver.pvalue <- rank(new.result.df2.rho1$liver_pvalue)
result.rho1 <- matrix(, nrow(new.result.df2.rho1), 8)
colnames(result.rho1)<-c("bayrank","bayover","bay_TPR","bay_FPR", "orirank","oriover","ori_TPR","ori_FPR" )
for (i in 1:nrow(new.result.df2.rho1))
{
newdata1.rho1 <- subset(new.result.df2.rho1, rank.p.below.0 <= i)
overlap.newdata1.rho1 <- newdata1.rho1[newdata1.rho1$ensembl_id %in% liver.ASE.ensembl$ensembl_gene_id, ]
result.rho1[i, 1] <- i
result.rho1[i, 2] <- nrow(overlap.newdata1.rho1)/nrow(newdata1.rho1)
result.rho1[i, 3] <- nrow(overlap.newdata1.rho1)/nrow(liver.ASE.ensembl)
newdata2.rho1 <- subset(new.result.df2.rho1, rank.liver.pvalue <= i)
overlap.newdata2.rho1 <- newdata2.rho1[newdata2.rho1$ensembl_id %in% liver.ASE.ensembl$ensembl_gene_id, ]
result.rho1[i, 5] <- i
result.rho1[i, 6] <- nrow(overlap.newdata2.rho1)/nrow(newdata2.rho1)
result.rho1[i, 7] <- nrow(overlap.newdata2.rho1)/nrow(liver.ASE.ensembl)
}
head(result.rho1)
## bayrank bayover bay_TPR bay_FPR orirank oriover ori_TPR
## [1,] 1 0.0000000 0.000000000 NA 1 0.0000000 0.000000000
## [2,] 2 0.5000000 0.003058104 NA 2 0.5000000 0.003058104
## [3,] 3 0.3333333 0.003058104 NA 3 0.6666667 0.006116208
## [4,] 4 0.2500000 0.003058104 NA 4 0.5000000 0.006116208
## [5,] 5 0.2000000 0.003058104 NA 5 0.4000000 0.006116208
## [6,] 6 0.1666667 0.003058104 NA 6 0.5000000 0.009174312
## ori_FPR
## [1,] NA
## [2,] NA
## [3,] NA
## [4,] NA
## [5,] NA
## [6,] NA
plot(result.rho1[, 1], result.rho1[, 3], type="l", col="red", xlab="Ranking", ylab="TPR", ylim=c(0, 1), xlim=c(0, 1800) )
par(new=TRUE)
plot( result.rho1[, 1], result.rho1[, 7], type="l", col="green", xlab="Ranking", ylab="TPR", ylim=c(0, 1), xlim=c(0, 1800) )
legend("topright", inset=.05, c("Bayesian","Original"), text.col = c("red", "green"), horiz=TRUE)
qplot(omega, 1-p.below.0, group=rho.class, colour = rho.class, shape=rho.class, data = new.result.df2)
qplot(omega, beta_tieda, group=rho.class, colour = rho.class, shape=rho.class, data = new.result.df2)
qplot(betas.hat, beta_tieda, group=rho.class, colour = rho.class, shape=rho.class, data = new.result.df2)
qplot(neg_log_lung_pvalue, data = new.result.df2)
qplot(exp(-neg_log_lung_pvalue), data = new.result.df2)
