## [1] "Document is generated at 2020-01-17."Table of contents
Read PPP1 peptide matrix
## [1] "/Users/gc5k/manuscript/Protein/2019Dec"md=read.csv("guot_170728_PC1_CPP585_sw_allFrag_with_dscore_filtered.csv", as.is = T, header = T, sep = "\t")
proFile="pppb_pep2.txt"
prot=read.table(proFile, as.is = T, header = T)
#rownames(prot)=prot[,1]
#prot=prot[,-c(1,2)]
PPP0=read.csv("PPPB1_lineup.csv", as.is = T, header = T)
PPP1=PPP0
PPP1$PPPA_ID=tolower(PPP1$PPPA_ID)
dim(PPP1)## [1] 1566 12## [1] 16644 1566Remove missing
#evaluate missing value
protT=t(prot)
misD=matrix(1, nrow(protT), 4)
for(i in 1:nrow(misD)) {
naIdx=which(is.na(protT[i,]))
if (length(naIdx) < ncol(protT)) {
misD[i, 1] = length(naIdx)/ncol(protT)
misD[i, 2] = min(protT[i,-naIdx])
misD[i, 3] = median(protT[i, -naIdx])
misD[i, 4] = mean(protT[i, -naIdx])
}
}
blankInd = which(misD[,1]> 0.8)
prot=prot[,-blankInd]
protT=protT[-blankInd,]
PPP1=PPP1[-blankInd,]
print(paste0("Removed: ", length(blankInd), " samples"))## [1] "Removed: 96 samples"Density plot
## [1] "Slide 1: A-AQUA, C-CTRL, N-Normal, T-Tumor"
Quality score
Quality score is defined as \[Q=\frac{\sum_{i=1}^\tilde{M}\tilde{s}_i(k-\frac{n_o}{n})^c}{\sum_{i=1}^Ms_i}\] in which \(\tilde{s}_i=[y_i-(E(y_i|n_{o_i})+T_{\alpha/M}\sigma_i)]^2\) if \(y_i > E(y_i|n_{o_i})\) or \(\tilde{s}_i=[y_i-(E(y_i|n_{o_i})-T_{\alpha/M}\sigma_i)]^2\) if \(y_i < E(y_i|n_{o_i})\). In contrast, \(s_i=[y_i-E(y_i|n_{o_i})]^2\).
\(T_{\alpha/M}\) is the z-score given the p-value cutoff \(\alpha/M\), and \(\alpha\) can be for example 0.01 or 0.05.
\(E(y_i|n_{o_i})\) can be predicted from the fitted spline.
\(\alpha\) determins the confidence interval of the spline. The smaller the \(\alpha\), the larger the interval and the smaller \(\tilde{M}\) till zero.
\(K\), between 0 and 1, indicates the direction of the penalty. When it is 1, the penalty is more weighted on the proteins with fewer observations, and nearly no penalty at all for proteins without missing values.
\(C\) is the penalty for the score.
In summary, the quality score is upon \(M\), \(\alpha\), \(K\), and \(C\).
## [1] "Slide 2: Qscore"## A C N T
## [1,] 0.14320251 0.11069550 0.10148688 0.10206335
## [2,] 0.10885643 0.13331441 0.10983203 0.10756830
## [3,] 0.11976395 0.08213037 0.08023418 0.08110984
## [4,] 0.08900582 0.11677759 0.09476257 0.09175888
## [5,] 0.10121566 0.06375519 0.06496515 0.06546652
## [6,] 0.07045752 0.09840241 0.07949354 0.07611556
Pair-wise comparison for expression
GC
## [1] "Slide 3: GC control"## [1] "Bartlett test"
PCA
## [1] "Slide 4: PCA"## [,1] [,2] [,3]
## [1,] -0.4108678 0.0844348 0.2532472
## [2,] 0.4551288 0.4578845 0.6650268
## [3,] 0.5130904 0.5709893 0.8342511
## [4,] -0.5057928 1.0116235 1.2674499## [,1] [,2] [,3]
## [1,] 0.4057713 0.4812725 0.6459228
## [2,] 0.2234960 1.2121509 1.2621014
## [3,] 0.1751561 0.9479307 0.9786104
## [4,] -0.2319347 0.9824451 1.0362388
m-score vs expression
Count vs Exp analysis PCT1
iNPC1=which(PPP1$Tissue == "N" & PPP1$MS_ID == "PC1")
iTPC1=which(PPP1$Tissue == "T" & PPP1$MS_ID == "PC1")
layout(matrix(1:2, 1, 2))
#TrEst=matrix(-9, nrow(prot), 12)
TrEst=matrix(-9, ncol(protT), 12)
colnames(TrEst)=c("n_cs", "ObsM_cs", "ObsSD_cs", "EstM_cs", "EstSD_cs", "n_cl", "ObsM_cl", "ObsSD_cl", "EstM_cl", "EstSD_cl", "t", "p-value")
#for(i in 1:nrow(prot)) {
for(i in 1:ncol(protT)) {
cl=protT[iNPC1, i]
clNa=which(is.na(cl))
clSetSt=sort(cl[!is.na(cl)])
TrEst[i,1]=length(clSetSt)
TrEst[i,2]=mean(clSetSt)
TrEst[i,3]=sd(clSetSt)
cs=protT[iTPC1, i]
csNa=which(is.na(cs))
csSetSt=sort(cs[!is.na(cs)])
TrEst[i,6]=length(csSetSt)
TrEst[i,7]=mean(csSetSt)
TrEst[i,8]=sd(csSetSt)
if(TrEst[i, 1] > 10 && TrEst[i,6] > 10) {
qclSet=qnorm(seq(1-(length(clNa)+0.5)/(length(cl)+1), 0.999, length.out = length(clSetSt)))
lm1=lm(as.numeric(clSetSt)~as.numeric(qclSet))
TrEst[i,4]=lm1$coefficients[1]
TrEst[i,5]=lm1$coefficients[2]
# plot(main=paste0("cl [", length(clSetSt), "]"), clSetSt, qclSet, cex=0.5, pch=16)
qcsSet=qnorm(seq(1-(length(csNa)+0.5)/(length(cs)+1), 0.999, length.out = length(csSetSt)))
lm2=lm(csSetSt~qcsSet)
TrEst[i,9]=lm2$coefficients[1]
TrEst[i,10]=lm2$coefficients[2]
# plot(main=paste0("cs [", length(csSetSt), "]"), csSetSt, qcsSet, cex=0.5, pch=16)
tTest=t.test(csSetSt, clSetSt)
TrEst[i,11]=tTest$statistic
TrEst[i,12]=tTest$p.value
}
}
TrEstOrg=TrEst
idxN=which(TrEstOrg[,2] > 15)
idxT=which(TrEstOrg[,7] > 15)
write.table(rownames(prot)[idxN], "PCT1_Norm.txt", row.names=F, col.names=F, quote=F)
write.table(rownames(prot)[idxT], "PCT1_Tumor.txt", row.names=F, col.names=F, quote=F)
write.table(rownames(prot)[sort(union(idxN, idxT))], "PCT1_NT.txt", row.names=F, col.names=F, quote=F)
layout(matrix(1:6, 2, 3))
cutoff=c(10, 50, 100)
for(i in 1:length(cutoff)) {
idx=which(TrEstOrg[,1] > cutoff[i] & TrEstOrg[,6] > cutoff[i])
lg=length(which(TrEstOrg[idx,12]< (0.05/nrow(TrEstOrg))))
plot(main=paste0(length(idx), " [Sig.", lg, "]"), xlab="Count [T-N]", ylab="Exp [T-N]", TrEstOrg[idx,6]-TrEstOrg[idx,1], TrEstOrg[idx,7]-TrEstOrg[idx,2], cex=0.1, pch=16, bty='l',
col=ifelse(TrEstOrg[idx,12]< (0.05/nrow(TrEstOrg)), "red", "grey"))
# plot(TrEst[,1]-TrEst[,6], TrEst[,2]-TrEst[,7], cex=0.1, pch=16, col=ifelse(TrEst[,12] <0.05/nrow(TrEst), "red", "grey"))
}
lCL=glm(TrEst[,2]~TrEst[,1])
lCS=glm(TrEst[,7]~TrEst[,6])
layout(matrix(1:2, 1, 2))
plot(main="Normal", TrEst[,1], TrEst[,2], bty="l", xlab="Count", ylab="Exp", pch=16, cex=0.3)
abline(lCL, col="grey")
lines(spline(TrEst[,1], TrEst[,2]), col="green")
points(mean(TrEst[,1]), mean(TrEst[,2]), pch=16, cex=2, col="gold")
plot(main="Tumor", TrEst[,6], TrEst[,7], bty="l", xlab="Count", ylab="Exp", pch=16, cex=0.3)
abline(lCS, col="red")
lines(spline(TrEst[,6], TrEst[,7]), col="green")
points(mean(TrEst[,6]), mean(TrEst[,7]), pch=16, cex=2, col="gold")
##
## Call: glm(formula = TrEst[, 2] ~ TrEst[, 1])
##
## Coefficients:
## (Intercept) TrEst[, 1]
## 13.10071 0.01032
##
## Degrees of Freedom: 15290 Total (i.e. Null); 15289 Residual
## (1353 observations deleted due to missingness)
## Null Deviance: 28250
## Residual Deviance: 22260 AIC: 49140##
## Call: glm(formula = TrEst[, 7] ~ TrEst[, 6])
##
## Coefficients:
## (Intercept) TrEst[, 6]
## 13.319016 0.008457
##
## Degrees of Freedom: 15445 Total (i.e. Null); 15444 Residual
## (1198 observations deleted due to missingness)
## Null Deviance: 27860
## Residual Deviance: 23880 AIC: 50570Count vs Exp analysis
iN=which(PPP1$Tissue == "N")
iT=which(PPP1$Tissue == "T")
layout(matrix(1:2, 1, 2))
TrEst=matrix(-9, ncol(protT), 12)
colnames(TrEst)=c("n_cs", "ObsM_cs", "ObsSD_cs", "EstM_cs", "EstSD_cs", "n_cl", "ObsM_cl", "ObsSD_cl", "EstM_cl", "EstSD_cl", "t", "p-value")
for(i in 1:ncol(protT)) {
cl=protT[iN, i]
clNa=which(is.na(cl))
clSetSt=sort(cl[!is.na(cl)])
TrEst[i,1]=length(clSetSt)
TrEst[i,2]=mean(clSetSt)
TrEst[i,3]=sd(clSetSt)
cs=protT[iT, i]
csNa=which(is.na(cs))
csSetSt=sort(cs[!is.na(cs)])
TrEst[i,6]=length(csSetSt)
TrEst[i,7]=mean(csSetSt)
TrEst[i,8]=sd(csSetSt)
if(TrEst[i, 1] > 10 && TrEst[i,6] > 10) {
qclSet=qnorm(seq(1-(length(clNa)+0.5)/(length(cl)+1), 0.999, length.out = length(clSetSt)))
lm1=lm(clSetSt~qclSet)
TrEst[i,4]=lm1$coefficients[1]
TrEst[i,5]=lm1$coefficients[2]
# plot(main=paste0("cl [", length(clSetSt), "]"), clSetSt, qclSet, cex=0.5, pch=16)
qcsSet=qnorm(seq(1-(length(csNa)+0.5)/(length(cs)+1), 0.999, length.out = length(csSetSt)))
lm2=lm(csSetSt~qcsSet)
TrEst[i,9]=lm2$coefficients[1]
TrEst[i,10]=lm2$coefficients[2]
# plot(main=paste0("cs [", length(csSetSt), "]"), csSetSt, qcsSet, cex=0.5, pch=16)
tTest=t.test(csSetSt, clSetSt)
TrEst[i,11]=tTest$statistic
TrEst[i,12]=tTest$p.value
}
}
TrEstOrg=TrEst
layout(matrix(1:6, 2, 3))
cutoff=c(10, 50, 100, 200, 300, 500)
for(i in 1:length(cutoff)) {
idx=which(TrEstOrg[,1] > cutoff[i] & TrEstOrg[,6] > cutoff[i])
lg=length(which(TrEstOrg[idx,12]< (0.05/nrow(TrEstOrg))))
plot(main=paste0(length(idx), " [Sig.", lg, "]"), xlab="Count [T-N]", ylab="Exp [T-N]", TrEstOrg[idx,6]-TrEstOrg[idx,1], TrEstOrg[idx,7]-TrEstOrg[idx,2], cex=0.1, pch=16, bty='l',
col=ifelse(TrEstOrg[idx,12]< (0.05/nrow(TrEstOrg)), "red", "grey"))
# plot(TrEst[,1]-TrEst[,6], TrEst[,2]-TrEst[,7], cex=0.1, pch=16, col=ifelse(TrEst[,12] <0.05/nrow(TrEst), "red", "grey"))
}
lCL=glm(TrEst[,2]~TrEst[,1])
lCS=glm(TrEst[,7]~TrEst[,6])
layout(matrix(1:2, 1, 2))
plot(main="Normal", TrEst[,1], TrEst[,2], bty="l", xlab="Count", ylab="Exp", pch=16, cex=0.3)
abline(lCL, col="grey")
lines(spline(TrEst[,1], TrEst[,2]), col="green")
points(mean(TrEst[,1]), mean(TrEst[,2]), pch=16, cex=2, col="gold")
plot(main="Tumor", TrEst[,6], TrEst[,7], bty="l", xlab="Count", ylab="Exp", pch=16, cex=0.3)
abline(lCS, col="red")
lines(spline(TrEst[,6], TrEst[,7]), col="green")
points(mean(TrEst[,6]), mean(TrEst[,7]), pch=16, cex=2, col="gold")
##
## Call: glm(formula = TrEst[, 2] ~ TrEst[, 1])
##
## Coefficients:
## (Intercept) TrEst[, 1]
## 13.113977 0.002737
##
## Degrees of Freedom: 16369 Total (i.e. Null); 16368 Residual
## (274 observations deleted due to missingness)
## Null Deviance: 29240
## Residual Deviance: 22470 AIC: 51640##
## Call: glm(formula = TrEst[, 7] ~ TrEst[, 6])
##
## Coefficients:
## (Intercept) TrEst[, 6]
## 13.259880 0.002377
##
## Degrees of Freedom: 16477 Total (i.e. Null); 16476 Residual
## (166 observations deleted due to missingness)
## Null Deviance: 28270
## Residual Deviance: 23110 AIC: 52340G_2007.0659 example
Each patient has a biological sample for tumor and a sample for normal control. Each tomor/normal sample has been analyzed independently on a machine. The analysis for Patient G_2007.0659, who is taken as the representative of the layout of the experiment.
UniSamp=table(PPP1$USZ_ID) #get sample ids, and G_2007.0659 is the first sample
for(i in 1:1){
idx=which(PPP1$USZ_ID == names(UniSamp)[i])
CB=combn(length(idx), 2)
mat=matrix(0, ncol(CB), 3)
for(j in 1:ncol(CB)) {
ln=which(!is.na(prot[,idx[CB[1,j]]]) & !is.na(prot[,idx[CB[2,j]]]))
tg=paste0(PPP1[idx[CB[1,j]],]$Tissue, PPP1[idx[CB[2,j]],]$Tissue)
mat[j, 1] = tg
mat[j, 2] = length(ln)
mat[j, 3] = names(UniSamp)[i]
}
nn=mean(as.numeric(mat[which(mat[,1]=="NN", 1), 2]))
tt=mean(as.numeric(mat[which(mat[,1]=="TT", 1), 2]))
nt=mean(as.numeric(mat[which(mat[,1]=="NT" | mat[,1] == "TN", 1), 2]))
tn=mean(as.numeric(mat[which(mat[,1]=="NT" | mat[,1] == "TN", 1), 2]))
if(i == 1) {
TB = mat
} else {
TB = rbind(TB, mat)
}
layout(matrix(1:4, 2, 2))
ID=matrix(c(1,2,3,4,1,4,2,3), 4,2, byrow = T)
for(j in 1:4) {
idxP=which(!is.na(prot[,idx[ID[j,1]]]) & !is.na(prot[,idx[ID[j,2]]]))
plot(xlim=c(0, 25), ylim=c(0, 25), col="black", main=paste0(names(UniSamp)[i]," [", ID[j,1], ",", ID[j,2], "]"), xlab=paste0(PPP1$Tissue[idx[ID[j,1]]], ", ", PPP1$MS_ID[idx[ID[j,1]]], " [", length(which(!is.na(prot[idx[ID[j,1]]]))), "]"), ylab=paste0(PPP1$Tissue[idx[ID[j,2]]], ", ", PPP1$MS_ID[idx[ID[j,2]]], " [", length(which(!is.na(prot[idx[ID[j,2]]]))), "]"),
prot[idxP, idx[ID[j,1]]], prot[idxP, idx[ID[j,2]]],
pch=16, cex=0.5, bty='l')
idxP_1=which(!is.na(prot[idx[ID[j,1]]]) & is.na(prot[idx[ID[j,2]]]))
idxP_2=which(is.na(prot[ID[j,1]]) & !is.na(prot[idx[ID[j,2]]]))
l1=length(which(!is.na(prot[idx[ID[j,1]]])))
l2=length(which(!is.na(prot[idx[ID[j,2]]])))
points(prot[idxP_1, idx[ID[j,1]]], rep(0, length(idxP_1)), col="grey", pch=3, cex=0.2)
points(rep(0, length(idxP_2)), prot[idxP_2, idx[ID[j,2]]], col="grey", pch=3, cex=0.2)
abline(a=0, b=1, col="red", lty=2)
legend("topleft",
legend = c(format(length(idxP)/sqrt(l1*l2), digits = 4), format(length(idxP)/min(l1,l2), digits = 4), format(digits=4, cor(prot[idxP, idx[ID[j,1]]], prot[idxP, idx[ID[j,2]]]))),
pch=c(16, 16, 16), col=c("white", "white", "white"), bty = 'n')
legend("bottomright",
legend = c("Observed in both", "Observed in either"),
pch=c(16, 16), col=c("black", "grey"), bty = 'n')
}
}
Annotation for G2007.0659
The patient G2007.0659 has its normal sample and tumor sample scanned independently on proCan 1 and 4, respectively. Top left is the correlation for the peptides of the normal tissue observed in proCan 1 and 4, respectively. The number of commonly observed peptides and their expression correlation can be found at the top left in each plot. The bottom left is for the tumor tissues, and the top left is between one of the normal tissue and one of the tumor tissue. Similarly for the bottom right panel.
Variance: Tech vs Biology
UniSamp=table(PPP1$USZ_ID)
UniCor=matrix(-1, length(UniSamp), 6)
for(i in 1:length(UniSamp)) {
idx=which(PPP1$USZ_ID == names(UniSamp)[i])
CB=combn(length(idx), 2)
mat=matrix(0, ncol(CB), 8)
#mat[,7]=100
for(j in 1:ncol(CB)) {
ln=which(!is.na(prot[,idx[CB[1,j]]]) & !is.na(prot[,idx[CB[2,j]]]))
ln1=which(!is.na(prot[,idx[CB[1,j]]]) & is.na(prot[,idx[CB[2,j]]]))
ln2=which(is.na(prot[,idx[CB[1,j]]]) & !is.na(prot[,idx[CB[2,j]]]))
if(length(ln) != 0 && length(ln1) !=0 && length(ln2) != 0) {
tg=paste0(PPP1[idx[CB[1,j]],]$Tissue, PPP1[idx[CB[2,j]],]$Tissue)
mat[j, 1] = tg
mat[j, 2] = length(ln)
mat[j, 3] = names(UniSamp)[i]
mat[j, 4] = cor(prot[ln, idx[CB[1,j]]], prot[ln, idx[CB[2,j]]])
mat[j, 5] = length(which(!is.na(prot[,idx[CB[1,j]]])))
mat[j, 6] = length(which(!is.na(prot[,idx[CB[2,j]]])))
pepDat=matrix(0, as.numeric(mat[j,5])+as.numeric(mat[j,6])-as.numeric(mat[j,2]), 2)
pepDat[1:length(ln),1]=prot[ln,idx[CB[1,j]]]
pepDat[1:length(ln),2]=prot[ln,idx[CB[2,j]]]
pepDat[(length(ln)+1):as.numeric(mat[j,5]),1]=prot[ln1, idx[CB[1,j]]]
pepDat[(as.numeric(mat[j,5])+1):nrow(pepDat),2]=prot[ln2, idx[CB[2,j]]]
mat[,8] = cor(pepDat[,1], pepDat[,2])
}
}
nn=mean(as.numeric(mat[which(mat[,1]=="NN", 1), 2]))
tt=mean(as.numeric(mat[which(mat[,1]=="TT", 1), 2]))
nt=mean(as.numeric(mat[which(mat[,1]=="NT" | mat[,1] == "TN", 1), 2]))
tn=mean(as.numeric(mat[which(mat[,1]=="NT" | mat[,1] == "TN", 1), 2]))
cnt=0
if(length(which(mat[,1] == "NN") > 0)) {
UniCor[i,1] = mean(as.numeric(mat[mat[,1]=="NN",4]))
cnt=cnt+1
}
if(length(which(mat[,1] == "TT") > 0)) {
UniCor[i,2] = mean(as.numeric(mat[mat[,1]=="TT",4]))
cnt=cnt+1
}
if(length(which(mat[,1] == "NT" | mat[,1] == "TN") > 0)) {
UniCor[i,3] = mean(as.numeric(mat[mat[,1]=="NT" || mat[,1] == "TN",4]))
cnt=cnt+1
}
if(cnt==3) {
z1=1-UniCor[i,1]
z2=1-UniCor[i,2]
z3=1-UniCor[i,3]
UniCor[i,4]=z1/(z1+z2+z3)
UniCor[i,5]=z2/(z1+z2+z3)
UniCor[i,6]=z3/(z1+z2+z3)
}
if(i == 1) {
TB = mat
} else {
TB = rbind(TB, mat)
}
}
colnames(UniCor)=c("", "", "", "Var (Tech normal)", "Var (Tech tumor)", "Var (Biology)")
idxCor=which(UniCor[,4]>0 & UniCor[,5]>0 & UniCor[,6]>0)
boxplot(UniCor[idxCor,4:6], ylab="Proportion of variance", col=c("yellow", "red", "blue"))
Variance analysis
There are three kinds of pairs:
\(r_{N,N}=cor(N_1, N_2)\), correlation between a pair of normal samples, indicating technical variation.
\(r_{T,T}=cor(T_1, T_2)\), correlation between a pair of tumor sample, indicating technical variation.
\(r_{N,T}=r_{T,N}=cor(N, T)=cor(T,N)\), correlation between a normal sample and a tumor sample, indicating biological variation.
Then the proportion of total variance can be partitioned into
\(V(Tech N)=\frac{r_{N_1,N_2}}{r_{N_1,N_2}+r_{T_1,T_2}+r_{N,T}}\)
\(V(Tech T)=\frac{r_{T_1,T_2}}{r_{N_1,N_2}+r_{T_1,T_2}+r_{N,T}}\)
\(V(Tech Bio)=\frac{r_{N,T}}{r_{N_1,N_2}+r_{T_1,T_2}+r_{N,T}}\)
Peptide count differentiation
TtestM=as.data.frame(matrix(0, 6, 3))
colnames(TtestM)[1:3]=c("M1", "M2", "p-value")
lb=c("NN", "NT", "TN", "TT")
cnt=1
for(i in 2:length(lb)) {
for(j in 1:(i-1)) {
rownames(TtestM)[cnt]=paste0(lb[i], "-", lb[j])
tt=t.test(as.numeric(TB[which(TB[,1]==lb[i]), 2]), as.numeric(TB[which(TB[,1]==lb[j]), 2]))
TtestM[cnt,c(1,2)]=tt$estimate[1:2]
TtestM[cnt,3]=tt$p.value
cnt=cnt+1
}
}
library(knitr)## Warning: package 'knitr' was built under R version 3.4.3| M1 | M2 | p-value | |
|---|---|---|---|
| NT-NN | 5856.163 | 5857.804 | 0.9769920 |
| TN-NN | 5873.854 | 5857.804 | 0.7793264 |
| TN-NT | 5873.854 | 5856.163 | 0.7425975 |
| TT-NN | 6434.375 | 5857.804 | 0.0000000 |
| TT-NT | 6434.375 | 5856.163 | 0.0000000 |
| TT-TN | 6434.375 | 5873.854 | 0.0000000 |
lb=c("NN", "NT", "TN", "TT")
cntMat=as.data.frame(matrix(0, length(UniSamp), 4))
colnames(cntMat)=lb
for(i in 1:length(UniSamp)) {
idx=which(TB[,3] == names(UniSamp)[i])
if(length(idx) == 0) {
break
}
tbSet=as.data.frame(TB[idx,])
for(j in 1:length(lb)) {
idx1=which(tbSet[,1] == lb[j])
if(length(idx1) > 0) {
cntMat[i, j] = mean(as.numeric(as.character(tbSet[idx1, 2])))
}
}
}
layout(matrix(c(1, 1, 2, 3, 4, 5, 6, 7), 2, 4, byrow = F))
boxplot(cntMat, col=c("gold", 2:4), bty='l')
for(i in 1:(length(lb)-1)) {
for(j in (i+1):length(lb)) {
idx=which(cntMat[,i] > 0 & cntMat[,j])
mod=lm(cntMat[idx,i]~cntMat[idx,j])
matUniFt=as.matrix(cntMat[idx, c(i,j)])
tp=t.test(cntMat[idx,i], cntMat[idx,j])
colnames(matUniFt)=c(lb[i], lb[j])
plot(bty='n', xlab=lb[i], ylab=lb[j], cntMat[idx,i], cntMat[idx,j], xlim=c(0, 10000), ylim=c(0, 10000), col=ifelse(cntMat[idx,i]<3000 | cntMat[idx,j]<3000, "black", "grey"), pch=ifelse(cntMat[idx,i]>cntMat[idx,j], 16, 1))
rug(cntMat[idx,i], side=1, col="red")
rug(cntMat[idx,j], side=2, col="blue")
abline(a=0, b=1, col="grey", lty=2)
abline(mod, col="grey", lwd=2)
legend("topleft", legend = c(paste0(lb[i],"=", format(mod$coefficients[1], digits = 3), "+", format(mod$coefficients[2], digits=3), "*", lb[j], "+e")), bty='n', lty=1, col="grey")
}
}
Annotation
t-test for the difference of counts of observed peptides between a pair of samples.
Correlation at various levels
TSnames=c("AQUA", "CTRL", "Normal", "Tumor")
Aidx=which(PPP1$Tissue == "A")
Cidx=which(PPP1$Tissue == "C")
Nidx=which(PPP1$Tissue == "N")
Tidx=which(PPP1$Tissue == "T")
IDX=c(length(Aidx), length(Cidx), length(Nidx), length(Tidx))
TSidx=list(Aidx)
TSidx[2]=list(Cidx)
TSidx[3]=list(Nidx)
TSidx[4]=list(Tidx)
####AQ
PPP1_A=PPP1[TSidx[[1]],]
exDat_A=protT[TSidx[[1]],]
batchName=names(table(PPP1_A$Batch))
AQcor=matrix(0, length(batchName), 6)
cnt=0
for(i in 1:length(batchName)) {
if(length(which(PPP1_A$Batch == i))!=2) next
cnt=cnt+1
ex2=exDat_A[which(PPP1_A$Batch==i),]
n1=length(which(!is.na(ex2[1,])))
n2=length(which(!is.na(ex2[2,])))
no=length(which(!is.na(ex2[1,]) & !is.na(ex2[2,])))
ct=cor.test(ex2[1,], ex2[2,])
AQcor[cnt,1:6]=c(ct$estimate, n1, n2, no, no/sqrt(n1*n2), no/min(n1,n2))
}
####CTRL
PPP1_CL=PPP1[TSidx[[2]],]
exDat_CL=protT[TSidx[[2]],]
batchNameCL=names(table(PPP1_CL$Batch))
CLcor=matrix(0, length(batchNameCL), 6)
CLcnt=0
for(i in 1:length(batchNameCL)) {
if(length(which(PPP1_CL$Batch == i))!=2) next
CLcnt=CLcnt+1
ex2=exDat_CL[which(PPP1_CL$Batch==i),]
n1=length(which(!is.na(ex2[1,])))
n2=length(which(!is.na(ex2[2,])))
no=length(which(!is.na(ex2[1,]) & !is.na(ex2[2,])))
ct=cor.test(ex2[1,], ex2[2,])
CLcor[CLcnt,1:6]=c(ct$estimate, n1, n2, no, no/sqrt(n1*n2), no/min(n1,n2))
}
layout(matrix(1:6, 3, 2))
hist(main="AQUA", AQcor[1:cnt,5], xlim=c(0, 1), xlab="Correlation (Overlapping)")
legend("topleft", legend = format(mean(AQcor[1:cnt,5]), digits = 3), bty='n')
hist(main="AQUA", AQcor[1:cnt,6], xlim=c(0, 1), xlab="Correlation (Normalized)")
legend("topleft", legend = format(mean(AQcor[1:cnt,6]), digits = 3), bty='n')
hist(main="AQUA", AQcor[1:cnt,1], xlim=c(0, 1), xlab="Correlation expression")
legend("topleft", legend = format(mean(AQcor[1:cnt,1]), digits = 3), bty='n')
hist(main="CTRL", CLcor[1:CLcnt,5], xlim=c(0, 1), xlab="Correlation (Overlapping)")
legend("topleft", legend = format(mean(CLcor[1:CLcnt,5]), digits = 3), bty='n')
hist(main="CTRL",
CLcor[1:CLcnt,6], xlim=c(0, 1), xlab="Correlation (Normalized)")
legend("topleft", legend = format(mean(CLcor[1:CLcnt,6]), digits = 3), bty='n')
hist(main="CTRL",
CLcor[1:CLcnt,1], xlim=c(0, 1), xlab="Correlation expression")
legend("topleft", legend = format(mean(CLcor[1:CLcnt,1]), digits = 3), bty='n')
layout(matrix(1:12, 3, 4))
TAG=c("NN", "NT", "TN", "TT")
for(i in 1:length(TAG)) {
idxTT=which(TB[,1]==TAG[i] & as.numeric(TB[,2])>0)
hist(main=paste0(TAG[i], " [", length(idxTT),"]"), breaks = 40, ylim=c(0, 120), xlab="Correlation (Overlapping)", xlim =c(0.3, 1), as.numeric(TB[idxTT,2])/sqrt(as.numeric(TB[idxTT,5])*as.numeric(TB[idxTT,6])))
legend("topleft", legend = format(mean(as.numeric(TB[idxTT,2])/sqrt(as.numeric(TB[idxTT,5])*as.numeric(TB[idxTT,6]))), digits = 4), bty='n')
DidxTT=apply(matrix(c(as.numeric(TB[idxTT, 5]), as.numeric(TB[idxTT,6])), length(idxTT),2), 1, min)
hist(main=paste(TAG[i]), breaks = 40, ylim=c(0, 120), xlab="Correlation (Normalized)", xlim =c(0.3, 1), as.numeric(TB[idxTT,2])/DidxTT, col="red")
legend("topleft", legend = format(mean(as.numeric(TB[idxTT,2])/DidxTT), digits = 4), bty='n')
hist(main=paste(TAG[i]), breaks = 40, ylim=c(0, 120), xlab="Correlation (Pearson)", xlim =c(0.3, 1), as.numeric(TB[idxTT,4]), col="green")
legend("topleft", legend = format(mean(as.numeric(TB[idxTT,4])), digits = 4), bty='n')
}
Proportion of variance
Each column represents one of four possible sample pairs.
Top row: \(cor_1=\frac{n_{o}}{\sqrt{n_1n_2}}\) without normalization.
Middle row: \(cor_2=\frac{n_{o}}{min(n_1,n_2)}\) normalized correlation.
Bottom row: \(cor_3=cor(exp_1,exp_2)\) for \(n_o\) peptides.
Expression vs counts: across 6 MZ
Gold points are those peptides that had counts less than 20% of the normal and tumor samples observed respectively.
layout(matrix(1:6, 2, 3))
ms=c("PC1", "PC2", "PC3", "PC4", "PC5", "PC6")
for(m in 1:length(ms)) {
mat=matrix(0, ncol(protT), 4)
idxN=which(PPP1$Tissue == "N" & PPP1$MS_ID == ms[m])
idxT=which(PPP1$Tissue == "T" & PPP1$MS_ID == ms[m])
for(i in 1:ncol(protT)) {
mat[i,1]=length(which(!is.na(protT[idxN,i])))
mat[i,2]=length(which(!is.na(protT[idxT,i])))
protNor=protT[idxN,i]
protTr=protT[idxT,i]
mat[i,3]=mean(as.numeric(protNor), na.rm = T)
mat[i,4]=mean(as.numeric(protTr), na.rm = T)
}
plot(main=paste(ms[m], "T:", length(idxT), ", N:", length(idxN)), xlim=c(-100, 150), ylim=c(-5,5), mat[,2]-mat[,1], mat[,4]-mat[,3], pch=16, cex=0.1, bty="l", col="grey", xlab="Count (T-N)", ylab="Expression (T-N)")
abline(h=0, lty=2, col="red")
idxDiff=which(abs(mat[,4]-mat[,3])>1.5 & mat[,1]<length(idxN)*0.2 & mat[,2]<length(idxT)*0.2)
points(mat[idxDiff,2]-mat[idxDiff,1], mat[idxDiff,4]-mat[idxDiff,3], col="gold", pch=1, cex=0.3)
mm=mat[idxDiff,]
rownames(mm)=colnames(protT[,idxDiff])
colnames(mm)=c("Cnt[N]", "Cnt[T]", "Exp[N]", "Exp[T]")
write.table(mm, paste0("extreme_pro", m,".txt"), row.names = T, col.names = T, quote = F)
}
for(i in 1:length(ms)) {
exTab=read.table(paste0("extreme_pro", i, ".txt"), as.is = T, header = T)
print(exTab)
}## Cnt.N. Cnt.T. Exp.N.
## 14817_ELSDAPAFISQPR_2 1 1 12.99698
## 48333_QFTQSPETEASADSFPDTK_3 3 1 13.13402
## 45761_NLLASEEGITTSK_2 1 1 12.70562
## 37818_LLEGEEYR_2 4 1 13.57437
## 44557_NDLRDTFAALGR_2 1 1 13.71406
## DECOY_64509_VIEGDVVSALNK_3 3 1 16.94339
## 32415_KHGATVLTALGGILK_3 2 2 12.21060
## DECOY_57454_SVEETLR_2 2 5 13.38066
## 32647_KKEEEELVALK_2 3 1 12.67733
## 65608_VLSNMGAHFGGYLVK_3 2 1 14.39162
## 19578_FSKEEVDQMFAAFPPDVTGNLDYK_3 1 1 13.20456
## 14588_ELLQTELSGFLDAQK_3 1 1 13.01726
## 67193_VTAETEYGK_2 1 5 12.51999
## 53361_SDEHVVSGNLVTPLPVILGHEAAGIVESVGEGVTTVKPGDK_5 1 1 14.74655
## 67675_VVDSLQTSLDAETR_2 1 1 15.78252
## 16407_ETADTDTAEQVIASFR_3 2 1 13.88374
## 14440_ELGVGIALRK_2 3 30 14.48093
## 47150_NVEAMSGMEGR_2 3 2 13.42243
## 39476_LQDLVDK_2 3 4 12.92011
## 21876_GHFTEEDK_2 3 1 12.51329
## 48997_QLEDELAAMQK_2 2 1 14.66071
## 49509_QNANSQLSILFIEKPQGGTVK_3 2 1 12.67080
## 54162_SGEGQEDAGELDFSGLLK_2 1 1 12.59698
## 44660_NEEIDEMIK_2 1 1 15.20644
## DECOY_62709_VAPEEHPVLLTEATLNPK_3 1 2 16.97383
## 11725_EAFTIMDQNR_2 2 1 14.73390
## 36599_LGNQEPGGQTALK_2 2 1 12.67714
## 3412_ALGTNPTNAEVR_2 2 4 14.58431
## 25245_HEERQDEHGFISR_3 1 1 11.47307
## 42563_MEGDLNEMEIQLSHANR_3 1 1 14.19891
## 60831_TPLC(UniMod:4)GHC(UniMod:4)NNVIR_3 2 1 12.96126
## 2822_AISEELDHALNDMTSI_2 1 1 13.58672
## 48924_QKYEESQSELESSQK_2 1 1 13.06409
## 66566_VQLLHSQNTSLINQK_2 1 1 12.97939
## 38440_LLSSLDIDHNQYK_3 4 2 15.14239
## 24500_GVDEATIIDILTKR_3 5 2 13.47126
## 22634_GLGWSPAGSLEVEK_2 1 1 12.44305
## 29685_IMGEVMHALR_2 9 1 12.82485
## 37485_LKNAYEESLEHLETFKR_4 1 1 15.43215
## 34722_LASADIETYLLEK_2 1 1 13.96055
## 39778_LQNEIEDLMVDVER_2 2 2 13.73647
## 90_AADAEAEVASLNR_2 1 1 14.01759
## 14320_ELENELEAEQK_2 2 1 13.79605
## 34402_LAEQELIETSER_2 2 1 14.99909
## 33934_KVLGAFSDGLAHLDNLK_2 1 1 13.67786
## 66687_VQWKVDNALQSGNSQESVTEQDSK_3 1 4 14.64895
## 4753_AQLEFNQIK_2 3 3 16.09321
## 41700_LVSDEMVVELIEK_3 1 1 10.60251
## 37251_LINQPLPDLK_2 2 2 12.36430
## 31550_IYGISFPDPK_2 6 15 15.85513
## 22068_GIGWLPLGSLEAEK_2 1 1 12.88426
## 24075_GSPVNSLFVAPAVTPVK_2 1 2 14.46996
## 35436_LEEAGGATSVQIEMNK_2 1 1 13.20624
## 20000_FVMYMGSR_2 2 2 13.27329
## 14433_ELGSLNSYLEQLR_2 1 1 12.58906
## 40939_LTGAIMHFGNMK_3 1 1 14.32442
## 46225_NNLLQAELEELR_3 1 1 12.80458
## 67398_VTLVDVTR_2 1 1 14.53807
## 55973_SNNDFTTR_2 1 15 11.54652
## 34403_LAEQELIETSER_3 1 1 13.15626
## 28334_IGHENGEFQIFLDALDK_3 5 3 13.66499
## 6660_C(UniMod:4)AELEEELKTVTNNLK_3 1 2 15.31084
## 64899_VKLEQQVDDLEGSLEQEK_2 1 1 13.17800
## 53981_SFLEDIRK_2 1 2 13.54815
## 47692_QAFNPEGEFQR_2 3 1 14.08456
## 15506_EPMTVSSDQMAK_2 6 5 12.71842
## 32681_KKMEGDLNEMEIQLSHANR_3 3 1 13.28793
## 63014_VDFETFLPMLQAVAK_2 2 1 11.69094
## 14901_ELTYQTEEDRK_3 4 4 14.79463
## 17553_FALFDLAEGK_2 1 1 13.04934
## 56245_SPPNPDNIAPGYSGPLK_2 1 1 12.96978
## 21711_GGPLPGPYR_2 11 8 15.69085
## 39895_LQRELDEATESNEAMGR_3 2 1 13.58041
## 42066_LYNTLDDLLLYINAR_2 6 1 12.91463
## 40170_LRQFHLHWGSSDDHGSEHTVDGVK_5 1 1 13.04991
## 43379_MMDFETFLPMLQHISK_3 1 1 13.61242
## DECOY_41630_LVPQQLAH_2 3 1 12.36531
## 15178_ENALDRAEQAEAEQK_2 1 1 12.66429
## 5068_ASAGLLGAHAAAITAYALTLTK_4 1 5 10.61790
## 20268_GADPEETILNAFK_3 1 1 11.62262
## 40730_LSVDLKPLK_2 3 1 13.10355
## 29788_INATLETKQPR_3 1 1 13.31881
## 29124_ILASDKPYILAEELRR_4 1 1 14.14598
## 25706_HIAEVSQEVTR_2 6 19 13.87614
## 25160_HDC(UniMod:4)DLLR_2 1 2 13.98507
## 55441_SLLSSAENEPPVPLVSNWRPPQPINNR_3 1 1 15.31655
## 45026_NGLPVQESDRLK_2 2 1 12.28086
## 5963_AVEPQLQEEER_2 1 1 14.81545
## 4134_ANDDLKENIAIVER_3 1 1 16.95638
## 44558_NDLRDTFAALGR_3 3 1 13.36548
## 41242_LTYTQQLEDLK_2 1 1 13.38530
## 16840_EVENVAITALEGLK_2 2 1 12.87134
## 62153_TVYHAEEIQC(UniMod:4)NGR_3 2 1 12.66560
## 65022_VLAVNQENER_2 3 4 14.20498
## 70380_YQYLLTGR_2 1 4 16.12923
## 40345_LSEMTEQDQQR_2 1 1 12.20045
## DECOY_27515_IC(UniMod:4)YAINLGK_2 2 1 13.76474
## 27368_IAQLEEQVEQEAREK_3 3 1 13.81254
## 41081_LTPLLSVR_2 1 1 10.81343
## 38948_LNQALLDLHALGSAR_3 1 3 15.01255
## 15357_ENQSILITGESGAGK_2 3 6 14.00347
## 62665_VANEPVLAFTQGSPER_3 1 2 11.93390
## 70491_YSFSEDTK_2 1 1 14.50981
## 17227_EVYM(UniMod:35)PSSIFQDDFVIPDISEPGTWK_3 1 1 15.74012
## Exp.T.
## 14817_ELSDAPAFISQPR_2 15.48614
## 48333_QFTQSPETEASADSFPDTK_3 11.24079
## 45761_NLLASEEGITTSK_2 14.53167
## 37818_LLEGEEYR_2 15.21565
## 44557_NDLRDTFAALGR_2 15.28852
## DECOY_64509_VIEGDVVSALNK_3 19.71111
## 32415_KHGATVLTALGGILK_3 13.80097
## DECOY_57454_SVEETLR_2 15.36065
## 32647_KKEEEELVALK_2 15.15991
## 65608_VLSNMGAHFGGYLVK_3 17.11738
## 19578_FSKEEVDQMFAAFPPDVTGNLDYK_3 17.58212
## 14588_ELLQTELSGFLDAQK_3 14.76458
## 67193_VTAETEYGK_2 14.69912
## 53361_SDEHVVSGNLVTPLPVILGHEAAGIVESVGEGVTTVKPGDK_5 13.02360
## 67675_VVDSLQTSLDAETR_2 19.59816
## 16407_ETADTDTAEQVIASFR_3 16.50867
## 14440_ELGVGIALRK_2 12.83010
## 47150_NVEAMSGMEGR_2 15.59881
## 39476_LQDLVDK_2 15.79187
## 21876_GHFTEEDK_2 14.49889
## 48997_QLEDELAAMQK_2 17.39807
## 49509_QNANSQLSILFIEKPQGGTVK_3 14.99302
## 54162_SGEGQEDAGELDFSGLLK_2 15.17560
## 44660_NEEIDEMIK_2 18.75547
## DECOY_62709_VAPEEHPVLLTEATLNPK_3 15.06734
## 11725_EAFTIMDQNR_2 19.33717
## 36599_LGNQEPGGQTALK_2 10.60699
## 3412_ALGTNPTNAEVR_2 16.19902
## 25245_HEERQDEHGFISR_3 14.51241
## 42563_MEGDLNEMEIQLSHANR_3 17.50713
## 60831_TPLC(UniMod:4)GHC(UniMod:4)NNVIR_3 15.44072
## 2822_AISEELDHALNDMTSI_2 16.06158
## 48924_QKYEESQSELESSQK_2 16.29973
## 66566_VQLLHSQNTSLINQK_2 16.49424
## 38440_LLSSLDIDHNQYK_3 17.61781
## 24500_GVDEATIIDILTKR_3 15.18301
## 22634_GLGWSPAGSLEVEK_2 14.92990
## 29685_IMGEVMHALR_2 14.85379
## 37485_LKNAYEESLEHLETFKR_4 13.63412
## 34722_LASADIETYLLEK_2 18.12525
## 39778_LQNEIEDLMVDVER_2 16.45617
## 90_AADAEAEVASLNR_2 17.34938
## 14320_ELENELEAEQK_2 16.62258
## 34402_LAEQELIETSER_2 20.11633
## 33934_KVLGAFSDGLAHLDNLK_2 11.95602
## 66687_VQWKVDNALQSGNSQESVTEQDSK_3 13.08221
## 4753_AQLEFNQIK_2 18.31805
## 41700_LVSDEMVVELIEK_3 12.69505
## 37251_LINQPLPDLK_2 13.99721
## 31550_IYGISFPDPK_2 17.73884
## 22068_GIGWLPLGSLEAEK_2 15.69528
## 24075_GSPVNSLFVAPAVTPVK_2 12.02127
## 35436_LEEAGGATSVQIEMNK_2 16.01938
## 20000_FVMYMGSR_2 14.78792
## 14433_ELGSLNSYLEQLR_2 14.54777
## 40939_LTGAIMHFGNMK_3 18.90293
## 46225_NNLLQAELEELR_3 16.51071
## 67398_VTLVDVTR_2 17.05896
## 55973_SNNDFTTR_2 13.45520
## 34403_LAEQELIETSER_3 15.68007
## 28334_IGHENGEFQIFLDALDK_3 15.29583
## 6660_C(UniMod:4)AELEEELKTVTNNLK_3 13.64746
## 64899_VKLEQQVDDLEGSLEQEK_2 16.39526
## 53981_SFLEDIRK_2 16.04690
## 47692_QAFNPEGEFQR_2 16.64062
## 15506_EPMTVSSDQMAK_2 14.55185
## 32681_KKMEGDLNEMEIQLSHANR_3 16.02235
## 63014_VDFETFLPMLQAVAK_2 15.47394
## 14901_ELTYQTEEDRK_3 16.58149
## 17553_FALFDLAEGK_2 15.22884
## 56245_SPPNPDNIAPGYSGPLK_2 15.71064
## 21711_GGPLPGPYR_2 17.20332
## 39895_LQRELDEATESNEAMGR_3 15.70281
## 42066_LYNTLDDLLLYINAR_2 15.01271
## 40170_LRQFHLHWGSSDDHGSEHTVDGVK_5 15.27605
## 43379_MMDFETFLPMLQHISK_3 17.12975
## DECOY_41630_LVPQQLAH_2 10.36538
## 15178_ENALDRAEQAEAEQK_2 15.22968
## 5068_ASAGLLGAHAAAITAYALTLTK_4 12.18864
## 20268_GADPEETILNAFK_3 14.64898
## 40730_LSVDLKPLK_2 15.82963
## 29788_INATLETKQPR_3 16.50036
## 29124_ILASDKPYILAEELRR_4 16.10293
## 25706_HIAEVSQEVTR_2 15.61775
## 25160_HDC(UniMod:4)DLLR_2 16.36365
## 55441_SLLSSAENEPPVPLVSNWRPPQPINNR_3 18.30931
## 45026_NGLPVQESDRLK_2 15.29030
## 5963_AVEPQLQEEER_2 12.60978
## 4134_ANDDLKENIAIVER_3 13.11719
## 44558_NDLRDTFAALGR_3 16.63612
## 41242_LTYTQQLEDLK_2 17.07095
## 16840_EVENVAITALEGLK_2 14.87820
## 62153_TVYHAEEIQC(UniMod:4)NGR_3 15.06240
## 65022_VLAVNQENER_2 15.88091
## 70380_YQYLLTGR_2 13.83365
## 40345_LSEMTEQDQQR_2 15.18563
## DECOY_27515_IC(UniMod:4)YAINLGK_2 16.24658
## 27368_IAQLEEQVEQEAREK_3 17.19276
## 41081_LTPLLSVR_2 13.69203
## 38948_LNQALLDLHALGSAR_3 13.11571
## 15357_ENQSILITGESGAGK_2 15.55601
## 62665_VANEPVLAFTQGSPER_3 13.64105
## 70491_YSFSEDTK_2 12.44166
## 17227_EVYM(UniMod:35)PSSIFQDDFVIPDISEPGTWK_3 13.42729
## Cnt.N. Cnt.T. Exp.N. Exp.T.
## 14817_ELSDAPAFISQPR_2 1 2 15.22423 13.57467
## 17158_EVTTPPNQR_2 2 17 11.73000 13.45206
## 29387_ILLMDEDQDR_2 1 6 11.94145 13.47840
## 57688_SVTEQGAELSNEERNLLSVAYK_3 2 9 10.68528 12.29898
## 32859_KLELSENR_2 1 4 11.42533 12.97595
## 57689_SVTGFDSAHDTER_2 2 2 15.63186 13.76639
## DECOY_6907_C(UniMod:4)IIVEEGK_2 1 4 11.25711 12.88037
## 32042_KESYSIYVYK_3 1 1 14.56058 12.66289
## 9566_DLQARDEQNEEK_3 2 3 15.21937 13.64008
## 37089_LIEGLPEK_2 2 6 14.89546 16.89220
## 22818_GLSAEPGWQAK_2 1 2 16.94704 15.33621
## 48605_QHLLTNLVEVDGR_3 1 25 12.27212 13.85965
## 27084_HYINLITR_2 2 15 11.90750 15.32980
## 41754_LVTAIGDVVNHDPAVGDR_3 1 1 15.05892 12.94456
## 8845_DIQMTQSPSSLSATVGDR_2 1 2 15.21669 13.16938
## 38398_LLSFVDDEAFIRDVAK_3 1 2 14.46784 12.27316
## 51809_RLPENLYNDR_3 3 6 13.69462 15.62786
## 36942_LHPYKDFIATLGK_2 1 1 11.86941 10.36538
## 38070_LLIYDASSLESGVPSR_2 3 1 14.54336 12.26278
## DECOY_33827_KVAVPYSPAAGVDFELESFSER_3 11 1 17.85826 20.04579
## 30062_IPPVNTNLENLYLQGNR_2 1 8 11.23077 12.80947
## DECOY_59869_TLADLIR_2 2 3 14.75228 13.19287
## 29012_IKIEFTPEQIEEFK_2 2 2 13.95653 12.43708
## 20268_GADPEETILNAFK_3 1 1 15.22165 11.60865
## DECOY_48298_QFLPFLQR_2 1 2 14.17658 15.82766
## 58128_TAMDQALQWLEDK_2 1 1 12.98471 15.23875
## 25706_HIAEVSQEVTR_2 5 17 13.12712 14.76982
## 33460_KPVAAAAAAPAPAPAPAPAPAPAKPKEEK_4 1 1 15.47325 12.02738
## 1583_AEVVDEIAAK_2 4 13 14.30842 15.81035
## 25683_HIADLAGNPDLILPVPAFNVINGGSHAGNK_4 1 1 16.94630 14.45702
## 42985_MIGFPSSAGSVSPR_2 5 2 13.67120 15.18017
## 16758_EVAVGDSTGMAR_2 1 2 15.28121 13.35399
## DECOY_36731_LGTFEVEDQIEAAR_3 3 4 13.67394 15.77646
## 42709_MFNWMVTR_2 3 2 17.11068 14.06214
## 47560_PLSGTPAPNK_2 1 2 14.15010 12.08192
## Cnt.N. Cnt.T. Exp.N.
## 4026_AMGVDPQQK_2 1 2 15.16606
## 63649_VFAIPPSFASIFLTK_3 6 3 12.45935
## 25070_HALIIYDDLSK_3 1 1 12.42276
## 41107_LTQESIMDLENDKQQLDER_3 4 1 14.36574
## 39224_LPNTLKPDSYR_3 2 3 15.98345
## DECOY_57454_SVEETLR_2 1 1 15.55399
## 11046_DVFLGM(UniMod:35)FLYEYAR_2 1 1 14.96667
## 31133_ITYGETGGNSPVQEFTVPGSK_3 1 1 11.70581
## 17392_EYSLQVLK_2 3 13 10.69769
## 48336_QFTSSTSYNR_2 4 1 12.37042
## 31010_ITLSQVGDVLR_2 3 7 17.41797
## 19939_FVFSEVNGDR_2 10 6 15.13197
## 15837_EQQQLIDDHFLFDKPVSPLLLASGMAR_4 5 2 14.25507
## 63622_VEVVDEER_2 2 1 14.80913
## 3413_ALGTNPTNAEVRK_2 1 2 15.27489
## 69184_YENEVALR_2 3 1 13.87956
## DECOY_55312_SLGSADELFK_2 1 1 17.69619
## 20576_GAVPWYTINLDLPPYK_2 1 3 15.54366
## 42563_MEGDLNEMEIQLSHANR_3 3 1 15.00070
## 28435_IGQLLVC(UniMod:4)NC(UniMod:4)IFK_2 9 5 15.40239
## 21907_GHPETLEK_2 6 11 16.88875
## 47651_QAEEAEEQANTNLSK_2 4 1 14.69613
## 37089_LIEGLPEK_2 3 12 15.27158
## 29284_ILGILALIDEGETDWK_2 1 2 10.76915
## 44664_NEFGEIQGDK_2 1 2 15.94871
## 32720_KLAEQELIETSER_3 4 10 17.31380
## 19804_FTITGLPTDAK_2 1 1 13.93152
## 24249_GTFATLSELHC(UniMod:4)DKLHVDPENFR_4 1 1 13.50330
## 13963_EIVDSYLPVILDIIK_3 2 1 12.67266
## 39778_LQNEIEDLMVDVER_2 4 2 15.37948
## 9449_DLLLPQPDLR_2 5 1 12.28652
## 33694_KSPSPEFSGMPR_3 2 21 13.03000
## DECOY_9588_DLQIYSR_2 4 2 15.10767
## 60893_TPSDHYNWQATLQNESGK_3 7 3 14.92575
## 44298_NAKPSEYEK_2 1 1 14.16828
## 2712_AILEQNFR_2 3 3 15.06158
## 52474_RSSPETLISDLLMR_3 2 12 11.61459
## 59759_TKAEPAVPQAPQK_2 3 1 14.25410
## 44817_NFGPTGIGFGGLTQQVEK_2 1 1 13.65209
## 3756_ALSNAANLQR_2 1 1 12.13378
## 32681_KKMEGDLNEMEIQLSHANR_3 2 1 13.87373
## 12596_EEKPAVTAAPK_2 2 1 11.66834
## 10274_DQSPGKGTLEDQIIQANPALEAFGNAK_3 1 1 14.70927
## 1772_AFNADFH_2 3 20 12.01637
## 45010_NGIPLELR_2 1 1 14.34923
## 50058_QSPVDIDTHTAK_3 2 1 13.28051
## 63240_VDVIPVNLPGEHGQR_2 1 1 13.65066
## 60396_TLTQGGVTYR_2 1 3 14.30323
## 12060_EATIPGHLNSYTIK_3 2 1 14.64899
## 12629_EELLEYEEK_2 7 10 16.88183
## 62247_TYINSLAILDDEPVIR_3 9 1 14.30343
## 1738_AFLDELK_2 1 4 12.89732
## 57098_STATISGLKPGVDYTITVYAVTGR_3 1 1 12.66149
## 25706_HIAEVSQEVTR_2 3 23 13.56559
## 15522_EPQVYTLPPSRDELTK_3 4 2 15.66726
## 54752_SIMLVAHR_2 1 1 13.30116
## 28265_IGEDFISDLDQLRK_3 1 2 15.53469
## 68303_VYTFEMQIIK_2 1 1 12.85377
## 39942_LQTEAGELSR_2 2 2 15.39605
## 1583_AEVVDEIAAK_2 10 17 14.40476
## 14991_EMAIPVLEAR_2 1 18 12.48168
## 25683_HIADLAGNPDLILPVPAFNVINGGSHAGNK_4 1 1 16.36157
## DECOY_27515_IC(UniMod:4)YAINLGK_2 6 2 13.06304
## 25710_HIAQLAGNSDLILPVPAFNVINGGSHAGNK_4 1 1 15.17485
## 6819_C(UniMod:4)GDLEEELK_2 1 4 15.95899
## 67390_VTLRPYLTPNDR_2 2 2 13.95613
## 58643_TEHYEEQIEAFK_3 1 2 12.08578
## 33618_KREEAPSLRPAPPPISGGGYR_3 1 1 12.61993
## 17227_EVYM(UniMod:35)PSSIFQDDFVIPDISEPGTWK_3 1 4 17.71579
## 19155_FNTTAVPK_2 2 1 13.02040
## Exp.T.
## 4026_AMGVDPQQK_2 13.66296
## 63649_VFAIPPSFASIFLTK_3 13.96807
## 25070_HALIIYDDLSK_3 14.05608
## 41107_LTQESIMDLENDKQQLDER_3 16.80565
## 39224_LPNTLKPDSYR_3 14.24928
## DECOY_57454_SVEETLR_2 13.73576
## 11046_DVFLGM(UniMod:35)FLYEYAR_2 13.19192
## 31133_ITYGETGGNSPVQEFTVPGSK_3 15.03099
## 17392_EYSLQVLK_2 12.27476
## 48336_QFTSSTSYNR_2 14.34881
## 31010_ITLSQVGDVLR_2 15.91734
## 19939_FVFSEVNGDR_2 13.22414
## 15837_EQQQLIDDHFLFDKPVSPLLLASGMAR_4 15.76905
## 63622_VEVVDEER_2 16.72759
## 3413_ALGTNPTNAEVRK_2 13.38345
## 69184_YENEVALR_2 11.41040
## DECOY_55312_SLGSADELFK_2 16.15169
## 20576_GAVPWYTINLDLPPYK_2 13.71348
## 42563_MEGDLNEMEIQLSHANR_3 16.78564
## 28435_IGQLLVC(UniMod:4)NC(UniMod:4)IFK_2 13.30505
## 21907_GHPETLEK_2 15.20600
## 47651_QAEEAEEQANTNLSK_2 16.49793
## 37089_LIEGLPEK_2 17.76323
## 29284_ILGILALIDEGETDWK_2 12.44139
## 44664_NEFGEIQGDK_2 14.33985
## 32720_KLAEQELIETSER_3 15.49665
## 19804_FTITGLPTDAK_2 15.59337
## 24249_GTFATLSELHC(UniMod:4)DKLHVDPENFR_4 15.45200
## 13963_EIVDSYLPVILDIIK_3 11.11464
## 39778_LQNEIEDLMVDVER_2 16.89789
## 9449_DLLLPQPDLR_2 13.82700
## 33694_KSPSPEFSGMPR_3 14.54007
## DECOY_9588_DLQIYSR_2 13.48825
## 60893_TPSDHYNWQATLQNESGK_3 13.11150
## 44298_NAKPSEYEK_2 11.95280
## 2712_AILEQNFR_2 13.48104
## 52474_RSSPETLISDLLMR_3 13.90326
## 59759_TKAEPAVPQAPQK_2 16.15606
## 44817_NFGPTGIGFGGLTQQVEK_2 15.40685
## 3756_ALSNAANLQR_2 13.95619
## 32681_KKMEGDLNEMEIQLSHANR_3 15.39034
## 12596_EEKPAVTAAPK_2 14.18591
## 10274_DQSPGKGTLEDQIIQANPALEAFGNAK_3 16.70223
## 1772_AFNADFH_2 13.55898
## 45010_NGIPLELR_2 16.62879
## 50058_QSPVDIDTHTAK_3 15.11296
## 63240_VDVIPVNLPGEHGQR_2 15.75739
## 60396_TLTQGGVTYR_2 12.77056
## 12060_EATIPGHLNSYTIK_3 17.55058
## 12629_EELLEYEEK_2 18.43416
## 62247_TYINSLAILDDEPVIR_3 12.27299
## 1738_AFLDELK_2 14.75466
## 57098_STATISGLKPGVDYTITVYAVTGR_3 16.59746
## 25706_HIAEVSQEVTR_2 15.28276
## 15522_EPQVYTLPPSRDELTK_3 14.14484
## 54752_SIMLVAHR_2 16.76713
## 28265_IGEDFISDLDQLRK_3 13.91511
## 68303_VYTFEMQIIK_2 14.90262
## 39942_LQTEAGELSR_2 13.48944
## 1583_AEVVDEIAAK_2 16.08798
## 14991_EMAIPVLEAR_2 14.30580
## 25683_HIADLAGNPDLILPVPAFNVINGGSHAGNK_4 14.86087
## DECOY_27515_IC(UniMod:4)YAINLGK_2 14.60049
## 25710_HIAQLAGNSDLILPVPAFNVINGGSHAGNK_4 13.66251
## 6819_C(UniMod:4)GDLEEELK_2 14.22401
## 67390_VTLRPYLTPNDR_2 12.12764
## 58643_TEHYEEQIEAFK_3 14.04214
## 33618_KREEAPSLRPAPPPISGGGYR_3 15.30274
## 17227_EVYM(UniMod:35)PSSIFQDDFVIPDISEPGTWK_3 14.99307
## 19155_FNTTAVPK_2 14.77052
## Cnt.N. Cnt.T. Exp.N.
## 47963_QDGSVDFGRK_2 1 3 12.73310
## 29443_ILNPAAIPEGQFIDSR_3 4 1 16.09729
## DECOY_47837_QASVFSFGLGAQAASR_2 1 2 13.66291
## 15485_EPISVSSEQVLK_2 1 3 12.54811
## 61261_TSETKHDTSLKPISVSYNPATAK_4 1 4 11.84815
## 33808_KVADALTNAVAHVDDMPNALSALSDLHAHK_3 1 10 12.78294
## 22674_GLLALSLK_2 2 4 14.77769
## 67675_VVDSLQTSLDAETR_2 1 1 16.02031
## 58902_TFIIGELHPDDRPK_3 1 4 13.85012
## 46226_NNLLQAELEELRAVVEQTER_3 2 1 14.82874
## 29442_ILNPAAIPEGQFIDSR_2 6 3 16.39666
## 22834_GLSLSALQDLC(UniMod:4)R_2 4 1 14.60957
## 39476_LQDLVDK_2 6 1 15.90854
## 15974_ERIDNLIDPGSPFLELSQFAGYQLYDNEEVPGGGIITGIGR_4 1 9 12.11396
## 39504_LQEAAELEAVELPVPIR_2 1 4 10.93769
## 62241_TYHVGEQWQK_3 1 2 12.86999
## 11725_EAFTIMDQNR_2 1 1 17.61109
## 63644_VFAENKEIQK_3 1 6 12.34672
## 44407_NAVHVNLFETPVEAQYVR_3 1 1 11.94090
## 32664_KKHADSVAELGEQIDNLQR_4 4 2 14.36315
## 11860_EALISQLTR_2 8 7 17.00707
## 21907_GHPETLEK_2 12 13 15.45870
## 4245_ANSEVAQWR_2 1 2 16.95854
## 34155_KYPLLLDVYAGPC(UniMod:4)SQK_3 2 5 12.10909
## 54975_SKRPIPISTTAPPVQTPLPVIPHQK_5 2 2 15.08793
## 38440_LLSSLDIDHNQYK_3 5 4 16.71519
## DECOY_11135_DVLSVAFSSDNR_2 6 4 15.19266
## 11347_DYAFPYPQDC(UniMod:4)NPR_2 3 1 13.25945
## 44494_NDEELNKLLGK_3 1 2 14.98904
## 45977_NLTEEMAGLDEIIAK_2 4 6 15.71406
## DECOY_35635_LELSVDGAK_2 2 1 14.07560
## 34402_LAEQELIETSER_2 1 2 16.78111
## 24493_GVDAQGTLSK_2 1 2 14.79456
## 58644_TEIDKPSQMQVTDVQDNSISVK_3 1 1 12.42460
## 33712_KSVVWENK_2 6 6 13.17720
## 55438_SLLSNVEGDNAVPMQHNNRPTQPLK_3 3 5 11.69480
## 24553_GVGGSQPPDIDK_2 5 1 13.11181
## 57227_STLLELFGQIER_2 1 1 15.34053
## 34501_LALDIEIATYRK_3 1 5 14.28987
## 4056_AMLWLSEK_2 1 1 10.92024
## DECOY_48650_QIAWEQK_2 4 2 14.23056
## 3355_ALFQLSR_2 2 1 13.72977
## 46225_NNLLQAELEELR_3 5 3 14.69915
## 42075_LYPIANGNNQSPVDIK_2 2 6 12.99967
## 8258_DGKLVSESSDVLPK_3 2 2 13.37205
## 23663_GQNVQQVIYATGALAK_2 5 3 15.05943
## DECOY_46931_NSWGEEWGMGGYVK_2 2 1 16.51422
## 44061_MVSDIAGAWQR_2 1 1 15.14534
## 35100_LDGSVDFK_2 3 3 12.75145
## 10155_DQASQLLAGTEATLGHAK_2 4 1 12.73311
## 36196_LFYSTFATDDRK_3 1 6 12.88615
## DECOY_12541_EEGFPIRPHYFWPLLVGR_4 1 1 13.43301
## 27800_IEELEEEIEAER_2 6 5 16.02040
## 67409_VTMQNLNDR_2 10 5 14.80162
## 60396_TLTQGGVTYR_2 1 1 13.90047
## 58500_TDTGVSLQTYDDLLAK_2 1 10 17.12315
## 25706_HIAEVSQEVTR_2 9 24 13.17515
## 14802_ELQVLHIDFLNQDNAVSHHTWEFQTSSPVFR_4 1 1 15.60945
## 8005_DFLNQEGADPDSIEMVATR_3 1 1 12.59327
## 62346_VAAGAFQGLR_2 2 2 12.83881
## 33827_KVAVPYSPAAGVDFELESFSER_3 9 5 16.23014
## 39117_LPEGDLGK_2 1 2 11.78145
## 33925_KVIQYLAVVASSHK_3 1 1 14.14455
## 5963_AVEPQLQEEER_2 1 7 14.52340
## 39942_LQTEAGELSR_2 2 1 13.72549
## 36047_LFPDFFTR_2 3 1 13.98223
## 1583_AEVVDEIAAK_2 6 28 14.16644
## 16988_EVLGSATR_2 2 31 12.41848
## 36155_LFVGNLPADITEDEFK_3 1 6 12.94457
## 1722_AFIPPAPVLPSR_2 1 3 11.50839
## DECOY_55884_SNDPVATAFAEMLK_2 1 6 14.60741
## 17227_EVYM(UniMod:35)PSSIFQDDFVIPDISEPGTWK_3 1 1 16.07834
## Exp.T.
## 47963_QDGSVDFGRK_2 14.73855
## 29443_ILNPAAIPEGQFIDSR_3 13.69772
## DECOY_47837_QASVFSFGLGAQAASR_2 15.67916
## 15485_EPISVSSEQVLK_2 15.27980
## 61261_TSETKHDTSLKPISVSYNPATAK_4 13.63053
## 33808_KVADALTNAVAHVDDMPNALSALSDLHAHK_3 14.36909
## 22674_GLLALSLK_2 12.90083
## 67675_VVDSLQTSLDAETR_2 14.39250
## 58902_TFIIGELHPDDRPK_3 15.37851
## 46226_NNLLQAELEELRAVVEQTER_3 11.93842
## 29442_ILNPAAIPEGQFIDSR_2 14.81491
## 22834_GLSLSALQDLC(UniMod:4)R_2 12.82143
## 39476_LQDLVDK_2 17.89886
## 15974_ERIDNLIDPGSPFLELSQFAGYQLYDNEEVPGGGIITGIGR_4 13.82519
## 39504_LQEAAELEAVELPVPIR_2 14.06104
## 62241_TYHVGEQWQK_3 15.12822
## 11725_EAFTIMDQNR_2 14.51172
## 63644_VFAENKEIQK_3 14.20960
## 44407_NAVHVNLFETPVEAQYVR_3 15.72389
## 32664_KKHADSVAELGEQIDNLQR_4 12.74001
## 11860_EALISQLTR_2 15.42749
## 21907_GHPETLEK_2 13.85889
## 4245_ANSEVAQWR_2 14.02425
## 34155_KYPLLLDVYAGPC(UniMod:4)SQK_3 13.84642
## 54975_SKRPIPISTTAPPVQTPLPVIPHQK_5 13.40258
## 38440_LLSSLDIDHNQYK_3 14.71678
## DECOY_11135_DVLSVAFSSDNR_2 16.70377
## 11347_DYAFPYPQDC(UniMod:4)NPR_2 11.36659
## 44494_NDEELNKLLGK_3 12.74283
## 45977_NLTEEMAGLDEIIAK_2 13.62710
## DECOY_35635_LELSVDGAK_2 15.98854
## 34402_LAEQELIETSER_2 14.18057
## 24493_GVDAQGTLSK_2 12.94874
## 58644_TEIDKPSQMQVTDVQDNSISVK_3 15.48071
## 33712_KSVVWENK_2 15.78482
## 55438_SLLSNVEGDNAVPMQHNNRPTQPLK_3 13.59477
## 24553_GVGGSQPPDIDK_2 11.44838
## 57227_STLLELFGQIER_2 12.92387
## 34501_LALDIEIATYRK_3 16.03634
## 4056_AMLWLSEK_2 12.69975
## DECOY_48650_QIAWEQK_2 12.47090
## 3355_ALFQLSR_2 16.19554
## 46225_NNLLQAELEELR_3 12.90172
## 42075_LYPIANGNNQSPVDIK_2 14.63909
## 8258_DGKLVSESSDVLPK_3 15.45564
## 23663_GQNVQQVIYATGALAK_2 13.01721
## DECOY_46931_NSWGEEWGMGGYVK_2 14.38803
## 44061_MVSDIAGAWQR_2 12.78095
## 35100_LDGSVDFK_2 15.08692
## 10155_DQASQLLAGTEATLGHAK_2 14.33540
## 36196_LFYSTFATDDRK_3 14.55033
## DECOY_12541_EEGFPIRPHYFWPLLVGR_4 14.96570
## 27800_IEELEEEIEAER_2 14.15992
## 67409_VTMQNLNDR_2 12.60971
## 60396_TLTQGGVTYR_2 12.26191
## 58500_TDTGVSLQTYDDLLAK_2 14.40251
## 25706_HIAEVSQEVTR_2 14.94044
## 14802_ELQVLHIDFLNQDNAVSHHTWEFQTSSPVFR_4 13.59674
## 8005_DFLNQEGADPDSIEMVATR_3 14.15514
## 62346_VAAGAFQGLR_2 14.37748
## 33827_KVAVPYSPAAGVDFELESFSER_3 14.65975
## 39117_LPEGDLGK_2 13.56594
## 33925_KVIQYLAVVASSHK_3 16.73695
## 5963_AVEPQLQEEER_2 12.76709
## 39942_LQTEAGELSR_2 15.58754
## 36047_LFPDFFTR_2 11.90896
## 1583_AEVVDEIAAK_2 16.01213
## 16988_EVLGSATR_2 13.99382
## 36155_LFVGNLPADITEDEFK_3 15.26448
## 1722_AFIPPAPVLPSR_2 13.33844
## DECOY_55884_SNDPVATAFAEMLK_2 16.26361
## 17227_EVYM(UniMod:35)PSSIFQDDFVIPDISEPGTWK_3 14.22992
## Cnt.N. Cnt.T. Exp.N. Exp.T.
## 37818_LLEGEEYR_2 1 2 12.98544 15.11923
## 29443_ILNPAAIPEGQFIDSR_3 4 5 14.25199 16.20105
## 65660_VLTLELYK_2 3 5 13.02871 14.73775
## 52723_RVIQYFAVIAAIGDR_3 1 1 15.30203 12.91121
## 32202_KGDIFIVYDTR_3 3 1 16.71242 13.83548
## 59969_TLEDQMNEHR_3 2 5 13.07838 15.66174
## 3382_ALGITEIFIK_2 1 2 14.29177 11.96143
## 11330_DWSFYLLYYTEFTPTEKDEYAC(UniMod:4)R_3 1 1 14.95810 13.13079
## 19578_FSKEEVDQMFAAFPPDVTGNLDYK_3 1 4 16.08913 13.51306
## 41526_LVLNQPLQSYHQLK_3 3 3 16.70010 14.67164
## 11501_DYTGEDVTPQNFLAVLR_2 2 9 11.99503 13.67453
## 57596_SVNDLTSQR_2 2 5 15.44427 17.07741
## 32644_KKDFELNALNAR_3 1 1 14.38863 12.88857
## 67193_VTAETEYGK_2 1 3 14.28182 16.14689
## DECOY_23701_GQSEDPGSLLSLFR_2 3 1 16.25691 14.57850
## 51734_RLHPYKDFIATLGK_3 1 1 15.30700 13.66783
## 2146_AGLLGLLEEMR_2 4 5 14.31399 16.06564
## 66388_VPSHAVVAR_2 3 1 15.46984 12.99869
## 21703_GGPFSDSYR_2 1 2 15.96812 14.07730
## 68260_VYLTFDELR_2 1 3 14.58306 12.95871
## 16184_ESISVSSEQLAQFR_2 1 1 13.26808 11.68271
## 34335_LAEAQIEELR_2 1 2 13.51986 15.26329
## 67553_VTWEPAPGEVK_2 1 6 11.30195 13.64654
## 57690_SVTGFDSAHDTER_3 3 3 16.42424 13.82562
## 69521_YGVDIPEDENEK_2 3 3 12.00793 13.88619
## 29442_ILNPAAIPEGQFIDSR_2 5 5 15.23799 17.74530
## 9775_DMASNYK_2 3 2 13.18863 15.97513
## 20728_GDIFIVYDTR_2 3 2 15.40863 13.72294
## 944_ADIAESQVNK_2 2 3 14.31014 16.75012
## 40128_LRLEIPISGEPPPK_3 3 5 13.69061 15.20017
## 5665_ATLPEADGER_2 2 5 13.64459 15.49852
## 29013_IKIEFTPEQIEEFK_3 1 1 15.42990 13.90666
## 29536_ILTATIENNR_2 3 2 14.60919 16.87589
## 53659_SEFKLELDDVTSNMEQIIK_3 1 5 12.77023 14.79882
## 35852_LEYGGLGR_2 2 1 13.97425 15.83179
## 15069_EMLTTQAER_2 1 2 13.30705 15.85677
## 3412_ALGTNPTNAEVR_2 2 5 13.69961 15.40183
## 64130_VGNEYVTK_2 1 3 14.89967 16.98436
## 16518_ETGVPIAGR_2 1 1 11.59799 13.52076
## 11860_EALISQLTR_2 5 5 15.33742 17.72257
## DECOY_34734_LASDLLEWIRR_3 1 2 14.46417 16.11860
## 65596_VLSIWEER_2 2 1 14.54145 17.79882
## 4245_ANSEVAQWR_2 4 5 15.22134 16.85102
## 54975_SKRPIPISTTAPPVQTPLPVIPHQK_5 1 3 15.00805 13.48413
## 46018_NLVHIITHGEEKD_3 1 4 16.70020 14.74620
## 41106_LTQESIMDLENDK_2 2 5 13.56402 15.65534
## 6370_AVVISGAGK_2 1 2 11.56954 13.82661
## 63962_VGDTYERPK_2 1 1 11.71733 13.25461
## 53243_SAYLMGLNSADLLK_2 3 5 14.29822 16.18852
## 38440_LLSSLDIDHNQYK_3 3 5 15.06277 16.90115
## 48216_QEVPLATLEPLVK_2 1 2 14.22554 12.45075
## DECOY_41337_LVEDMENK_2 2 1 16.62877 18.69580
## 59627_TINEVETQILTR_2 4 5 13.30222 15.27235
## 17128_EVSTNTAMIQTSK_2 1 2 12.38958 15.09690
## 39778_LQNEIEDLMVDVER_2 3 5 13.44481 15.38212
## 45977_NLTEEMAGLDEIIAK_2 4 5 13.85229 15.76707
## 34402_LAEQELIETSER_2 3 5 15.38984 16.96778
## 35515_LEFTAHVLSQK_2 1 2 12.52430 14.73406
## 45612_NLDLDSIIAEVR_2 4 1 12.63591 15.54887
## 49239_QLNGSER_2 1 1 12.69113 10.94872
## 27084_HYINLITR_2 1 1 14.36085 17.06335
## 4753_AQLEFNQIK_2 4 5 15.50243 17.22103
## 8638_DIDDLELTLAK_2 3 5 15.02537 16.98042
## 55635_SLSTELFK_2 3 5 15.64694 17.38174
## 45679_NLFETPILAR_2 4 2 13.12532 14.77224
## 50189_QTLEKENADLAGELR_2 2 1 14.34415 12.32940
## 50421_QVMDHAFLLLSSER_3 3 6 16.54973 14.09112
## 18737_FLFVDKNFINSPVAQADWAAK_3 2 1 15.16547 13.20531
## 62348_VAAGASESTR_2 1 2 13.09748 14.59873
## 64274_VGWELLLTTIAR_2 2 5 12.94490 14.68460
## 33205_KMEGDLNEMEIQLSHANR_4 1 4 13.10660 15.17294
## 40939_LTGAIMHFGNMK_3 4 5 13.86439 15.58707
## 9929_DNGIRPSSLEQMAK_3 1 1 12.77622 14.45550
## 676_AAVEQLTEEQK_2 3 5 13.19966 15.17547
## 66168_VNVKNEEIDEMIK_3 1 2 16.61285 14.24778
## 35516_LEFTAHVLSQK_3 6 4 13.39062 14.95010
## 64731_VIQYFAVIAAIGDR_2 1 4 12.39176 14.60871
## 7927_DFELNALNAR_2 4 5 15.43534 17.47027
## 31142_IVADKDYSVTANSK_3 2 1 14.29752 10.97417
## 11780_EAGVFVPR_2 1 6 13.01375 14.89025
## 15506_EPMTVSSDQMAK_2 3 4 12.26458 14.32273
## 3680_ALQEAHQQALDDLQAEEDK_3 4 5 14.05125 16.07913
## 37789_LLEEGVLR_2 1 3 12.41836 14.67716
## 18509_FIPFIGVVK_2 2 1 10.56672 12.60065
## 8926_DITYEYKYPEGSSEER_3 3 4 15.39034 13.32007
## 48326_QFSSADEAALKEPIIK_3 6 9 12.92505 14.53724
## 2134_AGLENTVAETEC(UniMod:4)R_2 1 2 12.60990 14.93269
## 43379_MMDFETFLPMLQHISK_3 1 2 12.27049 14.05781
## 11058_DVFVPDDKQEFVK_3 3 5 15.81177 17.32700
## 67287_VTFLGLQHWVPELAR_3 3 1 12.52663 15.05253
## 12629_EELLEYEEK_2 2 1 17.41493 19.62197
## 50190_QTLEKENADLAGELR_3 1 1 17.05137 14.81394
## 39944_LQTENGELSR_2 2 3 14.59699 17.09673
## 15255_ENIAIVER_2 5 5 15.79828 18.18185
## 16188_ESIVSQLSR_2 1 1 13.19584 15.17405
## 65041_VLDELTLSK_2 4 2 14.32374 16.86230
## 31247_IVGDLAQFMVQNGLSR_2 3 1 16.44761 12.97693
## 21048_GENQSPVELHTK_2 1 2 11.83405 13.89707
## 15904_EQYEAMAER_2 2 2 12.94006 15.36833
## 22186_GIQGPQGPR_2 1 7 13.25338 16.46176
## 65022_VLAVNQENER_2 1 5 13.08297 15.20807
## 20267_GADPEETILNAFK_2 5 5 14.92805 16.91827
## 69617_YHVLFLGTDR_3 1 2 15.81545 13.95277
## 6532_AYGQALAK_2 1 2 13.43919 15.00899
## 42985_MIGFPSSAGSVSPR_2 2 2 14.17807 16.63895
## 50802_RATQIPSYKK_2 3 1 13.24456 11.51234
## 46224_NNLLQAELEELR_2 3 5 15.56591 17.31827
## 16758_EVAVGDSTGMAR_2 1 5 12.53178 14.34482
## 3398_ALGQNPTQAEVLR_2 5 5 14.00724 15.57266
## 37635_LLC(UniMod:4)GATLIAPR_2 1 1 14.50951 16.38565
## 23589_GQGTYEDYLEGFR_2 1 1 15.55810 13.69267
## 42709_MFNWMVTR_2 4 5 14.02272 16.34906
## 10837_DTQIQLDDAVR_2 4 5 15.35593 17.04825
## Cnt.N. Cnt.T.
## 31923_KDLYANNVMSGGTTMYPGIADR_3 1 1
## 58588_TEELEEAKK_2 1 1
## 7426_DAGEGLLAVQITDPEGKPKKTHIQDNHDGTYTVAYVPDVTGR_5 1 1
## 3009_AKPALEDLRQGLLPVLESFK_4 1 2
## 34741_LASELLEWIR_2 4 1
## 65660_VLTLELYK_2 4 1
## 44846_NFINSPVAQADWAAKR_3 1 2
## 59969_TLEDQMNEHR_3 5 1
## 28349_IGILITDGKSQDDIIPPSR_3 1 1
## 51383_RIDITLSSVK_2 1 2
## 55149_SLEAQADK_2 4 1
## 63863_VFSNGADLSGVTEEAPLKLSK_3 1 1
## 53058_SAITPGGLR_2 1 5
## 66931_VSGLGEKVDVGKDQEFTVK_3 1 1
## 26306_HPGDFGADAQGAMNK_2 2 1
## 22834_GLSLSALQDLC(UniMod:4)R_2 4 1
## 56483_SQIQAPNPK_2 2 1
## 39242_LPPPFPLK_2 1 2
## 37403_LKDLEALLNSK_3 3 2
## 57689_SVTGFDSAHDTER_2 2 1
## 53659_SEFKLELDDVTSNMEQIIK_3 3 1
## 51295_RGSIGENQVEVMVEEK_3 6 1
## 32789_KLDVTIEPSEEPLFPADELYGIVGANLKR_4 1 4
## 3412_ALGTNPTNAEVR_2 5 1
## 11342_DYAEVGR_2 4 1
## 9226_DLEKPFLLPVEAVYSVPGR_3 1 1
## 41106_LTQESIMDLENDK_2 2 1
## 53243_SAYLMGLNSADLLK_2 4 1
## 31253_IVGGWEC(UniMod:4)EKHSQPWQVLVASR_4 1 3
## 17128_EVSTNTAMIQTSK_2 3 2
## 39778_LQNEIEDLMVDVER_2 3 1
## 66816_VSADAMLR_2 3 1
## 34402_LAEQELIETSER_2 7 2
## 24321_GTLEDQIIQANPALEAFGNAK_3 2 1
## 20848_GDTTNTVLQGLK_2 1 8
## 11253_DVTVLQNTDGNNNEAWAK_2 2 2
## 11706_EAFMLFDR_2 1 1
## 57928_SYTVAIAGYALAQMGR_2 2 1
## 46225_NNLLQAELEELR_3 4 1
## 676_AAVEQLTEEQK_2 3 1
## 64731_VIQYFAVIAAIGDR_2 5 1
## 10233_DQLLPPSPNNR_2 1 3
## 63678_VFDEFKPLVEEPQNLIKQNC(UniMod:4)ELFEQLGEYKFQNALLVR_5 2 2
## 25816_HITVVELVGVFPTLIGR_3 1 1
## 44061_MVSDIAGAWQR_2 3 1
## 35048_LDELRDEGKASSAK_3 1 1
## 53686_SEIAELNR_2 3 4
## 65255_VLGNPKSDEMNVK_3 1 1
## 48821_QITASSSYK_2 2 1
## 34357_LAEKDEEMEQAK_3 1 1
## 4032_AMKDEEKMEIQEMQLK_4 2 1
## 11058_DVFVPDDKQEFVK_3 6 1
## 39333_LPVVLANGQIR_2 2 1
## DECOY_17906_FEEILTR_2 2 1
## 12060_EATIPGHLNSYTIK_3 5 1
## 58077_TAIQAAGYPDK_2 1 1
## 28917_IISTTTLNK_2 3 4
## 1745_AFLEVLVK_2 1 9
## 25706_HIAEVSQEVTR_2 1 5
## 15255_ENIAIVER_2 6 2
## 26425_HQGVMVGMGQKDSYVGDEAQSK_3 1 1
## 55441_SLLSSAENEPPVPLVSNWRPPQPINNR_3 7 2
## 50420_QVMDHAFLLLSSER_2 2 3
## 5963_AVEPQLQEEER_2 1 3
## 65401_VLLPLIFR_2 1 1
## 15586_EQDTSAHLER_2 3 1
## 59968_TLEDQMNEHR_2 5 1
## 41242_LTYTQQLEDLK_2 2 1
## 22186_GIQGPQGPR_2 2 7
## 65022_VLAVNQENER_2 5 1
## 44083_MVTAVASALSSR_2 4 1
## 60999_TQEPPVDLSK_2 4 1
## 11452_DYLHLPPEIVPATLR_3 1 1
## 42985_MIGFPSSAGSVSPR_2 3 4
## 15357_ENQSILITGESGAGK_2 3 1
## 33813_KVADALTNAVAHVDDM(UniMod:35)PNALSALSDLHAHK_5 1 1
## 45894_NLQEEISDLTEQLGSSGK_2 4 1
## 3398_ALGQNPTQAEVLR_2 6 1
## 62150_TVYFAEK_2 1 1
## 26427_HQGVMVGMGQKDSYVGDEAQSK_4 1 2
## Exp.N. Exp.T.
## 31923_KDLYANNVMSGGTTMYPGIADR_3 15.79190 13.93328
## 58588_TEELEEAKK_2 11.16858 14.47268
## 7426_DAGEGLLAVQITDPEGKPKKTHIQDNHDGTYTVAYVPDVTGR_5 14.86324 12.74410
## 3009_AKPALEDLRQGLLPVLESFK_4 14.62710 12.46995
## 34741_LASELLEWIR_2 14.07198 16.50088
## 65660_VLTLELYK_2 14.40370 15.92865
## 44846_NFINSPVAQADWAAKR_3 15.62169 13.97514
## 59969_TLEDQMNEHR_3 14.54227 17.09599
## 28349_IGILITDGKSQDDIIPPSR_3 15.25545 13.24998
## 51383_RIDITLSSVK_2 14.50477 12.23940
## 55149_SLEAQADK_2 13.61094 16.44366
## 63863_VFSNGADLSGVTEEAPLKLSK_3 16.04010 14.11092
## 53058_SAITPGGLR_2 12.32218 13.88691
## 66931_VSGLGEKVDVGKDQEFTVK_3 13.51260 11.79221
## 26306_HPGDFGADAQGAMNK_2 13.48631 15.05527
## 22834_GLSLSALQDLC(UniMod:4)R_2 13.65271 15.33680
## 56483_SQIQAPNPK_2 15.05387 16.80754
## 39242_LPPPFPLK_2 15.40675 12.76485
## 37403_LKDLEALLNSK_3 13.31497 15.25704
## 57689_SVTGFDSAHDTER_2 14.67418 12.74277
## 53659_SEFKLELDDVTSNMEQIIK_3 14.43719 16.56833
## 51295_RGSIGENQVEVMVEEK_3 15.52886 13.96321
## 32789_KLDVTIEPSEEPLFPADELYGIVGANLKR_4 12.82710 14.45298
## 3412_ALGTNPTNAEVR_2 14.35426 16.10666
## 11342_DYAEVGR_2 14.57841 13.05984
## 9226_DLEKPFLLPVEAVYSVPGR_3 12.74068 15.06378
## 41106_LTQESIMDLENDK_2 14.28666 17.15721
## 53243_SAYLMGLNSADLLK_2 14.18125 17.75968
## 31253_IVGGWEC(UniMod:4)EKHSQPWQVLVASR_4 14.33035 12.02928
## 17128_EVSTNTAMIQTSK_2 12.47770 15.49692
## 39778_LQNEIEDLMVDVER_2 14.48957 16.43795
## 66816_VSADAMLR_2 14.88122 16.56369
## 34402_LAEQELIETSER_2 14.86137 16.39088
## 24321_GTLEDQIIQANPALEAFGNAK_3 15.02552 17.11864
## 20848_GDTTNTVLQGLK_2 11.19490 13.03436
## 11253_DVTVLQNTDGNNNEAWAK_2 14.75170 12.60478
## 11706_EAFMLFDR_2 13.28352 17.41234
## 57928_SYTVAIAGYALAQMGR_2 12.80910 14.34376
## 46225_NNLLQAELEELR_3 13.28314 15.64595
## 676_AAVEQLTEEQK_2 14.00411 16.47569
## 64731_VIQYFAVIAAIGDR_2 14.56685 16.30219
## 10233_DQLLPPSPNNR_2 17.06305 15.40418
## 63678_VFDEFKPLVEEPQNLIKQNC(UniMod:4)ELFEQLGEYKFQNALLVR_5 13.79834 11.91472
## 25816_HITVVELVGVFPTLIGR_3 14.09471 12.11643
## 44061_MVSDIAGAWQR_2 14.71443 16.41354
## 35048_LDELRDEGKASSAK_3 16.06499 14.05777
## 53686_SEIAELNR_2 13.20799 14.84934
## 65255_VLGNPKSDEMNVK_3 15.33548 13.45292
## 48821_QITASSSYK_2 12.94430 15.56781
## 34357_LAEKDEEMEQAK_3 12.62608 16.14970
## 4032_AMKDEEKMEIQEMQLK_4 14.46444 16.28281
## 11058_DVFVPDDKQEFVK_3 15.63795 18.43240
## 39333_LPVVLANGQIR_2 15.72641 13.22904
## DECOY_17906_FEEILTR_2 14.34154 15.96042
## 12060_EATIPGHLNSYTIK_3 14.07298 15.82918
## 58077_TAIQAAGYPDK_2 12.96150 14.54198
## 28917_IISTTTLNK_2 12.90049 15.13041
## 1745_AFLEVLVK_2 11.33608 13.01049
## 25706_HIAEVSQEVTR_2 14.14891 15.90403
## 15255_ENIAIVER_2 15.83059 17.49442
## 26425_HQGVMVGMGQKDSYVGDEAQSK_3 13.93445 12.13751
## 55441_SLLSSAENEPPVPLVSNWRPPQPINNR_3 14.56297 16.44348
## 50420_QVMDHAFLLLSSER_2 14.01424 12.41734
## 5963_AVEPQLQEEER_2 11.10566 13.32657
## 65401_VLLPLIFR_2 14.98794 11.32784
## 15586_EQDTSAHLER_2 13.64750 15.34152
## 59968_TLEDQMNEHR_2 13.53913 16.08617
## 41242_LTYTQQLEDLK_2 14.57196 16.72491
## 22186_GIQGPQGPR_2 13.82733 17.04622
## 65022_VLAVNQENER_2 13.91017 16.11472
## 44083_MVTAVASALSSR_2 12.97918 14.87719
## 60999_TQEPPVDLSK_2 14.86855 17.21269
## 11452_DYLHLPPEIVPATLR_3 12.49503 14.20447
## 42985_MIGFPSSAGSVSPR_2 13.92717 15.57629
## 15357_ENQSILITGESGAGK_2 14.45227 15.97491
## 33813_KVADALTNAVAHVDDM(UniMod:35)PNALSALSDLHAHK_5 15.72528 14.02129
## 45894_NLQEEISDLTEQLGSSGK_2 14.53678 16.87497
## 3398_ALGQNPTQAEVLR_2 14.28328 16.90358
## 62150_TVYFAEK_2 14.37120 12.64845
## 26427_HQGVMVGMGQKDSYVGDEAQSK_4 15.71948 14.068843D plot
matN=matrix(1, ncol(protT), 7)
for(i in 1:ncol(protT)) {
iN=which(PPP1$Tissue == "N")
iT=which(PPP1$Tissue == "T")
matN[i,1]=length(which(!is.na(protT[iN, i])))
matN[i,2]=mean(as.numeric(protT[iN, i]),na.rm = T)
matN[i,3]=length(which(!is.na(protT[iT, i])))
matN[i,4]=mean(as.numeric(protT[iT, i]), na.rm = T)
matN[i,6]=sd(as.numeric(protT[iN, i]), na.rm = T)
matN[i,7]=sd(as.numeric(protT[iT, i]), na.rm = T)
if(matN[i,1] > 10 && matN[i,3]>10) {
matN[i,5]=t.test(as.numeric(protT[iN, i]), as.numeric(protT[iT, i]))$p.value
}
}The peptides have less than 15 observations are in red.
## Warning: package 'scatterplot3d' was built under R version 3.4.4
Expression vs Count
layout(matrix(1:4, 2, 2, byrow = T))
for(i in c(2, 4)) {
od=order(matN[,2])
plot(main=paste0("Expression: T/N>", i), axes=F, xlab="Peptide Count", ylab="", col="grey", -1*matN[od,3], matN[od,4], cex=0.1, pch=16, xlim=c(-720, 720), ylim=c(10, 35))
lines(spline(-1*matN[od,3], matN[od,4]), col="yellow")
points(col="grey20", matN[od,1], matN[od,2], cex=0.2, pch=16, xlim=c(720,0), ylim=c(10, 24))
lines(spline(matN[od,1], matN[od,2]), col="yellow")
pi=which(matN[od,5] < 0.05/nrow(prot) & (matN[od,4]-matN[od,2]) > log2(i))
points(-1*matN[od[pi],3], matN[od[pi],4], col="red", cex=0.5, pch=16)
points(mean(-1*matN[od[pi],3]), mean(matN[od[pi],4]), col="red", pch=1, cex=2, lwd=4)
points(matN[od[pi],1], matN[od[pi],2], col="green", cex=0.5, pch=16)
points(mean(matN[od[pi],1]), mean(matN[od[pi],2]), col="green", pch=1, cex=2, lwd=4)
abline(v=0, lty=2)
axis(side=1, at=c(-700, -200, 0, 200, 700), labels = c(700, 200, 0, 200, 700))
axis(side=2, at=c(10, 15, 20, 25))
axis(side=2, at=c(17), labels = c("Expression (T)"), line=1)
axis(side=4, at=c(10, 15, 20, 25))
axis(side=4, at=c(17), labels = c("Expression (N)"), line=1)
legend("topleft", legend = length(pi), pch=1, col="white", bty='n')
legend("topright", legend = c("T", "N"), col=c("grey", "grey20"), pch=16, bty='n')
}
for(i in c(1.5,2)) {
od=order(matN[,2])
plot(main=paste0("Expression: T/N >", i), axes=F, xlab="Peptide Count", ylab="", col="grey", -1*matN[od,3], matN[od,4], cex=0.2, pch=16, xlim=c(-720, 720), ylim=c(10, 35))
lines(spline(-1*matN[od,3], matN[od,4]), col="yellow")
points(col="grey20", matN[od,1], matN[od,2], cex=0.2, pch=16, xlim=c(720,0), ylim=c(10, 24))
lines(spline(matN[od,1], matN[od,2]), col="yellow")
pi=which(matN[od,5] < 0.05/nrow(prot) & (matN[od,2]-matN[od,4]) > log2(i))
points(-1*matN[od[pi],3], matN[od[pi],4], col="red", cex=0.5, pch=16)
points(mean(-1*matN[od[pi],3]), mean(matN[od[pi],4]), col="red", pch=1, cex=2, lwd=4)
points(matN[od[pi],1], matN[od[pi],2], col="green", cex=0.5, pch=16)
points(mean(matN[od[pi],1]), mean(matN[od[pi],2]), col="green", pch=1, cex=2, lwd=4)
abline(v=0, lty=2)
axis(side=1, at=c(-700, -200, 0, 200, 700), labels = c(700, 200, 0, 200, 700))
axis(side=2, at=c(10, 15, 20, 25))
axis(side=2, at=c(17), labels = c("Expression (T)"), line=1)
axis(side=4, at=c(10, 15, 20, 25))
axis(side=4, at=c(17), labels = c("Expression (N)"), line=1)
legend("topleft", legend = length(pi), pch=1, col="white", bty='n')
legend("topright", legend = c("T", "N"), col=c("grey", "grey20"), pch=16, bty='n')
}
points(mean(-1*matN[od,3], na.rm = T), mean(matN[od,4], na.rm = T), col="white", cex=2, lwd=2)
points(mean(matN[od,1], na.rm = T), mean(matN[od,2], na.rm = T), col="white", cex=2, lwd=2)
Sigmaplot
layout(matrix(1:4, 2, 2))
plot(main="Normal", matN[,2], matN[,6], bty="l", xlab="Expression [N]", ylab=expression(paste(sigma[N])), cex=0.3, pch=16, ylim=c(0, 3.5))
abline(lm(matN[,6]~matN[,2]), col="green")
odN=order(matN[,1])
plot(matN[,1], matN[,6], cex=0.3, pch=16, ylab=expression(paste(sigma[N])), xlab="Count", bty="l", main="Normal", ylim=c(0, 3.5))
lines(spline(matN[,1], matN[,6], n=nrow(matN)/100), col="green")
odT=order(matN[,3])
plot(main="Tumor", matN[,4], matN[,7], bty="l", xlab="Expression [T]",
ylab=expression(paste(sigma[T])), cex=0.3, pch=16, ylim=c(0, 3.5))
abline(lm(matN[,7]~matN[,4]), col="yellow")
plot(matN[,3], matN[,7], cex=0.3, pch=16, ylab=expression(paste(sigma[T])), xlab="Count", bty="l", main="Tumor", ylim=c(0, 3.5))
lines(spline(matN[,3], matN[,7], n=nrow(matN)/100), col="yellow")
Significant expression table
rownames(matN)=rownames(prot)
colnames(matN)=c("Cnt[N]", "Exp[N]", "Cnt[T]", "Exp[T]", "p-value", "Sigma[N]", "Sigma[T]")
write.table(matN, "ExpDat.txt", row.names = T, col.names = T, quote = F)
od=order(matN[,2])
pi1=which(matN[od,5] < 0.05/nrow(prot) & (matN[od,4]-matN[od,2]) > log2(2))
pi2=which(matN[od,5] < 0.05/nrow(prot) & (matN[od,2]-matN[od,4]) > log2(1.5))
expMat=matN[od[c(pi1, pi2)], ]
library(knitr)
colnames(expMat)=c("Count[N]", "Exp[N]", "Count[T]", "Exp[T]", "p-value", "Sig[N]", "Sig[T]")
rownames(expMat)=row.names(prot[od[c(pi1,pi2)],])
kable(expMat)| Count[N] | Exp[N] | Count[T] | Exp[T] | p-value | Sig[N] | Sig[T] | |
|---|---|---|---|---|---|---|---|
| 9701_DLVEAVAHILGIR_2 | 171 | 11.86642 | 260 | 12.98755 | 0.0e+00 | 0.8243043 | 1.1561031 |
| 59127_TGGMAFHSYFMEAIAPPLLQELKK_4 | 27 | 12.05894 | 151 | 13.08754 | 0.0e+00 | 0.5280154 | 0.8590112 |
| 43953_MVAAVAC(UniMod:4)AQVPK_2 | 22 | 12.38729 | 75 | 13.41423 | 0.0e+00 | 0.5599038 | 0.8512278 |
| 65414_VLLSLEHGLWAPNLHFHSPNPEIPALLDGR_4 | 199 | 12.84379 | 342 | 13.88842 | 0.0e+00 | 0.6501729 | 0.9838401 |
| 36239_LGAQALLGAAK_2 | 109 | 12.87777 | 411 | 14.17546 | 0.0e+00 | 0.7724513 | 0.8683135 |
| 5234_ASLAFLQK_2 | 37 | 12.87921 | 189 | 13.88113 | 0.0e+00 | 0.6604940 | 1.0794745 |
| 14991_EMAIPVLEAR_2 | 17 | 12.99384 | 102 | 14.09628 | 3.0e-07 | 0.5936080 | 0.8176642 |
| 21693_GGNVGINSFGFGGSNVHIILRPNTQPPPAPAPHATLPR_5 | 238 | 13.09256 | 444 | 14.10353 | 0.0e+00 | 0.5645722 | 0.8857373 |
| 64009_VGEVIVTKDDAMLLK_3 | 18 | 13.21556 | 52 | 14.48321 | 2.8e-06 | 0.8020378 | 0.8247242 |
| 14439_ELGVGIALR_2 | 365 | 13.24048 | 506 | 14.34926 | 0.0e+00 | 1.1030148 | 1.5897744 |
| 60139_TLLEGSGLESIISIIHSSLAEPR_3 | 320 | 13.24871 | 451 | 14.40496 | 0.0e+00 | 0.7216758 | 1.0830002 |
| 4097_AMVASGSELGK_2 | 17 | 13.28138 | 85 | 14.31720 | 0.0e+00 | 0.4540074 | 0.7692069 |
| 11423_DYHFKVDNDENEHQLSLR_4 | 161 | 13.31092 | 363 | 14.33592 | 0.0e+00 | 0.6942356 | 0.9810272 |
| 17730_FDASFFGVHPK_3 | 446 | 13.34703 | 611 | 14.49034 | 0.0e+00 | 0.6496788 | 0.9826620 |
| 53680_SEGVVAVLLTK_2 | 516 | 13.35213 | 633 | 14.51092 | 0.0e+00 | 0.8221358 | 1.0835413 |
| 34407_LAEQFVLLNLVYETTDK_3 | 261 | 13.38800 | 524 | 14.72082 | 0.0e+00 | 0.7528734 | 1.1744649 |
| 62342_VAAAVDLIIK_2 | 331 | 13.39178 | 484 | 14.50399 | 0.0e+00 | 0.6816973 | 1.0171145 |
| 31595_IYNVIGTLR_2 | 313 | 13.41147 | 495 | 14.50715 | 0.0e+00 | 0.8228783 | 1.2184293 |
| 26662_HSQDLAFLSMLNDIAAVPATAMPFR_3 | 257 | 13.41904 | 461 | 14.42177 | 0.0e+00 | 0.6506488 | 1.0144341 |
| 25706_HIAEVSQEVTR_2 | 24 | 13.42977 | 88 | 15.19794 | 0.0e+00 | 0.6250804 | 1.0328084 |
| 15644_EQGVTFPSGDIQEQLIR_2 | 441 | 13.43853 | 593 | 14.46292 | 0.0e+00 | 0.7633860 | 1.0733841 |
| 26059_HLSPDGQYVPR_2 | 466 | 13.44726 | 632 | 15.10067 | 0.0e+00 | 0.9447099 | 1.3445125 |
| 19227_FPQLDSTSFANSR_2 | 476 | 13.45676 | 606 | 14.56329 | 0.0e+00 | 0.6751972 | 1.0694037 |
| 70803_YVINAIPPTLTAK_2 | 126 | 13.50269 | 209 | 14.52664 | 0.0e+00 | 0.7090844 | 1.1793158 |
| 65528_VLQGDLVMNVYR_2 | 270 | 13.50701 | 471 | 14.57149 | 0.0e+00 | 0.6231824 | 0.9101708 |
| 65839_VMGLVPAK_2 | 213 | 13.52934 | 446 | 14.88360 | 0.0e+00 | 0.7507145 | 0.9333094 |
| 48166_QEPLLIGSTK_2 | 544 | 13.53024 | 641 | 14.74864 | 0.0e+00 | 0.7256862 | 1.0048340 |
| 27084_HYINLITR_2 | 57 | 13.56193 | 118 | 14.96098 | 0.0e+00 | 1.3417442 | 1.6104000 |
| 68702_WSQTAFPK_2 | 194 | 13.59804 | 303 | 14.60365 | 0.0e+00 | 0.9046979 | 1.2506303 |
| 1711_AFIDPLGLPDRPFYR_3 | 102 | 13.60220 | 278 | 14.62986 | 0.0e+00 | 0.9076854 | 1.2669038 |
| 17002_EVLNGLGK_2 | 180 | 13.60411 | 300 | 14.66186 | 0.0e+00 | 0.5197146 | 0.9368717 |
| 26060_HLSPDGQYVPR_3 | 209 | 13.64172 | 517 | 14.99368 | 0.0e+00 | 0.7353460 | 1.0360932 |
| 68090_VVVQVLAEEPEAVLK_2 | 205 | 13.65037 | 423 | 14.65679 | 0.0e+00 | 0.6464755 | 0.9285427 |
| 25389_HFLLEEDKPEEPTAHAFVSTLTR_4 | 357 | 13.66857 | 518 | 14.67380 | 0.0e+00 | 0.6092203 | 0.9133595 |
| 8964_DIVTNNGVIHLIDQVLIPDSAK_3 | 371 | 13.73303 | 581 | 14.83085 | 0.0e+00 | 0.7584934 | 1.1305484 |
| 43833_MSVQPTVSLGGFEITPPVVLR_2 | 579 | 13.73717 | 624 | 14.81199 | 0.0e+00 | 0.6431639 | 0.8038204 |
| 25564_HGLYLPTR_2 | 366 | 13.82088 | 574 | 14.88310 | 0.0e+00 | 0.6555921 | 0.9330899 |
| 42138_LYTLQDK_2 | 359 | 13.82767 | 576 | 14.84392 | 0.0e+00 | 0.6300876 | 0.8692118 |
| 63954_VGDPQELNGITR_2 | 586 | 13.83053 | 652 | 15.22099 | 0.0e+00 | 0.7497431 | 1.1079955 |
| 65099_VLEALLPLK_2 | 654 | 13.88434 | 669 | 15.16988 | 0.0e+00 | 0.9183551 | 1.1957256 |
| 52213_RPTPQDSPIFLPVDDTSFR_3 | 529 | 13.90887 | 635 | 15.03454 | 0.0e+00 | 0.7325772 | 1.0424138 |
| 55453_SLLVNPEGPTLMR_2 | 561 | 13.91010 | 647 | 15.17867 | 0.0e+00 | 0.6998375 | 1.0516110 |
| 5609_ATGPPVSELITK_2 | 601 | 14.00938 | 653 | 15.09539 | 0.0e+00 | 0.7121881 | 1.0970298 |
| 65415_VLLSLEHGLWAPNLHFHSPNPEIPALLDGR_5 | 292 | 14.01008 | 412 | 15.23346 | 0.0e+00 | 0.7066981 | 1.0651322 |
| 26641_HSLVSFVVR_2 | 293 | 14.02455 | 382 | 15.08536 | 0.0e+00 | 1.0870641 | 1.6936619 |
| 70501_YSGTLNLDR_2 | 511 | 14.05995 | 624 | 15.22134 | 0.0e+00 | 0.6470682 | 0.9533111 |
| 22908_GLVQALQTK_2 | 513 | 14.12427 | 623 | 15.38009 | 0.0e+00 | 0.7013912 | 1.0443899 |
| 4898_AQVADVVVSR_2 | 639 | 14.14903 | 665 | 15.43794 | 0.0e+00 | 0.7981367 | 1.1559439 |
| 39983_LQVVDQPLPVR_2 | 635 | 14.18420 | 664 | 15.59417 | 0.0e+00 | 0.8429341 | 1.1688118 |
| 59276_TGTVSLEVR_2 | 522 | 14.20235 | 624 | 15.40069 | 0.0e+00 | 0.6780268 | 0.9911792 |
| 43835_MSVQPTVSLGGFEITPPVVLR_3 | 645 | 14.20721 | 657 | 15.37187 | 0.0e+00 | 0.6800774 | 0.8526791 |
| 36919_LHLSGIDANPNALFPPVEFPAPR_3 | 523 | 14.28921 | 583 | 15.71279 | 0.0e+00 | 0.7753051 | 1.1121219 |
| 3054_ALAAGGYDVEK_2 | 618 | 14.31005 | 666 | 15.37184 | 0.0e+00 | 0.7530891 | 0.9710965 |
| 21229_GFEPGVTNILK_2 | 419 | 14.36793 | 598 | 15.49066 | 0.0e+00 | 0.7553859 | 1.1194887 |
| 24356_GTPLISPLIK_2 | 613 | 14.39548 | 660 | 15.83238 | 0.0e+00 | 0.8410878 | 1.2594809 |
| 8154_DGAWGAFR_2 | 175 | 14.40222 | 383 | 15.47520 | 0.0e+00 | 0.6450364 | 0.9310856 |
| 29682_IMFVDPSLTVR_2 | 564 | 14.43690 | 639 | 16.49272 | 0.0e+00 | 1.0770373 | 1.4924043 |
| 35_AAAITSDILEALGR_2 | 640 | 14.46173 | 672 | 15.82359 | 0.0e+00 | 0.8403339 | 1.2189721 |
| 61919_TVIIEQSWGSPK_2 | 650 | 14.47857 | 666 | 15.49892 | 0.0e+00 | 0.5749019 | 0.8199983 |
| 2176_AGLYGLPR_2 | 385 | 14.58619 | 578 | 15.80477 | 0.0e+00 | 0.7052312 | 0.9810432 |
| 37871_LLEQGLR_2 | 381 | 14.60711 | 565 | 15.88990 | 0.0e+00 | 0.7079327 | 1.0151247 |
| 67225_VTDALNATR_2 | 556 | 14.67091 | 588 | 15.71993 | 0.0e+00 | 0.5704402 | 0.8888676 |
| 1583_AEVVDEIAAK_2 | 37 | 14.68674 | 94 | 15.95882 | 0.0e+00 | 0.8900699 | 1.0194491 |
| 18964_FLYIWPNAR_2 | 500 | 14.71277 | 578 | 15.74328 | 0.0e+00 | 0.8463827 | 1.3699524 |
| 23455_GPSSVEDIK_2 | 659 | 14.72123 | 670 | 15.82835 | 0.0e+00 | 0.6145447 | 0.8479465 |
| 28780_IIHGNQIATNGVVHVIDR_4 | 178 | 14.73776 | 344 | 15.74567 | 0.0e+00 | 0.5216761 | 0.9554017 |
| 24850_GYAVLGGER_2 | 235 | 14.86875 | 478 | 16.04311 | 0.0e+00 | 0.6864785 | 0.9374032 |
| 20443_GANPVEIR_2 | 617 | 14.89402 | 658 | 15.89997 | 0.0e+00 | 0.5273360 | 0.8294227 |
| 28074_IFGVTTLDIVR_2 | 664 | 14.95555 | 677 | 15.96822 | 0.0e+00 | 0.6159779 | 0.9349770 |
| 64110_VGLQVVAVK_2 | 664 | 15.02240 | 674 | 16.10716 | 0.0e+00 | 0.5572369 | 0.8990343 |
| 29521_ILSGRPPLGFLNPR_3 | 590 | 15.03537 | 631 | 16.03879 | 0.0e+00 | 0.6878543 | 0.8839451 |
| 40276_LSDGVAVLK_2 | 611 | 15.23101 | 629 | 16.27486 | 0.0e+00 | 0.5320016 | 0.8627211 |
| 18093_FFPASADR_2 | 283 | 15.36322 | 371 | 16.38801 | 0.0e+00 | 0.8893353 | 0.9667434 |
| 65596_VLSIWEER_2 | 103 | 15.47520 | 178 | 16.48471 | 0.0e+00 | 0.5472939 | 0.6894442 |
| 3352_ALFLIPR_2 | 222 | 15.71215 | 313 | 16.71489 | 0.0e+00 | 0.5782074 | 0.7630289 |
| 18843_FLPLFDR_2 | 663 | 15.76876 | 676 | 16.80710 | 0.0e+00 | 0.7394782 | 1.0347077 |
| 4506_APSTYGGGLSVSSSR_2 | 322 | 13.03074 | 137 | 12.41042 | 0.0e+00 | 0.7872443 | 0.7191298 |
| 21695_GGPAYTPAGPQVPPLAR_2 | 613 | 13.53053 | 521 | 12.94020 | 0.0e+00 | 0.5283554 | 0.7362022 |
| 52358_RQVEVLTNQR_2 | 641 | 13.86393 | 610 | 13.26061 | 0.0e+00 | 0.6627862 | 0.7980292 |
| 7655_DAVYNSPK_2 | 532 | 13.86596 | 384 | 13.24935 | 0.0e+00 | 0.6881824 | 0.6819934 |
| 13006_EFVGQLVAPLPLGTGALR_2 | 600 | 13.95101 | 575 | 13.24821 | 0.0e+00 | 0.4849529 | 0.6847067 |
| 67954_VVPYFTQTPYSFLPLPTIK_3 | 649 | 14.15412 | 603 | 13.55978 | 0.0e+00 | 0.7366853 | 0.8420970 |
| 55906_SNFLNC(UniMod:4)YVSGFHPSDIEVDLLK_3 | 274 | 14.22801 | 222 | 13.58191 | 0.0e+00 | 1.1809337 | 1.1170843 |
| 60257_TLQALEFHTVPFQLLAR_3 | 410 | 14.24248 | 338 | 13.62354 | 0.0e+00 | 0.9669164 | 0.8730629 |
| 34886_LC(UniMod:4)AGTGENK_2 | 356 | 14.26860 | 251 | 13.56542 | 0.0e+00 | 1.3205687 | 1.2940559 |
| 42421_MDC(UniMod:4)QEC(UniMod:4)PEGYR_2 | 656 | 14.32992 | 622 | 13.64385 | 0.0e+00 | 0.8886534 | 1.0157035 |
| 21004_GEIGAVGNAGPAGPAGPR_2 | 399 | 14.43228 | 371 | 13.80131 | 0.0e+00 | 1.0141314 | 1.0340237 |
| 397_AALPYFPR_2 | 627 | 14.44582 | 506 | 13.83419 | 0.0e+00 | 0.6209630 | 0.6369868 |
| 35077_LDGAWWFLR_2 | 532 | 14.54372 | 537 | 13.87196 | 0.0e+00 | 0.6140650 | 0.8072237 |
| 25936_HLHFVDVDDLHIIVQELR_4 | 477 | 14.59869 | 444 | 13.99839 | 0.0e+00 | 0.4561597 | 0.6273249 |
| 42170_MAAASGYTDLR_2 | 93 | 14.61747 | 80 | 13.96941 | 0.0e+00 | 0.5256908 | 0.6659749 |
| 39068_LPAVEPTDQAQYLC(UniMod:4)R_2 | 617 | 14.62778 | 611 | 13.98940 | 0.0e+00 | 0.5352684 | 0.6823618 |
| 21384_GFQALGDAADIR_2 | 548 | 14.67925 | 294 | 14.00653 | 0.0e+00 | 0.7506270 | 0.7953264 |
| 68255_VYLLIGSR_2 | 526 | 14.86077 | 382 | 14.17438 | 0.0e+00 | 0.7543482 | 0.7636220 |
| 4242_ANRPFLVFIR_3 | 550 | 14.98823 | 473 | 14.19363 | 0.0e+00 | 0.6217726 | 0.8734193 |
| 66017_VNHVTLSQPK_2 | 620 | 15.00489 | 634 | 14.38555 | 0.0e+00 | 1.9773565 | 1.6431447 |
| 19050_FNDEHIPDSPFVVPVASLSDDAR_3 | 657 | 15.00590 | 640 | 14.40541 | 0.0e+00 | 0.5228355 | 0.6573248 |
| 12201_EDAIWNLLR_2 | 538 | 15.00877 | 445 | 14.37776 | 0.0e+00 | 1.3672454 | 1.4303715 |
| 45608_NLDLDSIIAEVK_2 | 392 | 15.04453 | 294 | 14.43789 | 0.0e+00 | 0.7729800 | 1.0411804 |
| 13668_EIDYIQYLR_2 | 322 | 15.07704 | 202 | 14.28905 | 0.0e+00 | 0.8493534 | 0.9041320 |
| 69127_YEELQQTAGR_2 | 454 | 15.24948 | 370 | 14.54987 | 0.0e+00 | 0.7446616 | 1.0329384 |
| 59317_TGYYFDGISR_2 | 648 | 15.36251 | 636 | 14.73227 | 0.0e+00 | 0.6493440 | 0.7826163 |
| 63229_VDVERDNLLDDLQR_2 | 663 | 15.49500 | 657 | 14.90046 | 0.0e+00 | 0.5300121 | 0.7780146 |
| 54055_SFSTASAITPSVSR_2 | 547 | 15.61185 | 440 | 14.92426 | 0.0e+00 | 0.7790755 | 1.1384843 |
| DECOY_53419_SDLAVPSELALLK_2 | 130 | 15.82087 | 85 | 15.21618 | 5.0e-07 | 0.9915482 | 0.7140460 |
| 8143_DGAGDVAFIR_2 | 547 | 15.85625 | 507 | 15.06900 | 0.0e+00 | 1.6443469 | 1.6862458 |
| 21473_GFYYSLK_2 | 588 | 15.88109 | 487 | 15.12805 | 0.0e+00 | 1.0329476 | 1.2068743 |
| 70930_YYGYTGAFR_2 | 441 | 16.04174 | 360 | 15.39200 | 0.0e+00 | 1.4588860 | 1.4642259 |
| 40162_LRPVAAEVYGTER_3 | 630 | 16.22747 | 586 | 15.42808 | 0.0e+00 | 1.7157734 | 1.7013527 |
| 25309_HEQVYIR_2 | 640 | 16.40234 | 652 | 15.81520 | 0.0e+00 | 1.3501990 | 1.5134634 |
| 8194_DGEVVSEATQQQHEVL_2 | 664 | 16.41021 | 659 | 15.73208 | 0.0e+00 | 0.6408973 | 0.8286688 |
Speculation
dis1=rnorm(10000, 6)
dis2=rnorm(10000, 6)
tail1=rchisq(1000,1, ncp=2)+6
tail2=0.95*tail1+rnorm(1000)
tail3=rnorm(100,6)
tail4=rchisq(100, 1, ncp=2)+6
plot(main="Speculation", ylim=c(0, 30), xlim=c(0, 30), bty='n', xlab="Normal", ylab="Tumor", c(dis1, tail1, tail3), c(dis2, tail2, tail4), pch=16, cex=0.5)
points(tail1, tail2, col="green", pch=1, cex=1, lwd=2)
points(tail3, tail4, col="pink", pch=1, cex=1, lwd=2)
points(dis1, dis2, pch=16, cex=0.5, col="black")
legend("bottomright", legend = c("Captured", "Hidden", "Not interested"), pch=c(1,1,16), col=c("green", "pink", "black"), bty='n')