Deconvolution to cancer and normal plasma based on human normal tissues
Shicheng Guo
July 7, 2016
Motivation:
- Deconvolution the contribution of the plasma to human normal and cancer tissues.
- Compare the contribution of cancer plasma with normal plasma
key update:
- Same genomic regions in simulation and real-data deconvolution
- Large number of Tissue-specific MHB regions so tha the result could be more stable(total ~700 MHBs)
- Introduce LCT and CCT as the referece and estimate the contribution to normal plasma
outputprefix="Coloncancer.plasma.0.05.top100"
library("DeconRNASeq")## Loading required package: limSolve## Loading required package: pcaMethods## Loading required package: Biobase## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, as.vector, cbind,
## colnames, do.call, duplicated, eval, evalq, Filter, Find, get,
## grep, grepl, intersect, is.unsorted, lapply, lengths, Map,
## mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, Position, rank, rbind, Reduce, rownames, sapply,
## setdiff, sort, table, tapply, union, unique, unlist, unsplit## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.##
## Attaching package: 'pcaMethods'## The following object is masked from 'package:stats':
##
## loadings## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following object is masked from 'package:limSolve':
##
## resolution## Loading required package: gridlibrary("ggplot2")
setwd("/home/shg047/oasis/monod/hapinfo/June")
# Colon<-Coloncancerdatapre()
# save(Colon,file=paste(outputprefix,".input.RData",sep=""))
load("Coloncancer.plasma.0.05.top100.input.RData")######################################################################################################
############## feature selection of tissue specific MHBs for deconvolution ########################
######################################################################################################
# select MHBs with linear pattern in mixture
load("Colon.Decov.linear.Var.RData")
data1=Colon$data[match(linearMHBs,rownames(Colon$data)),]
data1_ref=Colon$ref[match(linearMHBs,rownames(Colon$ref)),]
# select tissue specific MHBs
signatures<-AverageWithGroup(data1_ref)
outputprefix="TopGSIByCategory"
topGSIVar<-300
topgsi<-TopGSIByCategory(na.omit(Colon$GSI),top=topGSIVar,thresHigh=0.3,thresLow=0.1,plotfigure=T,figprefix=outputprefix)##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess# simulation data and real data
VirtualMatrix<-data.frame((data1[,grep("CCTmix",colnames(data1))]))
RealMatrix<-data.frame((data1[,grep("CCP|LCP|NCP",colnames(data1))]))
###################################################
##### Deconvolution-QR-Simulation-CCT #############
###################################################
DeconData<-data.frame(VirtualMatrix,signatures[match(rownames(VirtualMatrix),rownames(signatures)),])
DeconData<-na.omit(DeconData)
DeconData.tmp<-DeconData[na.omit(match(topgsi[,1],rownames(DeconData))),]
VirtualMatrix=data.frame(DeconData.tmp[,grep("CCTmix",colnames(DeconData))])
Signatures=data.frame(DeconData.tmp[,-grep("CCTmix",colnames(DeconData))])
library(car)
# NewVirtualMatrix=logit(VirtualMatrix)
# NewSignatures=logit(Signatures)
NewVirtualMatrix=(VirtualMatrix)
NewSignatures=(Signatures)
library("DeconRNASeq")
Rlt<-DeconRNASeq(NewVirtualMatrix,NewSignatures,checksig=FALSE,known.prop = F, use.scale = TRUE, fig = TRUE)## svd calculated PCA
## Importance of component(s):
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## R2 0.894 0.08418 0.00741 0.00367 0.00267 0.00239 0.0019 0.00152
## Cumulative R2 0.894 0.97820 0.98561 0.98928 0.99195 0.99434 0.9962 0.99776
## PC9 PC10 PC11
## R2 0.00116 0.0007 0.00038
## Cumulative R2 0.99892 0.9996 1.00000
##
## Attention: the number of pure cell types = 12 defined in the signature matrix;
##
## PCA results indicate that the number of cell types in the mixtures = 5acc<-Rlt$out.all
rownames(acc)=colnames(NewVirtualMatrix)
acc## Brain CCT Colon Esophagus Heart Intestine
## CCTmix.0 8.932639e-05 0.01661336 0.0005286128 0.01452074 0 0.03244060
## CCTmix.1 0.000000e+00 0.02782612 0.0070326402 0.02372023 0 0.03571665
## CCTmix.10 1.583038e-02 0.12209812 0.0385686524 0.04382155 0 0.04765881
## CCTmix.15 2.940786e-02 0.16423031 0.0455947920 0.04812829 0 0.05228761
## CCTmix.20 4.351409e-02 0.18800592 0.0642582756 0.06472110 0 0.05989333
## CCTmix.25 4.825924e-02 0.20963408 0.0682378555 0.07151337 0 0.06826273
## CCTmix.30 5.200971e-02 0.22682588 0.0803512757 0.07314986 0 0.07419112
## CCTmix.35 6.011887e-02 0.23713795 0.0749494639 0.07650301 0 0.07885798
## CCTmix.45 5.915626e-02 0.25056842 0.0907372458 0.08198832 0 0.08035094
## CCTmix.5 5.934129e-03 0.07404191 0.0212339166 0.02913797 0 0.04260127
## CCTmix.50 6.674292e-02 0.26389271 0.0930340293 0.07845508 0 0.08114843
## Kidney LCT Liver Lung Stomach
## CCTmix.0 0.003395694 0.01165826 0.003246425 0.000163903 0.0024742585
## CCTmix.1 0.001310466 0.01120690 0.000455986 0.001370924 0.0002040236
## CCTmix.10 0.014312175 0.03716157 0.000000000 0.014665960 0.0007810395
## CCTmix.15 0.020075472 0.06211861 0.006951103 0.021187061 0.0092811400
## CCTmix.20 0.028396600 0.06777927 0.013314582 0.021644275 0.0121133840
## CCTmix.25 0.034351128 0.07941853 0.021259800 0.018421303 0.0140992757
## CCTmix.30 0.038750658 0.09176269 0.022596189 0.026864663 0.0210999931
## CCTmix.35 0.045841982 0.09232136 0.030176283 0.023203712 0.0246845899
## CCTmix.45 0.051587229 0.10925795 0.029524429 0.030195869 0.0310136329
## CCTmix.5 0.000945400 0.01894663 0.000000000 0.007599829 0.0000000000
## CCTmix.50 0.055113540 0.11538981 0.031609165 0.024195536 0.0294250513
## WBC
## CCTmix.0 0.9148688
## CCTmix.1 0.8911561
## CCTmix.10 0.6651017
## CCTmix.15 0.5407377
## CCTmix.20 0.4363592
## CCTmix.25 0.3665427
## CCTmix.30 0.2923980
## CCTmix.35 0.2562048
## CCTmix.45 0.1856197
## CCTmix.5 0.7995590
## CCTmix.50 0.1609937fileout=paste("Colon-mixtureSimulation.Deconvolution.",topGSIVar,"M.txt",sep="")
write.table(acc,file=fileout,col.names=NA,row.names=T,sep="\t",quote=F)
idx<-unlist(lapply(strsplit(rownames(acc),"[.]"),function(x) x[[2]]))
y<-as.numeric(acc[,2])
lm.input<-data.frame(x=as.numeric(idx),y=100*y)
lm.input<-lm.input[order(lm.input$x),]
plot(y~x,data=lm.input,type="o",xlab="Expectation",ylab="Observed")
abline(0, 1,lwd=2,col="red")
dev.off()## null device
## 1plot(1,1)
abline(0, 1)
###################################################
##### Deconvolution-QR-Simulation-LCT #############
###################################################
# VirtualMatrix<-data.frame((data1[,grep("LCTmix",colnames(data1))]))
# DeconData<-data.frame(VirtualMatrix,signatures[match(rownames(VirtualMatrix),rownames(signatures)),])
# DeconData<-na.omit(DeconData)
# DeconData.tmp<-DeconData[na.omit(match(topgsi[,1],rownames(DeconData))),]
# VirtualMatrix=data.frame(DeconData.tmp[,grep("LCTmix",colnames(DeconData))])
# Signatures=data.frame(DeconData.tmp[,-grep("LCTmix",colnames(DeconData))])
# library(car)
# NewVirtualMatrix=logit(VirtualMatrix)
# NewSignatures=logit(Signatures)
# NewVirtualMatrix=(VirtualMatrix)
# NewSignatures=(Signatures)
# library("DeconRNASeq")
# Rlt<-try(DeconRNASeq(NewVirtualMatrix,NewSignatures,checksig=FALSE,known.prop = F, use.scale = TRUE, fig = TRUE))
# acc<-Rlt$out.all
# # rownames(acc)=colnames(NewVirtualMatrix)
# acc
# fileout=paste("Lung-mixtureSimulation.Deconvolution.",topGSIVar,"M.txt",sep="")
# write.table(acc,file=fileout,col.names=NA,row.names=T,sep="\t",quote=F)
###################################################
##### Deconvolution-QR-Real-Plasma-Data #############
###################################################
DeconData<-data.frame(RealMatrix,signatures[match(rownames(RealMatrix),rownames(signatures)),])
DeconData.tmp<-DeconData[na.omit(match(topgsi[,1],rownames(DeconData))),]
RealMatrix1=data.frame(DeconData.tmp[,grep("CCP",colnames(DeconData))])
RealMatrix2=data.frame(DeconData.tmp[,grep("NCP",colnames(DeconData))])
RealMatrix3=data.frame(DeconData.tmp[,grep("LCP",colnames(DeconData))])
Signatures=data.frame(DeconData.tmp[,-grep("CCP|LCP|NCP",colnames(DeconData))])
# Normalization (logit transform)
library(car)
# NewRealMatrix1=logit(RealMatrix1)
# NewRealMatrix2=logit(RealMatrix2)
# NewRealMatrix3=logit(RealMatrix3)
NewRealMatrix1=(RealMatrix1)
NewRealMatrix2=(RealMatrix2)
NewRealMatrix3=(RealMatrix3)
NewSignatures=(Signatures)
#NewRealMatrix1=RandomSamplingMean(RealMatrix1)
#NewRealMatrix2=RandomSamplingMean(RealMatrix2)
#NewRealMatrix3=RandomSamplingMean(RealMatrix3)
#NewSignatures=(Signatures)
library("DeconRNASeq")
input1<-inputrim(NewRealMatrix1,NewSignatures)
input2<-inputrim(NewRealMatrix2,NewSignatures)
input3<-inputrim(NewRealMatrix3,NewSignatures)
dim(input1$NewRealMatrix)## [1] 266 30dim(input2$NewRealMatrix)## [1] 23 75dim(input3$NewRealMatrix)## [1] 307 29Rlt1<-DeconRNASeq(input1$NewRealMatrix,input1$NewSignatures,checksig=FALSE,known.prop = F, use.scale = TRUE, fig = TRUE)## svd calculated PCA
## Importance of component(s):
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## R2 0.128 0.1107 0.1011 0.09246 0.09136 0.0864 0.07558 0.04232
## Cumulative R2 0.128 0.2387 0.3398 0.43227 0.52363 0.6100 0.68561 0.72793
## PC9 PC10 PC11 PC12
## R2 0.03789 0.03644 0.03386 0.03343
## Cumulative R2 0.76582 0.80226 0.83612 0.86955
##
## Attention: the number of pure cell types = 12 defined in the signature matrix;
##
## PCA results indicate that the number of cell types in the mixtures = 1Rlt2<-DeconRNASeq(input2$NewRealMatrix,input2$NewSignatures,checksig=FALSE,known.prop = F, use.scale = TRUE, fig = TRUE)## svd calculated PCA
## Importance of component(s):
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## R2 0.2442 0.1911 0.1386 0.1311 0.1064 0.08775 0.05923 0.03316
## Cumulative R2 0.2442 0.4353 0.5739 0.7050 0.8114 0.89911 0.95834 0.99150
## PC9 PC10 PC11 PC12
## R2 0.00777 0.00054 0.00007 0.00004
## Cumulative R2 0.99927 0.99981 0.99988 0.99992
##
## Attention: the number of pure cell types = 12 defined in the signature matrix;
##
## PCA results indicate that the number of cell types in the mixtures = 8Rlt3<-DeconRNASeq(input3$NewRealMatrix,input3$NewSignatures,checksig=FALSE,known.prop = F, use.scale = TRUE, fig = TRUE)## svd calculated PCA
## Importance of component(s):
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## R2 0.1558 0.1115 0.09485 0.08657 0.08038 0.06697 0.06453
## Cumulative R2 0.1558 0.2673 0.36218 0.44875 0.52913 0.59610 0.66063
## PC8 PC9 PC10 PC11 PC12
## R2 0.05535 0.04357 0.04144 0.03581 0.02809
## Cumulative R2 0.71598 0.75955 0.80099 0.83680 0.86489
##
## Attention: the number of pure cell types = 12 defined in the signature matrix;
##
## PCA results indicate that the number of cell types in the mixtures = 1acc1<-Rlt1$out.all
acc2<-Rlt2$out.all
acc3<-Rlt3$out.all
rownames(acc1)=colnames(NewRealMatrix1)
rownames(acc2)=colnames(NewRealMatrix2)
rownames(acc3)=colnames(NewRealMatrix3)
acc1<-data.frame(acc1)
acc2<-data.frame(acc2)
acc3<-data.frame(acc3)
write.table(acc1,file="Colon-RealSample.Deconvolution.txt",col.names=NA,row.names=T,sep="\t",quote=F)
write.table(acc2,file="NormalPlasma-RealSample.Deconvolution.txt",col.names=NA,row.names=T,sep="\t",quote=F)
write.table(acc3,file="LungPlasma-RealSample.Deconvolution.txt",col.names=NA,row.names=T,sep="\t",quote=F)
write.table(colMeans(acc1),file="Colon-RealSample.ColMeans.Deconvolution.txt",col.names=NA,row.names=T,sep="\t,quote=F")
write.table(colMeans(acc2),file="NormalPlasma-RealSample.ColMeans.Deconvolution.txt",col.names=NA,row.names=T,sep="\t",quote=F)
write.table(colMeans(acc3),file="LungPlasma-RealSample.ColMeans.Deconvolution.txt",col.names=NA,row.names=T,sep="\t",quote=F)
gginput1<-data.frame(Conv=c(acc1[,2],acc2[,2]),group=c(rep("CCP",nrow(acc1)),rep("NP",nrow(acc2))))
gginput2<-data.frame(Conv=c(acc3[,8],acc2[,8]),group=c(rep("LCP",nrow(acc3)),rep("NP",nrow(acc2))))
#pdf("deconv.boxplot-compare.Full.contribution.pdf")
#par(mfrow=c(2,2))
ggplot(gginput1,aes(factor(group), Conv))+geom_boxplot(outlier.shape = NA,aes(fill = "blue"))+coord_cartesian(ylim=c(0,0.15))+theme_bw()+theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())+ geom_jitter()
ggplot(gginput2,aes(factor(group), Conv))+geom_boxplot(outlier.shape = NA,aes(fill = "blue"))+coord_cartesian(ylim=c(0,0.15))+theme_bw()+theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())+ geom_jitter()
#dev.off()
# CCP vs NP
wilcox.test(acc1[,2],acc2[,2])##
## Wilcoxon rank sum test with continuity correction
##
## data: acc1[, 2] and acc2[, 2]
## W = 1846.5, p-value = 5.202e-10
## alternative hypothesis: true location shift is not equal to 0# LCP vs NP
wilcox.test(acc3[,8],acc2[,8])##
## Wilcoxon rank sum test with continuity correction
##
## data: acc3[, 8] and acc2[, 8]
## W = 1607.5, p-value = 3.443e-05
## alternative hypothesis: true location shift is not equal to 0# Colon cancer plasma deconvolution: frequency of DNA from colon cancer
sum(acc1[,2]>0)/nrow(acc1)## [1] 0.7666667sum(acc2[,2]>0)/nrow(acc2)## [1] 0.1333333# Lung cancer plasma deconvolution: frequency of DNA from colon cancer
sum(acc3[,8]>0)/nrow(acc3)## [1] 0.8965517sum(acc2[,8]>0)/nrow(acc2)## [1] 0.2666667# As our prior knownlege, WBC should be have higher proportion in the plasma (suppose>0.3)
acc1<-subset(acc1,WBC>0.3)
acc2<-subset(acc2,WBC>0.3)
acc3<-subset(acc3,WBC>0.3)
ci95(c(acc1[,12],acc2[,12],acc3[,12]))## rlt
## [1,] "mean=0.7203, 95%CI:0.659-0.7816"ci95(c(acc1[,2]+acc1[,3]))## rlt
## [1,] "mean=0.0333, 95%CI:0.0126-0.0541"ci95(c(acc3[,8]+acc3[,10]))## rlt
## [1,] "mean=0.0784, 95%CI:0.0199-0.1368"ci95(c(acc1[,2],acc3[,8]))## rlt
## [1,] "mean=0.0233, 95%CI:0.0043-0.0424"ci95(c(acc1[,3],acc3[,10]))## rlt
## [1,] "mean=0.0297, 95%CI:0.0116-0.0478"ci95(c(acc2[,2]))## rlt
## [1,] "mean=0.0017, 95%CI:-1e-04-0.0034"ci95(c(acc2[,8]))## rlt
## [1,] "mean=0.01, 95%CI:-7e-04-0.0206"acc1## Brain CCT Colon Esophagus Heart
## CCP.9 0.025270740 0.000000000 0.063343680 0.03186697 0.02878599
## CCP.11 0.023435769 0.029439289 0.032371332 0.06595032 0.02868456
## CCP.19 0.023241743 0.071692098 0.003446407 0.07192530 0.03315948
## CCP.21 0.115219025 0.000000000 0.022489823 0.03339848 0.01736567
## CCP.22 0.000000000 0.000000000 0.012866647 0.03610563 0.02373672
## CCP.24 0.006142883 0.001848379 0.000000000 0.02753909 0.00000000
## CCP.25 0.000000000 0.000000000 0.006491701 0.00000000 0.00800996
## CCP.27 0.043875963 0.000000000 0.021796409 0.01308978 0.04511575
## CCP.29 0.006364790 0.027961271 0.006170940 0.04103095 0.08468497
## Intestine Kidney LCT Liver Lung
## CCP.9 0.019288084 0.014844518 0.00000000 0.02836886 0.0006464271
## CCP.11 0.082839649 0.026355348 0.04510773 0.07654744 0.0053470745
## CCP.19 0.097441542 0.032748690 0.15519535 0.08282662 0.0071132641
## CCP.21 0.031356283 0.010911498 0.02355828 0.16239905 0.0859521903
## CCP.22 0.030834460 0.017929940 0.00000000 0.01325291 0.0000000000
## CCP.24 0.036982045 0.015554254 0.03464131 0.30701682 0.2476282744
## CCP.25 0.000000000 0.008784567 0.00000000 0.02610831 0.0000000000
## CCP.27 0.009609106 0.089160110 0.01060676 0.32392450 0.0121135302
## CCP.29 0.008774822 0.063170478 0.00000000 0.28840941 0.0510989552
## Stomach WBC
## CCP.9 0.037735729 0.7498490
## CCP.11 0.059072852 0.5248486
## CCP.19 0.115945559 0.3052640
## CCP.21 0.040562758 0.4567870
## CCP.22 0.016339467 0.8489342
## CCP.24 0.005021395 0.3176255
## CCP.25 0.024148190 0.9264573
## CCP.27 0.040637783 0.3900703
## CCP.29 0.059147325 0.3631861boxplot(acc1,cex.axis=0.75)
colMeans(acc1)## Brain CCT Colon Esophagus Heart Intestine
## 0.02706121 0.01454900 0.01877522 0.03565628 0.02994923 0.03523622
## Kidney LCT Liver Lung Stomach WBC
## 0.03105104 0.02990105 0.14542821 0.04554441 0.04429012 0.54255800acc2## Brain CCT Colon Esophagus Heart
## NCP.5 0.00000000 0.000000000 0.000000000 0.000000000 0.0225619972
## NCP.9 0.00000000 0.011267598 0.000000000 0.000000000 0.0000000000
## NCP.24 0.00000000 0.000000000 0.000000000 0.002615390 0.0011329744
## NCP.25 0.00000000 0.000000000 0.000000000 0.000000000 0.0000000000
## NCP.27 0.00000000 0.000000000 0.000000000 0.000000000 0.0000000000
## NCP.28 0.00000000 0.000000000 0.000000000 0.000000000 0.0126833543
## NCP.32 0.29487750 0.000000000 0.000000000 0.000000000 0.0000000000
## NCP.33 0.00000000 0.008416962 0.000000000 0.000000000 0.0071350233
## NCP.34 0.00000000 0.000000000 0.000000000 0.000000000 0.0000000000
## NCP.35 0.00000000 0.000000000 0.038269367 0.000000000 0.0000000000
## NCP.36 0.00000000 0.000000000 0.000000000 0.000000000 0.0002635203
## NCP.38 0.00000000 0.000000000 0.000000000 0.007252322 0.0019117423
## NCP.42 0.00000000 0.000000000 0.000000000 0.000000000 0.0000000000
## NCP.43 0.00000000 0.000000000 0.000000000 0.000000000 0.0821778401
## NCP.44 0.00000000 0.000000000 0.000000000 0.000000000 0.2539760861
## NCP.46 0.00000000 0.000000000 0.000000000 0.000000000 0.1092037439
## NCP.47 0.00000000 0.000000000 0.000000000 0.000000000 0.0000000000
## NCP.48 0.00000000 0.000000000 0.000000000 0.000000000 0.0203801239
## NCP.50 0.00000000 0.000000000 0.000000000 0.000000000 0.0092515184
## NCP.52 0.00000000 0.025059656 0.000000000 0.000000000 0.0000000000
## NCP.53 0.04894342 0.016833366 0.000000000 0.000000000 0.0000000000
## NCP.54 0.18694313 0.000000000 0.000000000 0.000000000 0.0000000000
## NCP.55 0.00000000 0.000000000 0.000000000 0.007408094 0.0022891932
## NCP.57 0.00000000 0.000000000 0.000000000 0.000000000 0.0933248522
## NCP.58 0.00000000 0.000000000 0.007537285 0.000000000 0.1937624528
## NCP.59 0.03380467 0.000000000 0.000000000 0.000000000 0.0000000000
## NCP.61 0.00000000 0.000000000 0.000000000 0.000000000 0.1424488801
## NCP.62 0.00000000 0.000000000 0.000000000 0.000000000 0.0000000000
## NCP.64 0.00000000 0.000000000 0.000000000 0.000000000 0.1819818746
## NCP.66 0.00000000 0.000000000 0.000000000 0.000000000 0.0000000000
## NCP.67 0.00000000 0.000000000 0.000000000 0.000000000 0.0000000000
## NCP.68 0.00000000 0.000000000 0.000000000 0.000000000 0.0659019027
## NCP.69 0.00000000 0.000000000 0.009779630 0.000000000 0.0000000000
## NCP.71 0.00000000 0.000000000 0.000000000 0.000000000 0.0000000000
## NCP.72 0.00000000 0.000000000 0.000000000 0.000000000 0.1210910902
## NCP.73 0.00000000 0.000000000 0.000000000 0.012234298 0.0180112897
## NCP.74 0.00000000 0.000000000 0.000000000 0.000000000 0.0207947581
## Intestine Kidney LCT Liver Lung
## NCP.5 0.0000000 0.000000000 0.000000000 0.00000000 0.004764035
## NCP.9 0.1216252 0.000000000 0.038988419 0.00000000 0.000000000
## NCP.24 0.0000000 0.000000000 0.000000000 0.00000000 0.000000000
## NCP.25 0.0000000 0.379838773 0.119521494 0.00000000 0.000000000
## NCP.27 0.0000000 0.000000000 0.000000000 0.45067203 0.000000000
## NCP.28 0.0000000 0.000000000 0.000000000 0.04139663 0.011734417
## NCP.32 0.0000000 0.000000000 0.000000000 0.00000000 0.026610212
## NCP.33 0.0000000 0.000000000 0.000000000 0.06273753 0.000000000
## NCP.34 0.0261595 0.000000000 0.000000000 0.00000000 0.007274000
## NCP.35 0.0000000 0.000000000 0.056304772 0.00000000 0.000000000
## NCP.36 0.0000000 0.005641649 0.000000000 0.00000000 0.000000000
## NCP.38 0.0000000 0.001419471 0.000000000 0.00000000 0.000000000
## NCP.42 0.0000000 0.000000000 0.000000000 0.35429064 0.000000000
## NCP.43 0.0000000 0.000000000 0.000000000 0.00000000 0.135232474
## NCP.44 0.0000000 0.000000000 0.000000000 0.00000000 0.000000000
## NCP.46 0.0000000 0.000000000 0.000000000 0.00000000 0.179793477
## NCP.47 0.0000000 0.041844578 0.000000000 0.00000000 0.000000000
## NCP.48 0.0000000 0.000000000 0.000000000 0.00000000 0.060017447
## NCP.50 0.0000000 0.000000000 0.000000000 0.03017573 0.030129432
## NCP.52 0.0000000 0.000000000 0.000000000 0.65424522 0.000000000
## NCP.53 0.1928496 0.000000000 0.145933334 0.00000000 0.067557592
## NCP.54 0.0000000 0.000000000 0.000000000 0.00000000 0.000000000
## NCP.55 0.0000000 0.001804840 0.000000000 0.00000000 0.000000000
## NCP.57 0.0000000 0.000000000 0.000000000 0.00000000 0.177890314
## NCP.58 0.0000000 0.000000000 0.000000000 0.00000000 0.298734609
## NCP.59 0.0000000 0.000000000 0.000000000 0.00000000 0.000000000
## NCP.61 0.0000000 0.000000000 0.000000000 0.00000000 0.221315699
## NCP.62 0.0264204 0.005455332 0.008642846 0.00000000 0.000000000
## NCP.64 0.0000000 0.000000000 0.000000000 0.00000000 0.000000000
## NCP.66 0.0000000 0.000000000 0.000000000 0.00000000 0.000000000
## NCP.67 0.0000000 0.053688832 0.000000000 0.10313539 0.000000000
## NCP.68 0.0000000 0.000000000 0.000000000 0.03125826 0.165758856
## NCP.69 0.0000000 0.000000000 0.000000000 0.02078924 0.000000000
## NCP.71 0.0000000 0.000000000 0.000000000 0.31059982 0.000000000
## NCP.72 0.0000000 0.000000000 0.000000000 0.00000000 0.200410804
## NCP.73 0.0000000 0.000000000 0.000000000 0.00000000 0.025173720
## NCP.74 0.0000000 0.000000000 0.000000000 0.00000000 0.030777564
## Stomach WBC
## NCP.5 0.000000000 0.9726740
## NCP.9 0.000000000 0.8281188
## NCP.24 0.002291566 0.9939601
## NCP.25 0.000000000 0.5006397
## NCP.27 0.000000000 0.5493280
## NCP.28 0.000000000 0.9341856
## NCP.32 0.000000000 0.6785123
## NCP.33 0.000000000 0.9217105
## NCP.34 0.000000000 0.9665665
## NCP.35 0.000000000 0.9054259
## NCP.36 0.010747828 0.9833470
## NCP.38 0.005812744 0.9836037
## NCP.42 0.000000000 0.6457094
## NCP.43 0.000000000 0.7825897
## NCP.44 0.000000000 0.7460239
## NCP.46 0.000000000 0.7110028
## NCP.47 0.000000000 0.9581554
## NCP.48 0.000000000 0.9196024
## NCP.50 0.000000000 0.9304433
## NCP.52 0.008543879 0.3121512
## NCP.53 0.000000000 0.5278827
## NCP.54 0.000000000 0.8130569
## NCP.55 0.004869215 0.9836287
## NCP.57 0.000000000 0.7287848
## NCP.58 0.046090726 0.4538749
## NCP.59 0.000000000 0.9661953
## NCP.61 0.014071273 0.6221641
## NCP.62 0.000000000 0.9594814
## NCP.64 0.000000000 0.8180181
## NCP.66 0.025779621 0.9742204
## NCP.67 0.000000000 0.8431758
## NCP.68 0.000000000 0.7370810
## NCP.69 0.000000000 0.9694311
## NCP.71 0.000000000 0.6894002
## NCP.72 0.003134362 0.6753637
## NCP.73 0.000000000 0.9445807
## NCP.74 0.000000000 0.9484277boxplot(acc2,cex.axis=0.75)
colMeans(acc2)## Brain CCT Colon Esophagus Heart
## 0.0152586139 0.0016642590 0.0015023320 0.0007975704 0.0367644383
## Intestine Kidney LCT Liver Lung
## 0.0099203963 0.0132349588 0.0099835369 0.0556567704 0.0444101258
## Stomach WBC
## 0.0032794923 0.8075275060acc3## Brain CCT Colon Esophagus Heart Intestine
## LCP.11 0.04474880 0.03685255 0.02774744 0.01021664 0.02242827 0.022052805
## LCP.12 0.15603045 0.02084895 0.08902630 0.00000000 0.00000000 0.021195006
## LCP.19 0.01030373 0.00966795 0.04100017 0.05216739 0.13841527 0.004772522
## LCP.21 0.06428793 0.02388306 0.04144403 0.03935496 0.09564646 0.042465850
## LCP.24 0.01413536 0.00000000 0.11254312 0.03534461 0.01243813 0.021945676
## LCP.26 0.01532293 0.00000000 0.04091663 0.15396974 0.04113473 0.043350417
## LCP.27 0.01112237 0.00000000 0.04531218 0.03005449 0.01973201 0.128018053
## Kidney LCT Liver Lung Stomach WBC
## LCP.11 0.087434285 0.01952869 0.18035958 0.026781020 0.064325948 0.4575240
## LCP.12 0.000000000 0.12800152 0.05738088 0.007770412 0.008997862 0.5107486
## LCP.19 0.061329513 0.01541319 0.03550624 0.057774991 0.016049373 0.5575997
## LCP.21 0.065892694 0.00000000 0.08583651 0.049300957 0.071562968 0.4203246
## LCP.24 0.056743425 0.06020549 0.08885315 0.132637839 0.034640707 0.4305125
## LCP.26 0.065262881 0.00000000 0.01591730 0.031541917 0.060639153 0.5319443
## LCP.27 0.001668498 0.01949638 0.20329032 0.000000000 0.035546086 0.5057596boxplot(acc3,cex.axis=0.75)
colMeans(acc3)## Brain CCT Colon Esophagus Heart Intestine
## 0.04513594 0.01303607 0.05685570 0.04587255 0.04711355 0.04054290
## Kidney LCT Liver Lung Stomach WBC
## 0.04833304 0.03466361 0.09530628 0.04368673 0.04168030 0.48777332out<-rbind(colMeans(acc1),colMeans(acc2),colMeans(acc3))
rownames(out)<-c("CCP","NP","LCP")
out## Brain CCT Colon Esophagus Heart Intestine
## CCP 0.02706121 0.014549004 0.018775215 0.0356562810 0.02994923 0.035236221
## NP 0.01525861 0.001664259 0.001502332 0.0007975704 0.03676444 0.009920396
## LCP 0.04513594 0.013036072 0.056855697 0.0458725458 0.04711355 0.040542904
## Kidney LCT Liver Lung Stomach WBC
## CCP 0.03105104 0.029901047 0.14542821 0.04554441 0.044290117 0.5425580
## NP 0.01323496 0.009983537 0.05565677 0.04441013 0.003279492 0.8075275
## LCP 0.04833304 0.034663609 0.09530628 0.04368673 0.041680300 0.4877733# pdf("deconv.boxplot.contribution.pdf")
boxplot(acc1,cex.axis=0.7,ylim=c(0,1),horizontal = T,las=1,main="CCP")
boxplot(acc3,cex.axis=0.7,ylim=c(0,1),horizontal = T,las=1,main="LCP")
boxplot(acc2,cex.axis=0.7,ylim=c(0,1),horizontal = T,las=1,main="NP")
# dev.off()
gginput1<-data.frame(Conv=c(acc1[,2],acc2[,2]),group=c(rep("CCP",nrow(acc1)),rep("NP",nrow(acc2))))
gginput2<-data.frame(Conv=c(acc3[,8],acc2[,8]),group=c(rep("LCP",nrow(acc3)),rep("NP",nrow(acc2))))
# pdf("deconv.boxplot-compare.contribution.pdf")
# par(mfrow=c(2,2))
ggplot(gginput1,aes(factor(group), Conv))+geom_boxplot(outlier.shape = NA)+coord_cartesian(ylim=c(0,0.2))+theme_bw()+theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())+ geom_jitter()
ggplot(gginput2,aes(factor(group), Conv))+geom_boxplot(outlier.shape = NA)+coord_cartesian(ylim=c(0,0.2))+theme_bw()+theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())+ geom_jitter()
# dev.off()
ci95(c(acc1[,12],acc2[,12],acc3[,12]))## rlt
## [1,] "mean=0.7203, 95%CI:0.659-0.7816"write.table(out,file="Plasma-RealSample.Same.Contribution.Deconvolution.txt",col.names=NA,row.names=T,sep="\t",quote=F)
save.image(file="Deconvolution.image.RData")Conclustion
We sought to quantify the tumor load based on the deconvolution analysis to the cancer and normal plasma by non-negative decomposition with quadratic programming with the reference of LC, CRC and 10 normal tissues. We estimated that a predominant fraction, 72.0% (95% CI:0.659-0.782) in the cancer and normal plasma were contributed by white blood cells, which is consistent with the levels reported in a recent study based on lower-coverage whole genome bisulfite sequencing (69.4%)16. Primary tumor and normal tissue-of-origin contributes at the similar level of 2.3% (95% CI: 0.4%-4.2%) and 3.0% (95% CI:1.2%-4.4%). We also applied the similar analysis to normal plasma, and we found the tumor contribution to normal plasma were significantly lower (P=3.4x10-5 and 5.2x10-10 for CRC and LC, respectively) than that to cancer plasma, with the contribution of 0.17% and 1.0% for colon and lung cancer respectively. We also found 76.7% CRC and 89.6% LC samples were detected the contribution from tumor tissues while only 13% and 26% normal plasmas have certain (low) tumor contribution. Therefore, circulating cell-free DNA contains a relatively stable fraction of molecules released from various normal tissues, whereas in cancer patient tumor cells and apoptosis cells released DNA molecules that can be more abundant than normal tissues (Table S6).
# R -e 'library("rmarkdown");library("knitr");rmarkdown::render("Deconv.Rmd")'
# R -e 'library("markdown");rpubsUpload("normalDev","Deconv.html")'