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Figures.R
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Figures.R
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#Required packages: Limma,data.table,ggplot2,reshape2
require(limma)||install.packages(limma)
require(data.table)||install.packages(data.table)
require(ggplot2)||install.packages(ggplot2)
require(reshape2)||install.packages(reshape2)
require(DESeq2)||install.packages(DESeq2)
require(grid)||install.packages(grid)
require(dplyr)||install.packages(dplyr)
#The (ACTUAL) Figure code used in the manuscript for blm as of 14/09/29. No changes to the figures will be made other than through this code!
#Except figure 1, because figure 1 is from a powerpoint. Fig1BW.ppt
##This code all runs off of count tables - these count tables should also be added to git (and have their locations in the code suitably refactored) -
#current count table locations:
# SEQC : ~/Desktop/MainSEQC/ subdirectories SEQC_MAIN_ILM_rawCounts_ZSu.2012_04_14 & SEQC_LifescopeCounts
# BLM : ~/Downloads/RawData/BLM/Countdata #added as PAPERUSED.zip (but makerefmetdfb isn't quite fixed yet)
#Figure2: LineFigure()
LineFigure<-function(){
theme_set(theme_bw(base_size=18))
df<-calcmodel(assignIdentity(makerefmetdfb(norm="UQN")))
ggplot()+geom_point(data=subset(df,identity!="external"),aes(x=log2(a1),y=log2(pred1)))+xlab("log2(Observed Mix1 Counts)")+ylab("log2(Predicted Mix1 Counts)")
}
#Figure3:targetfigure()
targetfigure<-function(){
theme_set(theme_bw(base_size=18))
a<-makeTargetPlot(type="BLM",df=makerefmetdfb(norm="UQN"))
return(a+coord_cartesian(ylim=c(0,0.75),xlim=c(0,0.75))+scale_x_continuous(breaks=c(0,0.25,0.5,0.75))+scale_y_continuous(breaks=c(0,0.25,0.5,0.75)))
}
#Figure6: call dendrogramplot())
dendrogramplot<-function(testing=FALSE){
require(stats)
require(RColorBrewer)
require(DESeq2)
SEQCDF<-makeseqcdf()
vsndata<-varstabdata()
test<-dist(t(assay(vsndata)))
test<-as.matrix(test)
rownames(test)<-colnames(test)<-with(colData(vsndata),paste(sample,site,sep=":"))
hcr <- suppressWarnings(hclust(dist(test)))
hcr$labels<-paste0(c(rep(c("A","B","C","D","Cm","Dm"),6)),c(rep(1,6),rep(2,6),rep(3,6),rep(4,6),rep(5,6),rep(6,6)))
hcrd<-as.dendrogram(hcr)
#labelColors = c("#CDB380", "#036564", "#EB6841", "#EDC951")
labelColors<<-brewer.pal(n = 4,name = "PuOr")
# cut dendrogram in 4 clusters
clusMember<<- cutree(hcr, 4)
# using dendrapply
colLab<-function(n) {
if (is.leaf(n)) {
a <- attributes(n)
labCol <- labelColors[clusMember[which(names(clusMember) == a$label)]]
attr(n, "nodePar") <- list(a$nodePar, lab.col = labCol, lab.cex=1.5)
}
n
}
clusDendro = dendrapply(hcrd, colLab)
if(testing==TRUE){return(clusDendro)}
# make plot
output<-plot(clusDendro, main = "",axes=FALSE)
rm(clusMember,labelColors,pos=1)
return(output)
}
DistributionFigure<-function(indf){
if(missing(indf)){indf<-makeseqcdf()}
{seqcmfrac<-NULL;for(I in levels(indf$site)){seqcmfrac<-rbind(seqcmfrac,calcmrnafrac(subset(indf,site==I)[,c(1,3:18)],selection=2:17))}
seqcmfrac<-as.data.frame(seqcmfrac)
seqcmfrac$site<-levels(indf$site)
seqcmfrac<-melt(seqcmfrac,id.vars="site")
seqcmfrac$replicate<-substr(seqcmfrac$variable,2,2)
seqcmfrac$sample<-substr(seqcmfrac$variable,1,1)
}#setup the data frame
summarize(group_by(seqcmfrac,sample,site),mean(value))
pme<-summarize(group_by(seqcmfrac,site),mean(value),AtoB=mean(value[sample=="A"])/mean(value[sample=="B"]))
#gives an A to B ratio. This value is to be compared to the shippydf values (1.39-1.476)
#The raw data from Shippy Et al (Nature Biotechnology 24, 1123 - 1131 (2006)): Just mean +sd
randshipsq<-data.frame(atob=rnorm(10000,mean=2.870,sd=0.095)/rnorm(100,mean=2.003,sd=0.124),atoc=rnorm(10000,mean=2.870,sd=0.095)/(rnorm(10000,mean=2.870,sd=0.095)*0.75+rnorm(10000,mean=2.003,sd=0.124)*0.25),
atod=rnorm(10000,mean=2.870,sd=0.095)/(rnorm(10000,mean=2.870,sd=0.095)*0.25+rnorm(10000,mean=2.003,sd=0.124)*0.75))
#convert to a distribution rather than boxplots
trial<-rbind(melt(randshipsq),subset(pm4,site!="PSU")[,3:4])
trial$type<-c(rep("Previous Measurement",30000),(rep("ERCC-estimated",96)))
levels(trial$variable)<-c("Sample A:B","Sample A:C","Sample A:D")
ggplot(data=trial)+geom_density(aes(x=value,fill=type,alpha=type))+facet_wrap(~variable)+xlab("Ratio")+scale_alpha_manual(values=c(0.75,0.75),name="mRNA fraction Ratio")+scale_fill_manual(values=c("grey20","grey60"),name="mRNA fraction Ratio")+ylab("")
}
sf1<-function(){
adf<-(subset(rbind(assignIdentity(makerefmetdfb(norm=0)),assignIdentity(makerefmetdfb(norm=1)),assignIdentity(makerefmetdfb(norm="UQN"))),identity!="unclassified"))
adf$norm<-as.factor(adf$norm);levels(adf$norm)[1]<-"None";levels(adf$norm)[2]<-"Library Size";levels(adf$norm)[3]<-"Upper Quartile" #just some sad cleaning...
adf$norm<-relevel(adf$norm,ref="None")
g<-ggplot(adf)
return(print(g+geom_point(data=subset(adf,identity!="unclassified"),aes(x=log2(a1*a2)/2,y=log2((a1)/a2),col=identity))+geom_point(data=subset(adf,identity=="external"),aes(x=log2(a1*a2)/2,y=log2((a1)/a2)),size=3)+facet_grid(. ~ norm)+geom_hline(yintercept=0)+ggtitle("Normalization")+
theme(legend.position=c(0.9,0.9))+theme(strip.background = element_rect(fill = 'white'))+ylim(c(-2,2))+scale_colour_manual(values=c("#CC6666","black","#99CC66","#6699CC"),labels=c("Brain","ERCC","Liver","Muscle"))+xlim(c(0,18))+
theme(legend.title=element_blank())+theme(title=element_text(size=20))+
xlab("Mean Counts (BLM-1+BLM-2)")+ylab("Ratio of counts (BLM-1/BLM-2)")+theme(strip.text.x=element_text(size=24))+theme(axis.title=element_text(size=24))+theme(axis.text=element_text(size=20))+
guides(colour=guide_legend(override.aes=list(size=5)))))
}#Sup fig 1; MA plots : by normalization
sf5<-function(indf){
require(reshape2)
if(missing(indf)){
indf<-makeseqcdf()}
#indf<-normalizeSEQC(indf) #apparently normalization of these data pre-analysis make for some issues...but only for naive/mymethod, decon is unaffected.
final<-NULL;finalMod<-NULL;finalNaive<-NULL
dset<-cbind(indf[,1:2],rowMeans(indf[,3:6]),rowMeans(indf[,7:10]),(indf[,11:14]),(indf[,15:18]))
names(dset)<-c("gene_id","site","A","B","C","C","C","C","D","D","D","D")
require("DeconRNASeq")
###SEQClm(subset(SEQCDF,site=="AGR")) #Debugging - the data that're getting plotted are NOT these...
###value Dval
###c1 0.7412337 0.2505380
###c2 0.7458750 0.2577192
###c3 0.7427236 0.2474030
###c4 0.7599289 0.2429792
###SEQClm(subset(SEQCDF,site=="AGR"),ignoremrna=TRUE)
###value Dval
###c1 0.7864730 0.3006213
###c2 0.7905321 0.3086469
###c3 0.7877769 0.2971081
###c4 0.8027694 0.2921405
for(I in levels(as.factor(dset$site))){
tmp<-DeconRNASeq(datasets=subset(dset,site==I)[c(5:12)],signatures=subset(dset,site==I)[,c(3,4)])
tmp<-tmp$out.all
tmp<-as.data.frame(tmp)
tmp$mix<-c(rep("C",4),rep("D",4))
tmp<-melt(tmp)
tmp$site<-I
naive<-SEQClm(subset(indf,site==I),ignoremrna=TRUE)
naive<-melt(naive);naive$site<-I;naive$mix<-c(rep("C",4),rep("D",4));naive$variable<-"A"
modeled<-SEQClm(subset(indf,site==I))
modeled<-melt(modeled);modeled$site<-I;modeled$mix<-c(rep("C",4),rep("D",4));modeled$variable<-"A"
final<-rbind(tmp,final)
finalNaive<-rbind(naive,finalNaive)
finalMod<-rbind(modeled,finalMod)
}
#calculates meanvalues
final$method<-"DeconRNASeq"
finalNaive$method<-"Naive"
finalMod$method<-"mRNA-correcting"
final<-rbind(final,finalNaive,finalMod)
final<-cbind(subset(final,mix=="C"),subset(final,mix=="D")[,3])
names(final)[6]<-"dval"
final$site<-as.factor(final$site)
levels(final$site)<-c("Lab 1 - ILM", "Lab 2 - ILM", "Lab 3 - ILM", "Lab 4 - ILM", "Lab 5 - ILM" , "Lab 6 - ILM" , "Lab 7 - LT" , "Lab 8 - LT", "Lab 9 - LT")
require(dplyr)
fmean<-group_by(final,method,site,variable)%>%summarise(mean(value),mean(dval),sd(value),sd(dval))
names(fmean)<-c("method","site","variable","meanc","meand","sdc","sdd")
g<-ggplot(final)
g+geom_point(aes(x=value,y=dval,color=(method),pch=method),alpha=0.65,size=4)+coord_cartesian(ylim=c(0.1,0.4),xlim=c(0.6,0.9))+
facet_wrap(~ site)+ylab("Amount of SEQC-A in SEQC-C")+xlab("Amount of SEQC-A in SEQC-D")+geom_point(aes(x=0.75,y=0.25),col="grey70")+theme(legend.position="none")+
geom_path(data=circleFun(center=c(0.75,0.25),diameter=0.1,npoints=25),aes(x,y),col="grey")+
geom_path(data=circleFun(center=c(0.75,0.25),diameter=0.05,npoints=25),aes(x,y),col="grey")+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank())+theme(panel.margin=unit(1,"cm"))+theme(aspect.ratio=1)+
theme(legend.text=element_text(size=rel(1.4)))+theme(axis.title=element_text(size=rel(1.6)))+theme(axis.text=element_text(size=rel(1)))+theme(strip.background = element_rect(fill = 'white'))+theme(strip.text=element_text(size=rel(1.3)))+
geom_pointrange(data=fmean,aes(x=meanc,y=meand,ymax=meand+2*sdd,ymin=meand-2*sdd),size=1.15,shape=1)+geom_errorbarh(data=fmean,aes(x=meanc,y=meand,xmax=meanc+2*sdc,xmin=meanc-2*sdc),size=1.3)
#if axes along each facet are needed, http://stackoverflow.com/questions/17661052/force-x-axis-text-on-for-all-facets-of-a-facet-grid-plot # it's not simple though...
}#now SF3. Target plot: Naive vs Decon vs Us
sf2a<-function(){
gdfa<-(assignIdentity(makerefmetdfb(norm="UQN")))
gdfa$plat<-"HiSeq"
gdfa$mrat<-calcmrnafrac(gdfa)[7]/calcmrnafrac(gdfa)[1] # since the plot is comparing A1 to A2, I take the ratio of their mrna fractions as a term to use later.
gdfb<-assignIdentity(makerefmetdfb(norm=0,platform = "5500"))
gdfb$plat<-"SOLiD 5500"
gdfb$mrat<-calcmrnafrac(gdfb)[8]/calcmrnafrac(gdfb)[1] #this plot is comparing A1 to B2, because i want to look at the ERCC pool effects - thus this mrna fraction ratio correction term
# rownames(gdfa)<-gdfa$gene_id;rownames(gdfb)<-gdfb$gene_id
gdf<-rbind(gdfa,gdfb) #it's later now.
gdf$pme[gdf$plat=="SOLiD 5500"]<-gdfb[,9]#/sum(gdfb[,9])*sum(gdfb[,2]) #this printme variable corresponds to B2 in the data, because yeah that is what's going on in the 5500 data - but is normalized by the library size.
gdf$pme[gdf$plat=="HiSeq"]<-gdfa[,8]#/sum(gdfa[,8])*sum(gdfa[,2]) #this "printme" variable is A2. because. A2 is what i want to look at for this dataset
g<-ggplot(subset(gdf,identity!="unclassified"))
g+geom_point(aes(y=log2(a1*mrat/pme),x=log2(a1/mrat*pme)/2,col=identity,size=identity))+facet_grid(. ~ plat)+scale_colour_manual(values=c("#CC6666","black","#99CC66","#6699CC"),labels=c("Brain","ERCC","Liver","Muscle"))+
theme(legend.position=c(0.9,0.9))+labs(x="A",y=expression(paste(Log[2]," Ratio BLM-1 : BLM-2")))+scale_alpha_manual(values=c(0.7,1,0.7,0.7))+
scale_size_manual(values=c(1.5,3,1.5,1.5),labels=c("Brain","ERCC","Liver","Muscle"))+theme(legend.title=element_blank())+theme(strip.background = element_rect(fill = 'white'))+
scale_x_continuous(limits=c(0,20),expand=c(0,0))+scale_y_continuous(limits=c(-2,2),expand=c(0,0))+guides(colour=guide_legend(override.aes = list(size=4)))+theme(strip.text.x=element_text(size=24))+
theme(axis.title=element_text(size=24))+theme(axis.text=element_text(size=20))+geom_point(data=subset(gdf,identity=="external"),aes(y=log2(a1*mrat/pme),x=log2(a1/mrat*pme)/2,col=identity,size=identity))
}#no longer referred to in the paper
sf4<-function(indf){
require(reshape2);require(grid);require(data.table);require(ggplot2)
if(missing(indf)){
indf<-makeseqcdf()}
#do UQN for ILM sites:
indfILM<-subset(indf,!site%in%c("NWU","PSU","SQW")) #subset to ILM only
indfILM$site<-droplevels(indfILM$site) #clear out those pesky empty factor levels
indfILM<-normalizeSEQC(indfILM,type="Both")
indfLT<-subset(indf,site%in%c("NWU","PSU","SQW"))
indf<-rbind(indfILM,indfLT)
final<-NULL
dset<-cbind(indf[,1:2],rowMeans(indf[,3:6]),rowMeans(indf[,7:10]),(indf[,11:14]),(indf[,15:18]))
names(dset)<-c("gene_id","site","A","B","C","C","C","C","D","D","D","D")
for(I in levels(as.factor(dset$site))){
modeled<-SEQClm(subset(indf,site==I))
modeled<-suppressMessages(melt(modeled));modeled$site<-I;modeled$mix<-c(rep("C",4),rep("D",4));modeled$variable<-"A"
final<-rbind(modeled,final)
}
#calculates meanvalues
final$method<-"mRNA-correcting"
final<-cbind(subset(final,mix=="C"),subset(final,mix=="D")[,2])
names(final)[6]<-"dval"
final$site<-as.factor(final$site)
levels(final$site)<-c("Lab 1 - ILM", "Lab 2 - ILM", "Lab 3 - ILM", "Lab 4 - ILM", "Lab 5 - ILM" , "Lab 6 - ILM" , "Lab 7 - LT" , "Lab 8 - LT", "Lab 9 - LT")
require(dplyr)
fmean<-group_by(final,method,site,variable)%>%summarise(mean(value),mean(dval),sd(value),sd(dval))
names(fmean)<-c("method","site","variable","meanc","meand","sdc","sdd")
g<-ggplot(final)
g+geom_point(aes(x=value,y=dval,color=(site),pch=method),alpha=0.65,size=4)+coord_cartesian(ylim=c(0.1,0.4),xlim=c(0.6,0.9))+
facet_wrap(~ site)+ylab("Amount of SEQC-A in SEQC-C")+xlab("Amount of SEQC-A in SEQC-D")+geom_point(aes(x=0.75,y=0.25),col="grey70")+theme(legend.position="none")+
geom_path(data=circleFun(center=c(0.75,0.25),diameter=0.1,npoints=25),aes(x,y),col="grey")+
geom_path(data=circleFun(center=c(0.75,0.25),diameter=0.05,npoints=25),aes(x,y),col="grey")+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank())+theme(panel.margin=unit(1,"cm"))+theme(aspect.ratio=1)+
theme(legend.text=element_text(size=rel(1.4)))+theme(axis.title=element_text(size=rel(1.6)))+theme(axis.text=element_text(size=rel(1)))+theme(strip.background = element_rect(fill = 'white'))+theme(strip.text=element_text(size=rel(1.3)))+
geom_pointrange(data=fmean,aes(x=meanc,y=meand,ymax=meand+2*sdd,ymin=meand-2*sdd),size=1.15,shape=1)+geom_errorbarh(data=fmean,aes(x=meanc,y=meand,xmax=meanc+2*sdc,xmin=meanc-2*sdc),size=1.3)
#if axes along each facet are needed, http://stackoverflow.com/questions/17661052/force-x-axis-text-on-for-all-facets-of-a-facet-grid-plot # it's not simple though...
}#Now Figure5. SEQC-C and D estimates for interlab.
#Supplemental Figure3:
sf3<-function(){figure<-makeTargetPlot(df=makerefmetdfb(norm="UQN",mapper="RSEM",method="RSEM",readtype="mostcomplex"),
df2 = makerefmetdfb(norm="UQN",mapper="RSEM",method="RSEM",readtype = "default"),Discriminator = "FPKM",numrings = 2)
figure+scale_alpha_manual(name="Unit",breaks=c(0,1),labels=c("Count","FPKM"),values=c(0.3,1))}#Target plot: FPKM (Threw an error last time i used it...)
#Supplemental Figure4:
#Supplemental Table1:
stab1<-function(){
Figure2<-makerefmetdfb(platform = "5500",norm=0) #we use the 5500 data here because the Illumina data has some inconsistencies with the actual spike-in mix amounts. Those inconsistencies don't affect
#ratios, just the absolute values, and it's a pain to understand/explain, but the 5500 data illustrate it just fine.
count<-rbind(colSums(Figure2b2[match(ercc96$name[ercc96$pool=="C"],Figure2b2$gene_id),2:14]),colSums(Figure2b2[grep("ERCC-",Figure2b2$gene_id,invert=TRUE),2:14]))
#must only consider the C subpool of erccs here because pools A and B have designed differences between BLM1a and BLM1b (mixa having 2x as much pool a as mixb)
#I can't conceptually justify this distinction, but it is absolutely important.
#You can get the correct ratios from pool A or pool B after you account for the differences in designed spike-in amount,
#but for a reason that i don't understand, you can NOT just look at the entire set of 96 spike-ins as a whole and assume that the subpools even out (even though they were designed to do so!)
ercc.targ <- c(.08,.08/8,.08*8,.08,.08/8,.08*8,.08,.08,.08/8,.08*8,.003,.003,.003)
table<-rbind(count[1,]/count[2,],ercc.targ,ercc.targ*count[2,]/count[1,])
rownames(table)<-c("ERCC Count Ratio","ERCC spike proportion","Rho")
return(table)
}
sf2<-function(){ #stuff i did to compare mix models using by uqn/tmm to that achieved by calcmrnafrac
##for SEQC:
require(edgeR);require(ggplot2);require(data.table);require(dplyr)
seqcmixmodeling<-function(SEQCDF=SEQCDF,nmethod="TMM",factor="ercc"){
SEQCDFN<-NULL
nflist<-NULL
mflist<-NULL
for(I in levels(SEQCDF$site)){
tmp<-subset(SEQCDF,site==I)
fac<-calcNormFactors(tmp[c(3:18)],method=nmethod)
#first use the normalization factors as they were intended...
tmp[,c(3:18)]<-tmp[,c(3:18)]*fac
if(factor=="ercc"){ fac<-calcmrnafrac(tmp,selection=c(3:18),e5="G")}
if(factor=="none"){ fac<-c(rep(1,8))}
if(factor==nmethod){}
tmp$modelC1<-(fac[1]*tmp$A1*.75)+(fac[5]*tmp$B1*.25)
tmp$modelC1<-tmp$modelC1/sum(tmp$modelC1)*sum(tmp$C1)
tmp$modelC2<-(fac[2]*tmp$A2*.75)+(fac[6]*tmp$B2*.25)
tmp$modelC2<-tmp$modelC2/sum(tmp$modelC2)*sum(tmp$C2)
tmp$modelC3<-(fac[3]*tmp$A3*.75)+(fac[7]*tmp$B3*.25)
tmp$modelC3<-tmp$modelC3/sum(tmp$modelC3)*sum(tmp$C3)
tmp$modelC4<-(fac[4]*tmp$A4*.75)+(fac[8]*tmp$B4*.25)
tmp$modelC4<-tmp$modelC4/sum(tmp$modelC4)*sum(tmp$C4)
tmp$modelD1<-fac[1]*tmp$A1*.25+fac[5]*tmp$B1*.75
tmp$modelD1<-tmp$modelD1/sum(tmp$modelD1)*sum(tmp$D1)
tmp$modelD2<-fac[2]*tmp$A2*.25+fac[6]*tmp$B2*.75
tmp$modelD2<-tmp$modelD2/sum(tmp$modelD2)*sum(tmp$D2)
tmp$modelD3<-fac[3]*tmp$A3*.25+fac[7]*tmp$B3*.75
tmp$modelD3<-tmp$modelD3/sum(tmp$modelD3)*sum(tmp$D3)
tmp$modelD4<-fac[4]*tmp$A4*.25+fac[8]*tmp$B4*.75
tmp$modelD4<-tmp$modelD4/sum(tmp$modelD4)*sum(tmp$D4)
nflist<-rbind(nflist,fac)
mflist<-rbind(mflist,calcmrnafrac(tmp,selection=c(3:18),e5="G"))
SEQCDFN<-rbind(SEQCDFN,tmp)
}
return(SEQCDFN)
}
blmmixnormmodels<-function(normtype="TMM",factor="ercc"){
tmp<-makerefmetdfb()
if(normtype=="TMM"){
require(edgeR)
fac<-c(calcNormFactors(tmp[2:14],method="TMM"))#,calcNormFactors(tmp[12:14],method="TMM"))
# nfac<-c(nfac,calcNormFactors(tdf[12:14]))
for(I in 2:14){tmp[I]<-tmp[I]*fac[I-1]}
# return(tmp)
}
if(normtype=="upperquartile"|normtype=="UQN"){
require(edgeR)
fac<-c(calcNormFactors(tmp[2:11],method="upperquartile"),calcNormFactors(tmp[12:14],method="upperquartile"))
# nfac<-c(nfac,calcNormFactors(tdf[12:14]))
for(I in 2:14){tmp[I]<-tmp[I]*fac[I-1]}
# return(tmp)
}
mixFrac1<-c(.25,.25,.5);mixFrac2<-c(.25,.5,.25)
if(factor=="ercc"){fac<-calcmrnafrac(tmp)}
tmp$pred1<-(tmp$bep*mixFrac1[1]*fac[11])+(tmp$lep*mixFrac1[2]*fac[12])+(tmp$mep*mixFrac1[3]*fac[13])
tmp$pred2<-(tmp$bep*mixFrac2[1]*fac[11])+(tmp$lep*mixFrac2[2]*fac[12])+(tmp$mep*mixFrac2[3]*fac[13])
tmp$pred1<-tmp$pred1/sum(tmp$pred1)*sum(tmp$a1)
tmp$pred2<-tmp$pred2/sum(tmp$pred2)*sum(tmp$a2)
return(tmp)
}
tmm<-seqcmixmodeling(makeseqcdf(),factor="none")
uqn<-seqcmixmodeling(makeseqcdf(),nmethod="upperquartile",factor="none")
tmm$norm<-"tmm"
uqn$norm<-"uqn"
fdf<-rbind(tmm,uqn)
fdf$xm<-rowMeans(fdf[,c("D1","D2","D3","D4")])
fdf$Mxm<-rowMeans(fdf[,c("modelD1","modelD2","modelD3","modelD4")])
fdf$dset<-"SEQC"
tmm<-blmmixnormmodels(factor="none")
uqn<-blmmixnormmodels(normtype="upperquartile",factor="none")
tmm$norm<-"tmm"
uqn$norm<-"uqn"
mdf<-rbind(tmm,uqn)
mdf$dset<-"BLM"
mdf$xm<-mdf$a1
mdf$Mxm<-mdf$pred1
odf<-rbind(fdf[,c("Mxm","xm","norm","dset")],mdf[,c("Mxm","xm","norm","dset")])
#none<-blmmixnormmodels()
g<-ggplot(summarise(group_by(odf,norm,dset),rmsd=sqrt(mean((Mxm-xm)^2))))
fig<-g+geom_bar(aes(x=norm,y=rmsd),stat="identity")+facet_wrap(~dset)
return(fig)
}#figure at least demonstrates the gist of what i'm going for here...
#Reviewer-Suggested Figures:
RevS1<-function(){
#dispersion:
g<-makerefmetdfb(norm="UQN")
g<-calcmodel(assignIdentity(g))
g<-subset(g,identity!="external")
g<-g[grep("rRNA",g$gene_id,invert=TRUE),]#get rid of those pesky rRNA genes
theme_set(theme_bw(base_size=18))
ggplot(data=g)+geom_point(aes(x=log2(a1),y=cv))+xlab("mean expression")
#linearity
ggplot(data=g)+geom_point(aes(x=log2(a1),y=log2(pred1*1603/1374)))+xlab("log counts (observed)")+ylab("log counts(predicted)")
#deviation from linearity
ggplot(data=g)+geom_point(aes(x=log2(a1),y=log2(pred1*1603/1374)-log2(a1)))+xlab("log counts (observed)")+ylab("deviation from linearity (predicted - observed)")
#note: the 1603/1374 term is simply because the sum of predicted counts isn't equal to the sum of observed counts - it's just a normalizing term for (predicted) library size
}#"Rich plot of the linearity, dispersion, etc"
####Actual code to do stuff. Called by Figure Functions####
circleFun<-function(center = c(0,0),diameter = 1, npoints = 100){
r = diameter / 2
tt <- seq(0,2*pi,length.out = npoints)
xx <- center[1] + r * cos(tt)
yy <- center[2] + r * sin(tt)
return(data.frame(x = xx, y = yy))
}#Generates dataframes of npoints points that form a circle centered at point center.
makerefmetdfb<-function(platform="ILLUMINA",mapper="TH",method="HTS",reference="UCSC",readtype="default",norm=0,reps=1,...){
#goal is to make this updated to use fread rather than fread because hella slow...problems: Also quite a pain in the ass...
require(data.table)
{ string<-paste(platform,mapper,method,reference,sep="/")
a1<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/BLM1a.ct",sep="")
a1d<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/BLM1aD.ct",sep="")
a1u<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/BLM1aU.ct",sep="")
b1<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/BLM1b.ct",sep="")
b1d<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/BLM1bD.ct",sep="")
b1u<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/BLM1bU.ct",sep="")
a2<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/BLM2a.ct",sep="")
b2<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/BLM2b.ct",sep="")
b2d<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/BLM2bD.ct",sep="")
b2u<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/BLM2bU.ct",sep="")
bep<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/BEP.ct",sep="")
lep<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/LEP.ct",sep="")
mep<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/MEP.ct",sep="")
clct<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/CL.ct",sep="")
bct<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/BCT.ct",sep="")
lct<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/LCT.ct",sep="")
mct<-paste("/Volumes/Archive2b/RawData/BLM/Countdata/",string,"/MCT.ct",sep="")
if(file.exists(b1)==FALSE){if(file.exists(clct)==FALSE){return(0)}}##check to see if there is actually data to collect. If not, don't throw an error. How massive!
if(length(grep("HTS",method))==1){
int<-fread(a1,sep="\t",select=1)
a1<-rowSums(fread(a1,sep="\t",drop=1))
a1d<-rowSums(fread(a1d,sep="\t",drop=1))
a1u<-rowSums(fread(a1u,sep="\t",drop=1))
b1<-rowSums(fread(b1,sep="\t",drop=1))
b1d<-rowSums(fread(b1d,sep="\t",drop=1))
b1u<-rowSums(fread(b1u,sep="\t",drop=1))
a2<-rowSums(fread(a2,sep="\t",drop=1))
b2<-rowSums(fread(b2,sep="\t",drop=1))
b2d<-rowSums(fread(b2d,sep="\t",drop=1))
b2u<-rowSums(fread(b2u,sep="\t",drop=1))
bep<-rowSums(fread(bep,sep="\t",drop=1))
lep<-rowSums(fread(lep,sep="\t",drop=1))
mep<-rowSums(fread(mep,sep="\t",drop=1))
}#some specific bits to make input from hts
else if(length(grep("LS",method))==1){
if(platform=="5500"){
int<-(fread(a1,sep="\t",select=1))
a1<-fread(a1,sep="\t",select=6)#6 is count, 8 is RPKM
a1d<-(fread(a1d,sep="\t",select=6))
a1u<-(fread(a1u,sep="\t",select=6))
b1<-(fread(b1,sep="\t",select=6))
b1d<-(fread(b1d,sep="\t",select=6))
b1u<-(fread(b1u,sep="\t",select=6))
a2<-(fread(a2,sep="\t",select=6))
b2<-(fread(b2,sep="\t",select=6))
b2d<-(fread(b2d,sep="\t",select=6))
b2u<-(fread(b2u,sep="\t",select=6))
bep<-(fread(bep,sep="\t",select=6))
lep<-(fread(lep,sep="\t",select=6))
mep<-(fread(mep,sep="\t",select=6))
}
else if (platform=="SOLID4"){
int<-fread(a1,select=1)
a1<-rowSums(fread(a1)[2:3])
a1d<-rowSums(fread(a1d)[2:3])
a1u<-rowSums(fread(a1u)[2:3])
b1<-rowSums(fread(b1)[2:3])
b1d<-rowSums(fread(b1d)[2:3])
b1u<-rowSums(fread(b1u)[2:3])
a2<-rowSums(fread(a2)[2:3])
b2<-rowSums(fread(b2)[2:3])
b2d<-rowSums(fread(b2d)[2:3])
b2u<-rowSums(fread(b2u)[2:3])
bep<-rowSums(fread(bep)[2:3])
lep<-rowSums(fread(lep)[2:3])
mep<-rowSums(fread(mep)[2:3])
}
}# LS input
else if (method=="CUFF"){
if(reps==1){
int<-(fread(a1,sep="\t",select=1))
a1<-(fread(a1,sep="\t",header=FALSE,select=8))
a1d<-(fread(a1d,sep="\t",header=FALSE,select=8))
a1u<-(fread(a1u,sep="\t",header=FALSE,select=8))
b1<-(fread(b1,sep="\t",header=FALSE,select=8))
b1d<-(fread(b1d,sep="\t",header=FALSE,select=8))
b1u<-(fread(b1u,sep="\t",header=FALSE,select=8))
a2<-(fread(a2,sep="\t",header=FALSE,select=8))
b2<-(fread(b2,sep="\t",header=FALSE,select=8))
b2d<-(fread(b2d,sep="\t",header=FALSE,select=8)) #this one might need to switch to 8 depending on how i set it up....instead use reversecuffdiffoutput.pl
b2u<-(fread(b2u,sep="\t",header=FALSE,select=8)) #if so, this one should be back to 7, probably...
bep<-(fread(bep,sep="\t",header=FALSE,select=8))
lep<-(fread(lep,sep="\t",header=FALSE,select=8))
mep<-(fread(mep,sep="\t",header=FALSE,select=8)) #same with this one
}
else if(reps==0){
int<-(fread(a1,sep="\t",select=1))
a1<-(fread(a1,sep="\t",header=FALSE,select=8))
a1d<-(fread(a1d,sep="\t",header=FALSE,select=8))
a1u<-(fread(a1u,sep="\t",header=FALSE,select=8))
b1<-(fread(b1,sep="\t",header=FALSE,select=8))
b1d<-(fread(b1d,sep="\t",header=FALSE,select=8))
b1u<-(fread(b1u,sep="\t",header=FALSE,select=8))
a2<-(fread(a2,sep="\t",header=FALSE,select=8))
b2<-(fread(b2,sep="\t",header=FALSE,select=8))
b2d<-(fread(b2d,sep="\t",header=FALSE,select=8)) #this one might need to switch to 8 depending on how i set it up....instead use reversecuffdiffoutput.pl
b2u<-(fread(b2u,sep="\t",header=FALSE,select=8)) #if so, this one should be back to 7, probably...
bep<-(fread(bep,sep="\t",header=FALSE,select=8))
lep<-(fread(lep,sep="\t",header=FALSE,select=8))
mep<-(fread(mep,sep="\t",header=FALSE,select=8)) #same with this one
}
}#more specific bits for CUFF
else if (method=="CUFFtx"|method=="CUFFTX"){
if(reps==1){
int<-(fread(a1,sep="\t",select=1))
a1<-(fread(a1,sep="\t",header=FALSE,select=8))
a1d<-(fread(a1d,sep="\t",header=FALSE,select=8))
a1u<-(fread(a1u,sep="\t",header=FALSE,select=8))
b1<-(fread(b1,sep="\t",header=FALSE,select=8))
b1d<-(fread(b1d,sep="\t",header=FALSE,select=8))
b1u<-(fread(b1u,sep="\t",header=FALSE,select=8))
a2<-(fread(a2,sep="\t",header=FALSE,select=8))
b2<-(fread(b2,sep="\t",header=FALSE,select=8))
b2d<-(fread(b2d,sep="\t",header=FALSE,select=8)) #this one might need to switch to 8 depending on how i set it up....instead use reversecuffdiffoutput.pl
b2u<-(fread(b2u,sep="\t",header=FALSE,select=8)) #if so, this one should be back to 7, probably...
bep<-(fread(bep,sep="\t",header=FALSE,select=8))
lep<-(fread(lep,sep="\t",header=FALSE,select=8))
mep<-(fread(mep,sep="\t",header=FALSE,select=8)) #same with this one
}
else if(reps==0){
int<-(fread(a1,sep="\t",select=1))
a1<-(fread(a1,sep="\t",header=FALSE,select=8))
a1d<-(fread(a1d,sep="\t",header=FALSE,select=8))
a1u<-(fread(a1u,sep="\t",header=FALSE,select=8))
b1<-(fread(b1,sep="\t",header=FALSE,select=8))
b1d<-(fread(b1d,sep="\t",header=FALSE,select=8))
b1u<-(fread(b1u,sep="\t",header=FALSE,select=8))
a2<-(fread(a2,sep="\t",header=FALSE,select=8))
b2<-(fread(b2,sep="\t",header=FALSE,select=8))
b2d<-(fread(b2d,sep="\t",header=FALSE,select=8)) #this one might need to switch to 8 depending on how i set it up....instead use reversecuffdiffoutput.pl
b2u<-(fread(b2u,sep="\t",header=FALSE,select=8)) #if so, this one should be back to 7, probably...
bep<-(fread(bep,sep="\t",header=FALSE,select=8))
lep<-(fread(lep,sep="\t",header=FALSE,select=8))
mep<-(fread(mep,sep="\t",header=FALSE,select=8)) #same with this one
}
}#more specific bits for CUFF
else if(method=="CLCT"){
if(mapper=="LS"){
a1<-(fread(clct,sep="\t",header=TRUE,select=1))
b1<-(fread(clct,sep="\t",header=TRUE,select=16))
a2<-(fread(clct,sep="\t",header=TRUE,select=31))
b2<-(fread(clct,sep="\t",header=TRUE,select=36))
b2d<-(fread(clct,sep="\t",header=TRUE,select=46))
a1d<-(fread(clct,sep="\t",header=TRUE,select=11))
b1d<-(fread(clct,sep="\t",header=TRUE,select=26))
a1u<-(fread(clct,sep="\t",header=TRUE,select=6))
b1u<-(fread(clct,sep="\t",header=TRUE,select=21))
b2u<-(fread(clct,sep="\t",header=TRUE,select=41))
bep<-fread(bct,sep="\t",header=TRUE,select=1)
lep<-fread(lct,sep="\t",header=TRUE,select=6)
mep<-fread(mct,sep="\t",header=TRUE,select=11)
#idk what int should be here...do i have to recalibrate everything?
}
else if(mapper=="TH"){
if(platform=="ILLUMINA"){
a1<-(fread(clct,sep="\t",header=TRUE,select=1))
b1<-(fread(clct,sep="\t",header=TRUE,select=6))
a2<-(fread(clct,sep="\t",header=TRUE,select=11))
b2<-(fread(clct,sep="\t",header=TRUE,select=16))
b2d<-(fread(clct,sep="\t",header=TRUE,select=21))
a1d<-(fread(clct,sep="\t",header=TRUE,select=26))
b1d<-(fread(clct,sep="\t",header=TRUE,select=31))
a1u<-(fread(clct,sep="\t",header=TRUE,select=36))
b1u<-(fread(clct,sep="\t",header=TRUE,select=41))
b2u<-(fread(clct,sep="\t",header=TRUE,select=46))
bep<-fread(bct,sep="\t",header=TRUE,select=1)
lep<-fread(lct,sep="\t",header=TRUE,select=6)
mep<-fread(mct,sep="\t",header=TRUE,select=11)
}
else if(platform=="5500"){
a1<-(fread(clct,sep="\t",header=TRUE,select=1))
b1<-(fread(clct,sep="\t",header=TRUE,select=16))
a2<-(fread(clct,sep="\t",header=TRUE,select=31))
b2<-(fread(clct,sep="\t",header=TRUE,select=46))
b2d<-(fread(clct,sep="\t",header=TRUE,select=36))
a1d<-(fread(clct,sep="\t",header=TRUE,select=6))
b1d<-(fread(clct,sep="\t",header=TRUE,select=26))
a1u<-(fread(clct,sep="\t",header=TRUE,select=11))
b1u<-(fread(clct,sep="\t",header=TRUE,select=21))
b2u<-(fread(clct,sep="\t",header=TRUE,select=41))
bep<-fread(bct,sep="\t",header=TRUE,select=1)
lep<-fread(lct,sep="\t",header=TRUE,select=6)
mep<-fread(mct,sep="\t",header=TRUE,select=11)
}
else if(platform=="SOLID4"){
a1<-(fread(clct,sep="\t",header=TRUE,select=1))
a1d<-(fread(clct,sep="\t",header=TRUE,select=6))
a1u<-(fread(clct,sep="\t",header=TRUE,select=11))
b1<-(fread(clct,sep="\t",header=TRUE,select=16))
b1d<-(fread(clct,sep="\t",header=TRUE,select=21))
b1u<-(fread(clct,sep="\t",header=TRUE,select=26))
a2<-(fread(clct,sep="\t",header=TRUE,select=31))
b2<-(fread(clct,sep="\t",header=TRUE,select=36))
b2d<-(fread(clct,sep="\t",header=TRUE,select=41))
b2u<-(fread(clct,sep="\t",header=TRUE,select=46))
bep<-fread(bct,sep="\t",header=TRUE,select=1)
lep<-fread(lct,sep="\t",header=TRUE,select=6)
mep<-fread(mct,sep="\t",header=TRUE,select=11)
}
}
}
else if(method=="SAIL"){
if(readtype=="default"){a1t<-a1
a1<-(fread(a1,sep="\t",select=3))###3 is TPM, 4 is FPKM
a1d<-(fread(a1d,sep="\t",select=3))
a1u<-(fread(a1u,sep="\t",select=3))
b1<-(fread(b1,sep="\t",select=3))
b1d<-(fread(b1d,sep="\t",select=3))
b1u<-(fread(b1u,sep="\t",select=3))
a2<-(fread(a2,sep="\t",select=3))
b2<-(fread(b2,sep="\t",select=3))
b2d<-(fread(b2d,sep="\t",select=3))
b2u<-(fread(b2u,sep="\t",select=3))
bep<-(fread(bep,sep="\t",select=3))
lep<-(fread(lep,sep="\t",select=3))
mep<-(fread(mep,sep="\t",select=3))
int<-(fread(a1t,sep="\t",select=1))
}
else if(readtype=="mostcomplex"){a1t<-a1
a1<-(fread(a1,sep="\t",select=4))###3 is TPM, 4 is FPKM
a1d<-(fread(a1d,sep="\t",select=4))
a1u<-(fread(a1u,sep="\t",select=4))
b1<-(fread(b1,sep="\t",select=4))
b1d<-(fread(b1d,sep="\t",select=4))
b1u<-(fread(b1u,sep="\t",select=4))
a2<-(fread(a2,sep="\t",select=4))
b2<-(fread(b2,sep="\t",select=4))
b2d<-(fread(b2d,sep="\t",select=4))
b2u<-(fread(b2u,sep="\t",select=4))
bep<-(fread(bep,sep="\t",select=4))
lep<-(fread(lep,sep="\t",select=4))
mep<-(fread(mep,sep="\t",select=4))
int<-(fread(a1t,sep="\t",select=1))
}
}
else if (method=="RSEM"){
if(readtype=="default"){a1t<-a1
a1<-(fread(a1,sep="\t",select=7))###5 is ExpectedCount, 6 is TPM, 7 is FPKM
a1d<-(fread(a1d,sep="\t",select=7))
a1u<-(fread(a1u,sep="\t",select=7))
b1<-(fread(b1,sep="\t",select=7))
b1d<-(fread(b1d,sep="\t",select=7))
b1u<-(fread(b1u,sep="\t",select=7))
a2<-(fread(a2,sep="\t",select=7))
b2<-(fread(b2,sep="\t",select=7))
b2d<-(fread(b2d,sep="\t",select=7))
b2u<-(fread(b2u,sep="\t",select=7))
bep<-(fread(bep,sep="\t",select=7))
lep<-(fread(lep,sep="\t",select=7))
mep<-(fread(mep,sep="\t",select=7))
int<-(fread(a1t,sep="\t",select=1))
}
if(readtype=="mostcomplex"){
a1t<-a1
a1<-(fread(a1,sep="\t",select=6))###5 is ExpectedCount, 6 is TPM, 7 is FPKM
a1d<-(fread(a1d,sep="\t",select=6))
a1u<-(fread(a1u,sep="\t",select=6))
b1<-(fread(b1,sep="\t",select=6))
b1d<-(fread(b1d,sep="\t",select=6))
b1u<-(fread(b1u,sep="\t",select=6))
a2<-(fread(a2,sep="\t",select=6))
b2<-(fread(b2,sep="\t",select=6))
b2d<-(fread(b2d,sep="\t",select=6))
b2u<-(fread(b2u,sep="\t",select=6))
bep<-(fread(bep,sep="\t",select=6))
lep<-(fread(lep,sep="\t",select=6))
mep<-(fread(mep,sep="\t",select=6))
int<-(fread(a1t,sep="\t",select=1))
}
if(readtype=="mostbasic"){
a1t<-a1
a1<-(fread(a1,sep="\t",select=5))###5 is ExpectedCount, 6 is TPM, 7 is FPKM
a1d<-(fread(a1d,sep="\t",select=5))
a1u<-(fread(a1u,sep="\t",select=5))
b1<-(fread(b1,sep="\t",select=5))
b1d<-(fread(b1d,sep="\t",select=5))
b1u<-(fread(b1u,sep="\t",select=5))
a2<-(fread(a2,sep="\t",select=5))
b2<-(fread(b2,sep="\t",select=5))
b2d<-(fread(b2d,sep="\t",select=5))
b2u<-(fread(b2u,sep="\t",select=5))
bep<-(fread(bep,sep="\t",select=5))
lep<-(fread(lep,sep="\t",select=5))
mep<-(fread(mep,sep="\t",select=5))
int<-(fread(a1t,sep="\t",select=1))
}
}#RSEM input
else if (method=="RSEMc"){
a1t<-a1
a1<-(fread(a1,header=TRUE,sep="\t",select=5))###5 is ExpectedCount, 6 is TPM, 7 is FPKM
a1d<-(fread(a1d,header=TRUE,sep="\t",select=5))
a1u<-(fread(a1u,header=TRUE,sep="\t",select=5))
b1<-(fread(b1,header=TRUE,sep="\t",select=5))
b1d<-(fread(b1d,header=TRUE,sep="\t",select=5))
b1u<-(fread(b1u,header=TRUE,sep="\t",select=5))
a2<-(fread(a2,header=TRUE,sep="\t",select=5))
b2<-(fread(b2,header=TRUE,sep="\t",select=5))
b2d<-(fread(b2d,header=TRUE,sep="\t",select=5))
b2u<-(fread(b2u,header=TRUE,sep="\t",select=5))
bep<-(fread(bep,header=TRUE,sep="\t",select=5))
lep<-(fread(lep,header=TRUE,sep="\t",select=5))
mep<-(fread(mep,header=TRUE,sep="\t",select=5))
int<-(fread(a1t,header=TRUE,sep="\t",select=1))
int2<-(fread(a1t,header=TRUE,sep="\t",select=2))
}
else if (method=="RSEMt"){
if(readtype=="default"){
a1t<-a1
a1<-(fread(a1,sep="\t",header=TRUE,select=5))###5 is ExpectedCount, 6 is TPM, 7 is FPKM
a1d<-(fread(a1d,sep="\t",header=TRUE,select=5))
a1u<-(fread(a1u,sep="\t",header=TRUE,select=5))
b1<-(fread(b1,sep="\t",header=TRUE,select=5))
b1d<-(fread(b1d,sep="\t",header=TRUE,select=5))
b1u<-(fread(b1u,sep="\t",header=TRUE,select=5))
a2<-(fread(a2,sep="\t",header=TRUE,select=5))
b2<-(fread(b2,sep="\t",header=TRUE,select=5))
b2d<-(fread(b2d,sep="\t",header=TRUE,select=5))
b2u<-(fread(b2u,sep="\t",header=TRUE,select=5))
bep<-(fread(bep,sep="\t",header=TRUE,select=5))
lep<-(fread(lep,sep="\t",header=TRUE,select=5))
mep<-(fread(mep,sep="\t",header=TRUE,select=5))
int<-(fread(a1t,sep="\t",header=TRUE,select=1))
int2<-(fread(a1t,sep="\t",header=TRUE,select=2))
}
else if(readtype=="mostbasic"){
a1t<-a1
a1<-(fread(a1,sep="\t",header=TRUE,select=6))###5 is ExpectedCount, 6 is TPM, 7 is FPKM
a1d<-(fread(a1d,sep="\t",header=TRUE,select=6))
a1u<-(fread(a1u,sep="\t",header=TRUE,select=6))
b1<-(fread(b1,sep="\t",header=TRUE,select=6))
b1d<-(fread(b1d,sep="\t",header=TRUE,select=6))
b1u<-(fread(b1u,sep="\t",header=TRUE,select=6))
a2<-(fread(a2,sep="\t",header=TRUE,select=6))
b2<-(fread(b2,sep="\t",header=TRUE,select=6))
b2d<-(fread(b2d,sep="\t",header=TRUE,select=6))
b2u<-(fread(b2u,sep="\t",header=TRUE,select=6))
bep<-(fread(bep,sep="\t",header=TRUE,select=6))
lep<-(fread(lep,sep="\t",header=TRUE,select=6))
mep<-(fread(mep,sep="\t",header=TRUE,select=6))
int<-(fread(a1t,sep="\t",header=TRUE,select=1))
int2<-(fread(a1t,sep="\t",header=TRUE,select=2))
}
if(readtype=="mostcomplex"){
a1t<-a1
a1<-(fread(a1,sep="\t",header=TRUE,select=7))###5 is ExpectedCount, 6 is TPM, 7 is FPKM
a1d<-(fread(a1d,sep="\t",header=TRUE,select=7))
a1u<-(fread(a1u,sep="\t",header=TRUE,select=7))
b1<-(fread(b1,sep="\t",header=TRUE,select=7))
b1d<-(fread(b1d,sep="\t",header=TRUE,select=7))
b1u<-(fread(b1u,sep="\t",header=TRUE,select=7))
a2<-(fread(a2,sep="\t",header=TRUE,select=7))
b2<-(fread(b2,sep="\t",header=TRUE,select=7))
b2d<-(fread(b2d,sep="\t",header=TRUE,select=7))
b2u<-(fread(b2u,sep="\t",header=TRUE,select=7))
bep<-(fread(bep,sep="\t",header=TRUE,select=7))
lep<-(fread(lep,sep="\t",header=TRUE,select=7))
mep<-(fread(mep,sep="\t",header=TRUE,select=7))
int<-(fread(a1t,sep="\t",header=TRUE,select=1))
int2<-(fread(a1t,sep="\t",header=TRUE,select=2))
}
}#RSEM transcriptome input
else if (method=="xpress"|method=="xpress2"|method=="xpress3"){
if(readtype=="default"){
a1t<-a1
a1<-(fread(a1,sep="\t",header=TRUE,select=8))###7 is expected counts, 8 is effective counts
a1d<-(fread(a1d,sep="\t",header=TRUE,select=8))
a1u<-(fread(a1u,sep="\t",header=TRUE,select=8))
b1<-(fread(b1,sep="\t",header=TRUE,select=8))
b1d<-(fread(b1d,sep="\t",header=TRUE,select=8))
b1u<-(fread(b1u,sep="\t",header=TRUE,select=8))
a2<-(fread(a2,sep="\t",header=TRUE,select=8))
b2<-(fread(b2,sep="\t",header=TRUE,select=8))
b2d<-(fread(b2d,sep="\t",header=TRUE,select=8))
b2u<-(fread(b2u,sep="\t",header=TRUE,select=8))
bep<-(fread(bep,sep="\t",header=TRUE,select=8))
lep<-(fread(lep,sep="\t",header=TRUE,select=8))
mep<-(fread(mep,sep="\t",header=TRUE,select=8))
int<-(fread(a1t,sep="\t",header=TRUE,select=2)) #1 is txid, 2 is gene_id
}
else if(readtype=="mostbasic"){
a1t<-a1
a1<-(fread(a1,sep="\t",header=TRUE,select=7))###7 is expected counts, 8 is effective counts
a1d<-(fread(a1d,sep="\t",header=TRUE,select=7))
a1u<-(fread(a1u,sep="\t",header=TRUE,select=7))
b1<-(fread(b1,sep="\t",header=TRUE,select=7))
b1d<-(fread(b1d,sep="\t",header=TRUE,select=7))
b1u<-(fread(b1u,sep="\t",header=TRUE,select=7))
a2<-(fread(a2,sep="\t",header=TRUE,select=7))
b2<-(fread(b2,sep="\t",header=TRUE,select=7))
b2d<-(fread(b2d,sep="\t",header=TRUE,select=7))
b2u<-(fread(b2u,sep="\t",header=TRUE,select=7))
bep<-(fread(bep,sep="\t",header=TRUE,select=7))
lep<-(fread(lep,sep="\t",header=TRUE,select=7))
mep<-(fread(mep,sep="\t",header=TRUE,select=7))
int<-(fread(a1t,sep="\t",header=TRUE,select=2)) #1 is txid, 2 is gene_id
}
else if(readtype=="mostcomplex"){
a1t<-a1
a1<-(fread(a1,sep="\t",header=TRUE,select=11))###7 is expected counts, 8 is effective counts
a1d<-(fread(a1d,sep="\t",header=TRUE,select=11))
a1u<-(fread(a1u,sep="\t",header=TRUE,select=11))
b1<-(fread(b1,sep="\t",header=TRUE,select=11))
b1d<-(fread(b1d,sep="\t",header=TRUE,select=11))
b1u<-(fread(b1u,sep="\t",header=TRUE,select=11))
a2<-(fread(a2,sep="\t",header=TRUE,select=11))
b2<-(fread(b2,sep="\t",header=TRUE,select=11))
b2d<-(fread(b2d,sep="\t",header=TRUE,select=11))
b2u<-(fread(b2u,sep="\t",header=TRUE,select=11))
bep<-(fread(bep,sep="\t",header=TRUE,select=11))
lep<-(fread(lep,sep="\t",header=TRUE,select=11))
mep<-(fread(mep,sep="\t",header=TRUE,select=11))
int<-(fread(a1t,sep="\t",header=TRUE,select=2)) #1 is txid, 2 is gene_id
}
}#xpress transcriptome input
if(length(names(a1))>1000){
tdf<-data.frame(names(a1),a1,a1d,a1u,b1,b1d,b1u,a2,b2,b2d,b2u,bep,lep,mep)} #ensures naming accuracy
else if(length(grep("RSEM|LS|RSEMt|xpress|SAIL|CUFF",method))>0){
tdf<-data.frame(int,a1,a1d,a1u,b1,b1d,b1u,a2,b2,b2d,b2u,bep,lep,mep)} #reverts from separate RSEM naming convention to 'standard'
else if(length(grep("RSEMt|RSEMc",method))>0){
tdf<-data.frame(int,a1,a1d,a1u,b1,b1d,b1u,a2,b2,b2d,b2u,bep,lep,mep,int2)} #reverts from separate RSEM naming convention to 'standard'}
else{tdf<-data.frame(int,a1,a1d,a1u,b1,b1d,b1u,a2,b2,b2d,b2u,bep,lep,mep)} #standard naming
if(length(tdf)==14){
colnames(tdf)<-c("gene_id","a1","a1d","a1u","b1","b1d","b1u","a2","b2","b2d","b2u","bep","lep","mep")}
else if(length(tdf)==15){ colnames(tdf)<-c("gene_id","a1","a1d","a1u","b1","b1d","b1u","a2","b2","b2d","b2u","bep","lep","mep","geneid")}
}
#read in and name count data from output files
{tdf<-tdf[grep("unknown|no_feature|ambiguous|too_low_aQual|not_aligned|alignment_not_unique",tdf$gene_id,invert=TRUE),]
tdf$norm<-norm
if(norm>0){tdf<-normcountdf(tdf,norm)}#do norm if norm==1
#tdf$mean1<-rowMeans(data.frame(tdf$a1,tdf$a1d,tdf$a1u,tdf$b1,tdf$b1d,tdf$b1u))
#tdf$mean2<-rowMeans(data.frame(tdf$a2,tdf$b2,tdf$b2d,tdf$b2u))
#tdf$obsM<-log2((tdf$mean1)/(tdf$mean2))
#tdf$obsM[is.na(tdf$obsM)]<-0
#tdf$obsM[tdf$obsM==Inf]<-0
#tdf$obsM[tdf$obsM==-Inf]<-0
#tdf$obsA<-log2(tdf$mean1*tdf$mean2)/2
#tdf$obsA[tdf$obsA==-Inf]<-0;tdf$obsA[is.na(tdf$obsA)]<-0
# tdf$obsM2<-log2(round(tdf$mean1)/round(tdf$mean2))
#tdf$expM<-log2((.25*tdf$bep+.5*tdf$mep+.25*tdf$lep)/(.25*tdf$bep+.5*tdf$lep+.25*tdf$mep))
# tdf$expM[is.na(tdf$expM)]<-0
# tdf$expM[tdf$expM==Inf]<-0
# tdf$expM[tdf$expM==-Inf]<-0
# act<-calculateexpectedmrnafractions(tdf)
#return(act) ##comment this line out when i'm through today (06/19/13)
# tdf$expM2<-log2((act[1]*tdf$bep+act[2]*tdf$mep+act[3]*tdf$lep)/(act[4]*tdf$bep+act[5]*tdf$lep+act[6]*tdf$mep))
# tdf$expM2[is.na(tdf$expM2)]<-0
# tdf$expM2[tdf$expM2==Inf]<-0
# tdf$expM2[tdf$expM2==-Inf]<-0
# tdf$expM3<-log2((act[1]*tdf$bep+act[2]*tdf$lep+act[3]*tdf$mep)/(act[4]*tdf$bep+act[5]*tdf$lep+act[6]*tdf$mep))
# tdf$expM3[is.na(tdf$expM3)]<-0
# tdf$expM3[tdf$expM3==Inf]<-0
# tdf$expM3[tdf$expM3==-Inf]<-0
# tdf$eM1<-((act[1]*tdf$bep+act[2]*tdf$lep+act[3]*tdf$mep))
# tdf$eM2<-((act[4]*tdf$bep+act[5]*tdf$lep+act[6]*tdf$mep))
# tdf$reference<-reference
#tdf$platform<-platform
#tdf$mapper<-mapper
#tdf$method<-method
tdf$uid<-paste(platform,mapper,method,reference,sep="")
tdf[tdf==Inf]<-NA
tdf[tdf==-Inf]<-NA
#tdf$cutoff<-summary(tdf$obsA[tdf$obsA>0])[3]
#trying to just do it all at once:
}#remove HTS_unknown features ; run normcountdf ; calculate means/MA, add metadata, change Inf to NA
#tdf$var1<-(((tdf$a1-tdf$mean1)^2+(tdf$a1d-tdf$mean1)^2+(tdf$a1u-tdf$mean1)^2+(tdf$b1-tdf$mean1)^2+(tdf$b1u-tdf$mean1)^2+(tdf$b1d-tdf$mean1)^2)*1/5)
#tdf$var2<-(((tdf$a2-tdf$mean2)^2+(tdf$b2-tdf$mean2)^2+(tdf$b2u-tdf$mean2)^2+(tdf$b2d-tdf$mean2)^2)*1/3)
#tdf<-predictmixfromblm(tdf)
#tdf<-bootstrapdMranges(tdf,reps)
return(tdf)
}#reads input files (Must upload those files somewhere & change the code to read those files from the remote server)
makeTargetPlot<-function(type="BLM",df,df2,Discriminator="CheeseValue",numrings=4,dfr=FALSE){
require(reshape2)
require(ggplot2)
require(dplyr)
theme_set(theme_bw(base_size=16))
if(type=="BLM"){ a<-blmmixfraction(df);
a$mixnum<-c(1,1,1,1,1,1,2,2,2,2);
a[11,]<-c(0,0,0,2);a[12,]<-c(0,0,0,2);
a<-a[c(1:4,7:10),]#we *are* ignoring the points on the line.
a<-as.data.frame(melt(a,id.vars=4))
a<-data.frame(a[a$mixnum==1,],a[a$mixnum==2,])
colnames(a)<-c("mixnum","Component","value1","blah","blah","value2")
a<-a[,c("Component","value1","value2")]
}
if(type=="BLM"){
if(!missing(df2)){b<-blmmixfraction(df2);
b$mixnum<-c(1,1,1,1,1,1,2,2,2,2);
b[11,]<-c(0,0,0,2);b[12,]<-c(0,0,0,2);
b<-b[c(1:4,7:10),] #we *are* ignoring the points on the line.
b<-as.data.frame(melt(b,id.vars=4))
b<-data.frame(b[b$mixnum==1,],b[b$mixnum==2,])
colnames(b)<-c("mixnum","Component","value1","blah","blah","value2")
b<-b[,c("Component","value1","value2")]
#sumdf2<-data.frame(amean1=c(mean(b$value1[b$variable=="mixpropA"&b$value1>0]),mean(b$value1[b$variable=="mixpropB"&b$value1>0]),mean(b$value1[b$variable=="mixpropC"&b$value1>0])),
# amean2=c(mean(b$value2[b$variable=="mixpropA"&b$value2>0]),mean(b$value2[b$variable=="mixpropB"&b$value2>0]),mean(b$value2[b$variable=="mixpropC"&b$value2>0])),
# sd1=c(sd(b$value1[b$variable=="mixpropA"&b$value1>0]),sd(b$value1[b$variable=="mixpropB"&b$value1>0]),sd(b$value1[b$variable=="mixpropC"&b$value1>0])),
# sd2=c(sd(b$value2[b$variable=="mixpropA"&b$value2>0]),sd(b$value2[b$variable=="mixpropB"&b$value2>0]),sd(b$value2[b$variable=="mixpropC"&b$value2>0])))
sumdf2<-summarize(group_by(b,Component),amean1=mean(value1),amean2=mean(value2),sd1=sd(value1),sd2=sd(value2))
}
#sumdf<-data.frame(amean1=c(mean(a$value1[a$variable=="mixpropA"&a$value1>0]),mean(a$value1[a$variable=="mixpropB"&a$value1>0]),mean(a$value1[a$variable=="mixpropC"&a$value1>0])),
# amean2=c(mean(a$value2[a$variable=="mixpropA"&a$value2>0]),mean(a$value2[a$variable=="mixpropB"&a$value2>0]),mean(a$value2[a$variable=="mixpropC"&a$value2>0])),
# sd1=c(sd(a$value1[a$variable=="mixpropA"&a$value1>0]),sd(a$value1[a$variable=="mixpropB"&a$value1>0]),sd(a$value1[a$variable=="mixpropC"&a$value1>0])),
# sd2=c(sd(a$value2[a$variable=="mixpropA"&a$value2>0]),sd(a$value2[a$variable=="mixpropB"&a$value2>0]),sd(a$value2[a$variable=="mixpropC"&a$value2>0])))
sumdf<-summarize(group_by(a,Component),amean1=mean(value1),amean2=mean(value2),sd1=sd(value1),sd2=sd(value2))
}
#do the plotting
if(type=="SEQC"){
a<-GeneralLMest(dat,spikeID="ERCC_",components=c("A1","A2","A3","A4","B1","B2","B3","B4"),mixes=c("C1","C2","C3","C4","D1","D2","D3","D4"))
}
if(!missing(df2)&type=="SEQC"){
g<-ggplot(subset(a,sample=="C"))
g+geom_point(aes(x=Apct,y=a$Apct[a$sample=="D"],color=as.factor(LID)),alpha=0.7,size=6)+
scale_y_continuous(limits=c(0,0.5),expand=c(0,0))+scale_x_continuous(limits=c(0.5,1),expand=c(0,0))+
facet_wrap(~ LID)+ylab("Amount of SEQC-A in SEQC-C")+xlab("Amount of SEQC-A in SEQC-D")+
geom_point(aes(x=0.75,y=0.25),col="grey70")+theme(legend.position="none")+geom_path(data=circleFun(center=c(0.75,0.25),diameter=0.1,npoints=25),aes(x,y),col="grey")+geom_path(data=circleFun(center=c(0.75,0.25),diameter=0.05,npoints=25),aes(x,y),col="grey")+geom_path(data=circleFun(center=c(0.75,0.25),diameter=0.15,npoints=25),aes(x,y),col="grey")+geom_path(data=circleFun(center=c(0.75,0.25),diameter=0.2,npoints=25),aes(x,y),col="grey")+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank())+theme(panel.margin=unit(1,"cm"))+
theme(legend.text=element_text(size=rel(1.4)))+theme(axis.title=element_text(size=rel(1.6)))+theme(axis.text=element_text(size=rel(1)))+
theme(strip.background = element_rect(fill = 'white'))+geom_pointrange(data=sumdf,aes(x=amean1,y=amean2,ymax=amean2+sd2,ymin=amean2-sd2))+theme(aspect.ratio=1)+
geom_errorbarh(data=sumdf,aes(x=amean1,y=amean2,xmax=amean1+sd1,xmin=amean1-sd1))+geom_point(data=subset(b,sample=="C"),aes(x=Apct,y=b$Apct[b$sample=="D"],color="red"))
}
if(type=="SEQC"){
g<-ggplot(subset(a,sample=="C"))
return(g+geom_point(aes(x=Apct,y=a$Apct[a$sample=="D"],color=as.factor(LID)),alpha=0.7,size=6)+
scale_y_continuous(limits=c(0,0.5),expand=c(0,0))+scale_x_continuous(limits=c(0.5,1),expand=c(0,0))+
facet_wrap(~ LID)+ylab("Amount of SEQC-A in SEQC-C")+xlab("Amount of SEQC-A in SEQC-D")+
geom_point(aes(x=0.75,y=0.25),col="grey70")+theme(legend.position="none")+geom_path(data=circleFun(center=c(0.75,0.25),diameter=0.1,npoints=25),aes(x,y),col="grey")+
geom_path(data=circleFun(center=c(0.75,0.25),diameter=0.05,npoints=25),aes(x,y),col="grey")+geom_path(data=circleFun(center=c(0.75,0.25),diameter=0.15,npoints=25),aes(x,y),col="grey")+
geom_path(data=circleFun(center=c(0.75,0.25),diameter=0.2,npoints=25),aes(x,y),col="grey")+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank())+theme(panel.margin=unit(1,"cm"))+
theme(legend.text=element_text(size=rel(1.4)))+theme(axis.title=element_text(size=rel(1.6)))+theme(axis.text=element_text(size=rel(1)))+
theme(strip.background = element_rect(fill = 'white'))+geom_pointrange(data=sumdf,aes(x=amean1,y=amean2,ymax=amean2+sd2,ymin=amean2-sd2))+
theme(aspect.ratio=1)+
geom_errorbarh(data=sumdf,aes(x=amean1,y=amean2,xmax=amean1+sd1,xmin=amean1-sd1)))
}
if(!missing(df2)&type=="BLM"){
a$mcor<-1
b$mcor<-0
pathdf<-NULL;pathdf2<-NULL;pathdf3<-NULL
for(I in 1:numrings){pathdf<-rbind(pathdf,circleFun(center=c(0.25,0.25),diameter=0.05*I,npoints=25))
pathdf2<-rbind(pathdf2,circleFun(center=c(0.25,0.5),diameter=0.05*I,npoints=25))
pathdf3<-rbind(pathdf3,circleFun(center=c(0.5,0.25),diameter=0.05*I,npoints=25))}
mergedm<-rbind(a,b)
merged<-rbind(sumdf,sumdf2)
merged$mcor<-c(1,1,1,0,0,0)
g<-ggplot(mergedm)
return(g+geom_path(data=pathdf,aes(x,y),col="grey")+geom_path(data=pathdf2,aes(x,y),col="grey")+geom_path(data=pathdf3,aes(x,y),col="grey")+
geom_point(aes(x=value1,y=value2,col=Component,alpha=as.factor(mcor)),size=5)+
xlab("Amount of Tissue in BLM-1")+ylab("Amount of Tissue in BLM-2")+geom_point(aes(x=c(0.25,0.25,.5),y=c(0.25,0.5,0.25)),col="grey70",size=3)+
theme(legend.position=c(0.6,0.7))+scale_color_manual(name="Tissue",breaks=c("mixpropA","mixpropB","mixpropC","mixpropA","mixpropB","mixpropC"),
labels=c("Brain","Liver","Muscle","Brain","Liver","Muscle"),values=rep(c("#CC6666","#99CC66","#6699CC"),2))+coord_cartesian(ylim=c(0,1),xlim=c(0,1))+
theme(axis.text=element_text(size=rel(1.3)))+theme(axis.title=element_text(size=rel(1.5)))+theme(legend.text=element_text(size=rel(1.5)))+theme(legend.title=element_text(size=rel(1.5)))+
geom_pointrange(data=merged,aes(x=amean1,y=amean2,ymax=amean2+sd2,ymin=amean2-sd2,alpha=as.factor(mcor)),size=1.15)+
geom_errorbarh(data=merged,aes(x=amean1,y=amean2,xmax=amean1+sd1,xmin=amean1-sd1,height=0,alpha=as.factor(mcor)),size=1.3)+scale_alpha_manual(name=Discriminator,breaks=c(1,0),labels=c("True","False"),values=c(0.3,1))+
theme(aspect.ratio=1)+theme(axis.ticks.margin=unit(x = 0.25,units = "cm"))+theme(axis.text=element_text(size=16)))
}
if(missing(df2)&type=="BLM"){
mergedm<-a
merged<-sumdf
g<-ggplot(mergedm)
#do fun things to add a table: this needs to go into MTP...
# dt<-summarize(group_by(mergedm,Component),Observed1=round(mean(value1),digits=3),SD1=round(sd(value1),digits=3),Observed2=round(mean(value2),digits=3),SD2=round(sd(value2),digits=3))
dt<-summarize(group_by(mergedm,Component),Observed1=round(mean(value1),digits=3),Observed2=round(mean(value2),digits=3))
levels(dt$Component)<-c("Brain","Liver","Muscle")
dt$Designed1=c(.25,.25,.5)
dt$Designed2=c(.25,.5,.25)
#dt<-dt[,c(1,2,6,3,4,7,5)]
dt<-dt[,c(1,2,4,3,5)]
#####The actual output i want for the DT table:
dt<-as.data.frame(t(dt))
colnames(dt)<-c("Brain","Liver","Muscle")
dt<-dt[2:length(dt[,1]),]
#####/stuff added 4/15####
# dt2<-summarize(dt,DistanceFromTruth=round(sqrt(sum(abs(Mix1Mean-Mix1))^2+sum(abs(Mix2Mean-Mix2))^2),digits=3),Xdistance=round(sum(abs(Mix1Mean-Mix1)),digits=3),Ydistance=round(sum(abs(Mix2Mean-Mix2)),digits=3))
require(gridExtra)
pathdf<-NULL;pathdf2<-NULL;pathdf3<-NULL
#pathdf<-"geom_path(data=circleFun(center=c(0.25,0.25),diameter=0.05,npoints=25),aes(x,y),col=\"grey\")"
#pathdf2<-"geom_path(data=circleFun(center=c(0.5,0.25),diameter=0.05,npoints=25),aes(x,y),col=\"grey\")"
#pathdf3<-"geom_path(data=circleFun(center=c(0.25,0.5),diameter=0.05,npoints=25),aes(x,y),col=\"grey\")"
for(I in 2:numrings){
#instead of actually making dfs, make a text block
# pathdf<-paste0(pathdf,"+geom_path(data=circleFun(center=c(0.25,0.25),diameter=",0.05*I,",npoints=25),aes(x,y),col=\"grey\")") # I can't make this work.
# pathdf2<-paste0(pathdf2,"+geom_path(data=circleFun(center=c(0.25,0.5),diameter=",0.05*I,",npoints=25),aes(x,y),col=\"grey\")")
# pathdf3<-paste0(pathdf3,"+geom_path(data=circleFun(center=c(0.5,0.25),diameter=",0.05*I,",npoints=25),aes(x,y),col=\"grey\")")
pathdf<-rbind(pathdf,circleFun(center=c(0.25,0.25),diameter=0.05*(I-1),npoints=25))
pathdf2<-rbind(pathdf2,circleFun(center=c(0.25,0.5),diameter=0.05*(I-1),npoints=25))
pathdf3<-rbind(pathdf3,circleFun(center=c(0.5,0.25),diameter=0.05*(I-1),npoints=25))
}
if(dfr==TRUE){return(dt)}
{#redefine tablegrob
drawDetails.table <- function (x, recording = TRUE)
{
lg <- if(!is.null(x$lg)) {
x$lg
} else {
with(x, gridExtra:::makeTableGrobs(as.character(as.matrix(d)),
rows, cols, NROW(d), NCOL(d), parse, row.just = row.just,
col.just = col.just, core.just = core.just, equal.width = equal.width,
equal.height = equal.height, gpar.coretext = gpar.coretext,
gpar.coltext = gpar.coltext, gpar.rowtext = gpar.rowtext,
h.odd.alpha = h.odd.alpha, h.even.alpha = h.even.alpha,
v.odd.alpha = v.odd.alpha, v.even.alpha = v.even.alpha,
gpar.corefill = gpar.corefill, gpar.rowfill = gpar.rowfill,
gpar.colfill = gpar.colfill))
}
widthsv <- convertUnit(lg$widths + x$padding.h, "mm", valueOnly = TRUE)
heightsv <- convertUnit(lg$heights + x$padding.v, "mm", valueOnly = TRUE)
widthsv[1] <- widthsv[1] * as.numeric(x$show.rownames)
widths <- unit(widthsv, "mm")
heightsv[1] <- heightsv[1] * as.numeric(x$show.colnames)
heights <- unit(heightsv, "mm")
cells = viewport(name = "table.cells", layout = grid.layout(lg$nrow +
1, lg$ncol + 1, widths = widths, heights = heights))
pushViewport(cells)
tg <- gridExtra:::arrangeTableGrobs(lg$lgt, lg$lgf, lg$nrow, lg$ncol,
lg$widths, lg$heights, show.colnames = x$show.colnames,
show.rownames = x$show.rownames, padding.h = x$padding.h,
padding.v = x$padding.v, separator = x$separator, show.box = x$show.box,
show.vlines = x$show.vlines, show.hlines = x$show.hlines,