devotional-peacekeepers / dna Goto Github PK
View Code? Open in Web Editor NEWI need someone to tell me if this is making things better or worse
Home Page: htthttps://github.com/Devotional-Peacekeepers/DNA/tree/main
I need someone to tell me if this is making things better or worse
Home Page: htthttps://github.com/Devotional-Peacekeepers/DNA/tree/main
#Toward this end, the function blc was replaced by blc2.
require(RPMM);
betaEst2=function (y, w, weights)
{
yobs = !is.na(y)
if (sum(yobs) <= 1)
return(c(1, 1))
y = y[yobs]
w = w[yobs]
weights = weights[yobs]
N = sum(weights * w)
p = sum(weights * w * y)/N
v = sum(weights * w * y * y)/N - p * p
logab = log(c(p, 1 - p)) + log(pmax(1e-06, p * (1 - p)/v -
1))
if (sum(yobs) == 2)
return(exp(logab))
opt = try(optim(logab, betaObjf, ydata = y, wdata = w, weights = weights,
method = "Nelder-Mead",control=list(maxit=50) ), silent = TRUE)
if (inherits(opt, "try-error"))
return(c(1, 1))
exp(opt$par)
} # end of function betaEst
blc2=function (Y, w, maxiter = 25, tol = 1e-06, weights = NULL, verbose = TRUE)
{
Ymn = min(Y[Y > 0], na.rm = TRUE)
Ymx = max(Y[Y < 1], na.rm = TRUE)
Y = pmax(Y, Ymn/2)
Y = pmin(Y, 1 - (1 - Ymx)/2)
Yobs = !is.na(Y)
J = dim(Y)[2]
K = dim(w)[2]
n = dim(w)[1]
if (n != dim(Y)[1])
stop("Dimensions of w and Y do not agree")
if (is.null(weights))
weights = rep(1, n)
mu = a = b = matrix(Inf, K, J)
crit = Inf
for (i in 1:maxiter) {
warn0 = options()$warn
options(warn = -1)
eta = apply(weights * w, 2, sum)/sum(weights)
mu0 = mu
for (k in 1:K) {
for (j in 1:J) {
ab = betaEst2(Y[, j], w[, k], weights)
a[k, j] = ab[1]
b[k, j] = ab[2]
mu[k, j] = ab[1]/sum(ab)
}
}
ww = array(0, dim = c(n, J, K))
for (k in 1:K) {
for (j in 1:J) {
ww[Yobs[, j], j, k] = dbeta(Y[Yobs[, j], j],
a[k, j], b[k, j], log = TRUE)
}
}
options(warn = warn0)
w = apply(ww, c(1, 3), sum, na.rm = TRUE)
wmax = apply(w, 1, max)
for (k in 1:K) w[, k] = w[, k] - wmax
w = t(eta * t(exp(w)))
like = apply(w, 1, sum)
w = (1/like) * w
llike = weights * (log(like) + wmax)
crit = max(abs(mu - mu0))
if (verbose)
print(crit)
if (crit < tol)
break
}
return(list(a = a, b = b, eta = eta, mu = mu, w = w, llike = sum(llike)))
}
#datMethCalibrated=BMIQcalibration(datM=datMeth,goldstandard.beta=gold.mean)
BMIQcalibration=function(datM,goldstandard.beta,nL=3,doH=TRUE,nfit=20000,th1.v=c(0.2,0.75),th2.v=NULL,niter=5,tol=0.001,plots=FALSE,calibrateUnitInterval=TRUE){
if (length(goldstandard.beta) !=dim(datM)[[2]] ) {stop("Error in function arguments length(goldstandard.beta) !=dim(datM)[[2]]. Consider transposing datM.")}
if (plots ) {par(mfrow=c(2,2))}
beta1.v = goldstandard.beta
if (calibrateUnitInterval ) {datM=CalibrateUnitInterval(datM)}
w0.m = matrix(0,nrow=length(beta1.v),ncol=nL);
w0.m[which(beta1.v <= th1.v[1]),1] = 1;
w0.m[intersect(which(beta1.v > th1.v[1]),which(beta1.v <= th1.v[2])),2] = 1;
w0.m[which(beta1.v > th1.v[2]),3] = 1;
print("Fitting EM beta mixture to goldstandard probes");
set.seed(1)
rand.idx = sample(1:length(beta1.v),min(c(nfit, length(beta1.v)) ) ,replace=FALSE)
em1.o = blc(matrix(beta1.v[rand.idx],ncol=1),w=w0.m[rand.idx,],maxiter=niter,tol=tol);
subsetclass1.v = apply(em1.o$w,1,which.max);
subsetth1.v = c(mean(max(beta1.v[rand.idx[subsetclass1.v==1]]),min(beta1.v[rand.idx[subsetclass1.v==2]])),mean(max(beta1.v[rand.idx[subsetclass1.v==2]]),min(beta1.v[rand.idx[subsetclass1.v==3]])));
class1.v = rep(2,length(beta1.v));
class1.v[which(beta1.v < subsetth1.v[1])] = 1;
class1.v[which(beta1.v > subsetth1.v[2])] = 3;
nth1.v = subsetth1.v;
print("Done");
if(plots){
print("Check");
tmpL.v = as.vector(rmultinom(1:nL,length(beta1.v),prob=em1.o$eta));
tmpB.v = vector();
for(l in 1:nL){
tmpB.v = c(tmpB.v,rbeta(tmpL.v[l],em1.o$a[l,1],em1.o$b[l,1]));
}
plot(density(beta1.v),main= paste("Type1fit-", sep=""));
d.o = density(tmpB.v);
points(d.o$x,d.o$y,col="green",type="l")
legend(x=0.5,y=3,legend=c("obs","fit"),fill=c("black","green"),bty="n");
}
if ( sum(class1.v==1)==1 ){ mod1U= beta1.v[class1.v==1]}
if ( sum(class1.v==3)==1 ){ mod1M= beta1.v[class1.v==3]}
if ( sum(class1.v==1) >1){
d1U.o = density(beta1.v[class1.v==1])
mod1U = d1U.o$x[which.max(d1U.o$y)]
}
if ( sum(class1.v==3)>1 ){
d1M.o = density(beta1.v[class1.v==3])
mod1M = d1M.o$x[which.max(d1M.o$y)]
}
for (ii in 1:dim(datM)[[1]] ){
printFlush(paste("ii=",ii))
sampleID=ii
beta2.v = as.numeric(datM[ii,])
d2U.o = density(beta2.v[which(beta2.v<0.4)]);
d2M.o = density(beta2.v[which(beta2.v>0.6)]);
mod2U = d2U.o$x[which.max(d2U.o$y)]
mod2M = d2M.o$x[which.max(d2M.o$y)]
th2.v = vector();
th2.v[1] = nth1.v[1] + (mod2U-mod1U);
th2.v[2] = nth1.v[2] + (mod2M-mod1M);
w0.m = matrix(0,nrow=length(beta2.v),ncol=nL);
w0.m[which(beta2.v <= th2.v[1]),1] = 1;
w0.m[intersect(which(beta2.v > th2.v[1]),which(beta2.v <= th2.v[2])),2] = 1;
w0.m[which(beta2.v > th2.v[2]),3] = 1;
print("Fitting EM beta mixture to input probes");
set.seed(1)
rand.idx = sample(1:length(beta2.v),min(c(nfit, length(beta2.v)),na.rm=TRUE) ,replace=FALSE)
em2.o = blc2(Y=matrix(beta2.v[rand.idx],ncol=1),w=w0.m[rand.idx,],maxiter=niter,tol=tol,verbose=TRUE);
print("Done");
subsetclass2.v = apply(em2.o$w,1,which.max);
if (sum(subsetclass2.v==2)>0 ){
subsetth2.v = c(mean(max(beta2.v[rand.idx[subsetclass2.v==1]]),min(beta2.v[rand.idx[subsetclass2.v==2]])),
mean(max(beta2.v[rand.idx[subsetclass2.v==2]]),min(beta2.v[rand.idx[subsetclass2.v==3]])));
}
if (sum(subsetclass2.v==2)==0 ){
subsetth2.v = c(1/2max(beta2.v[rand.idx[subsetclass2.v==1]])+ 1/2mean(beta2.v[rand.idx[subsetclass2.v==3]]), 1/3max(beta2.v[rand.idx[subsetclass2.v==1]])+ 2/3mean(beta2.v[rand.idx[subsetclass2.v==3]]));
}
class2.v = rep(2,length(beta2.v));
class2.v[which(beta2.v <= subsetth2.v[1])] = 1;
class2.v[which(beta2.v >= subsetth2.v[2])] = 3;
if(plots){
tmpL.v = as.vector(rmultinom(1:nL,length(beta2.v),prob=em2.o$eta));
tmpB.v = vector();
for(lt in 1:nL){
tmpB.v = c(tmpB.v,rbeta(tmpL.v[lt],em2.o$a[lt,1],em2.o$b[lt,1]));
}
plot(density(beta2.v), main= paste("Type2fit-",sampleID,sep="") );
d.o = density(tmpB.v);
points(d.o$x,d.o$y,col="green",type="l")
legend(x=0.5,y=3,legend=c("obs","fit"),fill=c("black","green"),bty="n");
}
classAV1.v = vector();classAV2.v = vector();
for(l in 1:nL){
classAV1.v[l] = em1.o$mu[l,1];
classAV2.v[l] = em2.o$mu[l,1];
}
print("Start normalising input probes");
nbeta2.v = beta2.v;
lt = 1;
selU.idx = which(class2.v==lt);
selUR.idx = selU.idx[which(beta2.v[selU.idx] > classAV2.v[lt])];
selUL.idx = selU.idx[which(beta2.v[selU.idx] < classAV2.v[lt])];
p.v = pbeta(beta2.v[selUR.idx],em2.o$a[lt,1],em2.o$b[lt,1],lower.tail=FALSE);
q.v = qbeta(p.v,em1.o$a[lt,1],em1.o$b[lt,1],lower.tail=FALSE);
nbeta2.v[selUR.idx] = q.v;
p.v = pbeta(beta2.v[selUL.idx],em2.o$a[lt,1],em2.o$b[lt,1],lower.tail=TRUE);
q.v = qbeta(p.v,em1.o$a[lt,1],em1.o$b[lt,1],lower.tail=TRUE);
nbeta2.v[selUL.idx] = q.v;
lt = 3;
selM.idx = which(class2.v==lt);
selMR.idx = selM.idx[which(beta2.v[selM.idx] > classAV2.v[lt])];
selML.idx = selM.idx[which(beta2.v[selM.idx] < classAV2.v[lt])];
p.v = pbeta(beta2.v[selMR.idx],em2.o$a[lt,1],em2.o$b[lt,1],lower.tail=FALSE);
q.v = qbeta(p.v,em1.o$a[lt,1],em1.o$b[lt,1],lower.tail=FALSE);
nbeta2.v[selMR.idx] = q.v;
if(doH){ ### if TRUE also correct type2 hemimethylated probes
lt = 2;
selH.idx = c(which(class2.v==lt),selML.idx);
minH = min(beta2.v[selH.idx],na.rm=TRUE)
maxH = max(beta2.v[selH.idx],na.rm=TRUE)
deltaH = maxH - minH;
deltaUH = -max(beta2.v[selU.idx],na.rm=TRUE) + min(beta2.v[selH.idx],na.rm=TRUE)
deltaHM = -max(beta2.v[selH.idx],na.rm=TRUE) + min(beta2.v[selMR.idx],na.rm=TRUE)
nmaxH = min(nbeta2.v[selMR.idx],na.rm=TRUE) - deltaHM;
nminH = max(nbeta2.v[selU.idx],na.rm=TRUE) + deltaUH;
ndeltaH = nmaxH - nminH;
hf = ndeltaH/deltaH ;
nbeta2.v[selH.idx] = nminH + hf*(beta2.v[selH.idx]-minH);
}
if(plots){
print("Generating final plot");
d1.o = density(beta1.v);
d2.o = density(beta2.v);
d2n.o = density(nbeta2.v);
ymax = max(d2.o$y,d1.o$y,d2n.o$y);
plot(density(beta2.v),type="l",ylim=c(0,ymax),xlim=c(0,1), main=paste("CheckBMIQ-",sampleID,sep="") );
points(d1.o$x,d1.o$y,col="red",type="l");
points(d2n.o$x,d2n.o$y,col="blue",type="l");
legend(x=0.5,y=ymax,legend=c("type1","type2","type2-BMIQ"),bty="n",fill=c("red","black","blue"));
}
datM[ii,]= nbeta2.v ;
} # end of for (ii=1 loop
datM
} # end of function BMIQcalibration
BMIQ = function(beta.v,design.v,nL=3,doH=TRUE,nfit=50000,th1.v=c(0.2,0.75),th2.v=NULL,niter=5,tol=0.001,plots=TRUE,sampleID=1,calibrateUnitInterval=TRUE){
if (calibrateUnitInterval) {
rangeBySample=range(beta.v,na.rm=TRUE)
minBySample=rangeBySample[1]
maxBySample=rangeBySample[2]
if ( (minBySample<0 | maxBySample>1) & !is.na(minBySample) & !is.na(maxBySample) ) {
y1=c(0.001,.999)
x1=c(minBySample,maxBySample)
lm1=lm( y1 ~ x1 )
intercept1=coef(lm1)[[1]]
slope1=coef(lm1)[[2]]
beta.v=intercept1+slope1*beta.v
} # end of if
} # end of if (calibrateUnitInterval
type1.idx = which(design.v==1);
type2.idx = which(design.v==2);
beta1.v = beta.v[type1.idx];
beta2.v = beta.v[type2.idx];
w0.m = matrix(0,nrow=length(beta1.v),ncol=nL);
w0.m[which(beta1.v <= th1.v[1]),1] = 1;
w0.m[intersect(which(beta1.v > th1.v[1]),which(beta1.v <= th1.v[2])),2] = 1;
w0.m[which(beta1.v > th1.v[2]),3] = 1;
print("Fitting EM beta mixture to goldstandard probes");
set.seed(1)
rand.idx = sample(1:length(beta1.v),min(c(nfit, length(beta1.v)) ) ,replace=FALSE)
em1.o = blc2(Y=matrix(beta1.v[rand.idx],ncol=1),w=w0.m[rand.idx,],maxiter=niter,tol=tol);
subsetclass1.v = apply(em1.o$w,1,which.max);
subsetth1.v = c(mean(max(beta1.v[rand.idx[subsetclass1.v==1]]),min(beta1.v[rand.idx[subsetclass1.v==2]])),mean(max(beta1.v[rand.idx[subsetclass1.v==2]]),min(beta1.v[rand.idx[subsetclass1.v==3]],na.rm=TRUE)));
class1.v = rep(2,length(beta1.v));
class1.v[which(beta1.v < subsetth1.v[1])] = 1;
class1.v[which(beta1.v > subsetth1.v[2])] = 3;
nth1.v = subsetth1.v;
print("Done");
if(plots){
print("Check");
tmpL.v = as.vector(rmultinom(1:nL,length(beta1.v),prob=em1.o$eta));
tmpB.v = vector();
for(l in 1:nL){
tmpB.v = c(tmpB.v,rbeta(tmpL.v[l],em1.o$a[l,1],em1.o$b[l,1]));
}
pdf(paste("Type1fit-",sampleID,".pdf",sep=""),width=6,height=4);
plot(density(beta1.v));
d.o = density(tmpB.v);
points(d.o$x,d.o$y,col="green",type="l")
legend(x=0.5,y=3,legend=c("obs","fit"),fill=c("black","green"),bty="n");
dev.off();
}
if ( sum(class1.v==1)==1 ){ mod1U= beta1.v[class1.v==1]}
if ( sum(class1.v==3)==1 ){ mod1M= beta1.v[class1.v==3]}
if ( sum(class1.v==1) >1){
d1U.o = density(beta1.v[class1.v==1])
mod1U = d1U.o$x[which.max(d1U.o$y)]
}
if ( sum(class1.v==3)>1 ){
d1M.o = density(beta1.v[class1.v==3])
mod1M = d1M.o$x[which.max(d1M.o$y)]
}
d2U.o = density(beta2.v[which(beta2.v<0.4)]);
d2M.o = density(beta2.v[which(beta2.v>0.6)]);
mod2U = d2U.o$x[which.max(d2U.o$y)]
mod2M = d2M.o$x[which.max(d2M.o$y)]
th2.v = vector();
th2.v[1] = nth1.v[1] + (mod2U-mod1U);
th2.v[2] = nth1.v[2] + (mod2M-mod1M);
w0.m = matrix(0,nrow=length(beta2.v),ncol=nL);
w0.m[which(beta2.v <= th2.v[1]),1] = 1;
w0.m[intersect(which(beta2.v > th2.v[1]),which(beta2.v <= th2.v[2])),2] = 1;
w0.m[which(beta2.v > th2.v[2]),3] = 1;
print("Fitting EM beta mixture to input probes");
set.seed(1)
rand.idx = sample(1:length(beta2.v),min(c(nfit, length(beta2.v)),na.rm=TRUE) ,replace=FALSE)
em2.o = blc2(Y=matrix(beta2.v[rand.idx],ncol=1),w=w0.m[rand.idx,],maxiter=niter,tol=tol);
print("Done");
subsetclass2.v = apply(em2.o$w,1,which.max);
if (sum(subsetclass2.v==2)>0 ){
subsetth2.v = c(mean(max(beta2.v[rand.idx[subsetclass2.v==1]]),min(beta2.v[rand.idx[subsetclass2.v==2]])),
mean(max(beta2.v[rand.idx[subsetclass2.v==2]]),min(beta2.v[rand.idx[subsetclass2.v==3]])));
}
if (sum(subsetclass2.v==2)==0 ){
subsetth2.v = c(1/2max(beta2.v[rand.idx[subsetclass2.v==1]])+ 1/2mean(beta2.v[rand.idx[subsetclass2.v==3]]), 1/3max(beta2.v[rand.idx[subsetclass2.v==1]])+ 2/3mean(beta2.v[rand.idx[subsetclass2.v==3]]));
}
class2.v = rep(2,length(beta2.v));
class2.v[which(beta2.v <= subsetth2.v[1])] = 1;
class2.v[which(beta2.v >= subsetth2.v[2])] = 3;
if(plots){
tmpL.v = as.vector(rmultinom(1:nL,length(beta2.v),prob=em2.o$eta));
tmpB.v = vector();
for(lt in 1:nL){
tmpB.v = c(tmpB.v,rbeta(tmpL.v[lt],em2.o$a[lt,1],em2.o$b[lt,1]));
}
pdf(paste("Type2fit-",sampleID,".pdf",sep=""),width=6,height=4);
plot(density(beta2.v));
d.o = density(tmpB.v);
points(d.o$x,d.o$y,col="green",type="l")
legend(x=0.5,y=3,legend=c("obs","fit"),fill=c("black","green"),bty="n");
dev.off();
}
classAV1.v = vector();classAV2.v = vector();
for(l in 1:nL){
classAV1.v[l] = em1.o$mu[l,1];
classAV2.v[l] = em2.o$mu[l,1];
}
print("Start normalising input probes");
nbeta2.v = beta2.v;
lt = 1;
selU.idx = which(class2.v==lt);
selUR.idx = selU.idx[which(beta2.v[selU.idx] > classAV2.v[lt])];
selUL.idx = selU.idx[which(beta2.v[selU.idx] < classAV2.v[lt])];
p.v = pbeta(beta2.v[selUR.idx],em2.o$a[lt,1],em2.o$b[lt,1],lower.tail=FALSE);
q.v = qbeta(p.v,em1.o$a[lt,1],em1.o$b[lt,1],lower.tail=FALSE);
nbeta2.v[selUR.idx] = q.v;
p.v = pbeta(beta2.v[selUL.idx],em2.o$a[lt,1],em2.o$b[lt,1],lower.tail=TRUE);
q.v = qbeta(p.v,em1.o$a[lt,1],em1.o$b[lt,1],lower.tail=TRUE);
nbeta2.v[selUL.idx] = q.v;
lt = 3;
selM.idx = which(class2.v==lt);
selMR.idx = selM.idx[which(beta2.v[selM.idx] > classAV2.v[lt])];
selML.idx = selM.idx[which(beta2.v[selM.idx] < classAV2.v[lt])];
p.v = pbeta(beta2.v[selMR.idx],em2.o$a[lt,1],em2.o$b[lt,1],lower.tail=FALSE);
q.v = qbeta(p.v,em1.o$a[lt,1],em1.o$b[lt,1],lower.tail=FALSE);
nbeta2.v[selMR.idx] = q.v;
if(doH){ ### if TRUE also correct type2 hemimethylated probes
lt = 2;
selH.idx = c(which(class2.v==lt),selML.idx);
minH = min(beta2.v[selH.idx],na.rm=TRUE)
maxH = max(beta2.v[selH.idx],na.rm=TRUE)
deltaH = maxH - minH;
deltaUH = -max(beta2.v[selU.idx],na.rm=TRUE) + min(beta2.v[selH.idx],na.rm=TRUE)
deltaHM = -max(beta2.v[selH.idx],na.rm=TRUE) + min(beta2.v[selMR.idx],na.rm=TRUE)
nmaxH = min(nbeta2.v[selMR.idx],na.rm=TRUE) - deltaHM;
nminH = max(nbeta2.v[selU.idx],na.rm=TRUE) + deltaUH;
ndeltaH = nmaxH - nminH;
hf = ndeltaH/deltaH ;
nbeta2.v[selH.idx] = nminH + hf*(beta2.v[selH.idx]-minH);
}
pnbeta.v = beta.v;
pnbeta.v[type1.idx] = beta1.v;
pnbeta.v[type2.idx] = nbeta2.v;
if(plots){
print("Generating final plot");
d1.o = density(beta1.v);
d2.o = density(beta2.v);
d2n.o = density(nbeta2.v);
ymax = max(d2.o$y,d1.o$y,d2n.o$y);
pdf(paste("CheckBMIQ-",sampleID,".pdf",sep=""),width=6,height=4)
plot(density(beta2.v),type="l",ylim=c(0,ymax),xlim=c(0,1));
points(d1.o$x,d1.o$y,col="red",type="l");
points(d2n.o$x,d2n.o$y,col="blue",type="l");
legend(x=0.5,y=ymax,legend=c("type1","type2","type2-BMIQ"),bty="n",fill=c("red","black","blue"));
dev.off();
}
print(paste("Finished for sample ",sampleID,sep=""));
return(list(nbeta=pnbeta.v,class1=class1.v,class2=class2.v,av1=classAV1.v,av2=classAV2.v,hf=hf,th1=nth1.v,th2=th2.v));
}
CheckBMIQ = function(beta.v,design.v,pnbeta.v){### pnbeta is BMIQ normalised profile
type1.idx = which(design.v==1);
type2.idx = which(design.v==2);
beta1.v = beta.v[type1.idx];
beta2.v = beta.v[type2.idx];
pnbeta2.v = pnbeta.v[type2.idx];
} # end of function CheckBMIQ
CalibrateUnitInterval=function(datM,onlyIfOutside=TRUE){
rangeBySample=data.frame(lapply(data.frame(t(datM)),range,na.rm=TRUE))
minBySample=as.numeric(rangeBySample[1,])
maxBySample=as.numeric(rangeBySample[2,])
if (onlyIfOutside) { indexSamples=which((minBySample<0 | maxBySample>1) & !is.na(minBySample) & !is.na(maxBySample))
}
if (!onlyIfOutside) { indexSamples=1:length(minBySample)}
if ( length(indexSamples)>=1 ){
for ( i in indexSamples) {
y1=c(0.001,0.999)
x1=c(minBySample[i],maxBySample[i])
lm1=lm( y1 ~ x1 )
intercept1=coef(lm1)[[1]]
slope1=coef(lm1)[[2]]
datM[i,]=intercept1+slope1*datM[i,]
} # end of for loop
}
datM
} #end of function for calibrating to [0,1]
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.