Further cleanup of R

public/R/titvFPEst.R

@@ -1,138 +0,0 @@
-titvFPEst <- function(titvExpected, titvObserved) { max(min(1 - (titvObserved - 0.5) / (titvExpected - 0.5), 1), 0.001) }
-
-titvFPEstV <- function(titvExpected, titvs) {
-    sapply(titvs, function(x) titvFPEst(titvExpected, x))
-}
-
-calcHet <- function(nknown, knownTiTv, nnovel, novelTiTv, callable) {
-  TP <- nknown + (1-titvFPEst(knownTiTv, novelTiTv)) * nnovel
-  2 * TP / 3 / callable
-}
-
-marginalTiTv <- function( nx, titvx, ny, titvy ) {
-    tvx = nx / (titvx + 1)
-    tix = nx - tvx
-    tvy = ny / (titvy + 1)
-    tiy = ny - tvy
-    tiz = tix - tiy
-    tvz = tvx - tvy
-    return(tiz / tvz)
-}
-marginaldbSNPRate <- function( nx, dbx, ny, dby ) {
-    knownx = nx * dbx / 100
-    novelx = nx - knownx
-    knowny = ny * dby / 100
-    novely = ny - knowny
-    knownz = knownx - knowny
-    novelz = novelx - novely
-    return(knownz / ( knownz + novelz ) * 100)
-}
-
-numExpectedCalls <- function(L, theta, calledFractionOfRegion, nIndividuals, dbSNPRate) {
-    nCalls <- L * theta * calledFractionOfRegion * sum(1 / seq(1, 2 * nIndividuals))
-    return(list(nCalls = nCalls, nKnown = dbSNPRate * nCalls, nNovel = (1-dbSNPRate) * nCalls))
-}
-
-normalize <- function(x) {
-    x / sum(x)
-}
-
-normcumsum <- function(x) {
-    cumsum(normalize(x))
-}
-
-cumhist <- function(d, ...) {
-    plot(d[order(d)], type="b", col="orange", lwd=2, ...)
-}
-
-revcumsum <- function(x) {
-   return(rev(cumsum(rev(x))))
-}
-
-phred <- function(x) {
-    log10(max(x,10^(-9.9)))*-10
-}
-
-pOfB <- function(b, B, Q) {
-    #print(paste(b, B, Q))
-    p = 1 - 10^(-Q/10)
-    if ( b == B )
-        return(p) 
-    else
-        return(1 - p)
-}
-
-pOfG <- function(bs, qs, G) {
-    a1 = G[1]
-    a2 = G[2]
-    
-    log10p = 0
-    for ( i in 1:length(bs) ) {
-        b = bs[i]
-        q = qs[i]
-        p1 = pOfB(b, a1, q) / 2 + pOfB(b, a2, q) / 2
-        log10p = log10p + log10(p1)
-    }
-    
-    return(log10p)
-}
-
-pOfGs <- function(nAs, nBs, Q) {
-    bs = c(rep("a", nAs), rep("t", nBs))
-    qs = rep(Q, nAs + nBs)
-    G1 = c("a", "a")
-    G2 = c("a", "t")
-    G3 = c("t", "t")
-
-    log10p1 = pOfG(bs, qs, G1)
-    log10p2 = pOfG(bs, qs, G2)
-    log10p3 = pOfG(bs, qs, G3)
-    Qsample = phred(1 - 10^log10p2 / sum(10^(c(log10p1, log10p2, log10p3))))
-    
-    return(list(p1=log10p1, p2=log10p2, p3=log10p3, Qsample=Qsample)) 
-}
-
-QsampleExpected <- function(depth, Q) {
-    weightedAvg = 0
-    for ( d in 1:(depth*3) ) {
-        Qsample = 0
-        pOfD = dpois(d, depth)
-        for ( nBs in 0:d ) {
-            pOfnB = dbinom(nBs, d, 0.5)
-            nAs = d - nBs
-            Qsample = pOfGs(nAs, nBs, Q)$Qsample
-            #Qsample = 1
-            weightedAvg = weightedAvg + Qsample * pOfD * pOfnB
-            print(as.data.frame(list(d=d, nBs = nBs, pOfD=pOfD, pOfnB = pOfnB, Qsample=Qsample, weightedAvg = weightedAvg)))
-        }
-    }
-    
-    return(weightedAvg)
-}
-
-plotQsamples <- function(depths, Qs, Qmax) {
-    cols = rainbow(length(Qs))
-    plot(depths, rep(Qmax, length(depths)), type="n", ylim=c(0,Qmax), xlab="Average sequencing coverage", ylab="Qsample", main = "Expected Qsample values, including depth and allele sampling")
-    
-    for ( i in 1:length(Qs) ) {
-        Q = Qs[i]
-        y = as.numeric(lapply(depths, function(x) QsampleExpected(x, Q)))
-        points(depths, y, col=cols[i], type="b")
-    }
-
-    legend("topleft", paste("Q", Qs), fill=cols)
-}
-
-pCallHetGivenDepth <- function(depth, nallelesToCall) {
-  depths = 0:(2*depth)   
-  pNoAllelesToCall = apply(as.matrix(depths),1,function(d) sum(dbinom(0:nallelesToCall,d,0.5)))
-  dpois(depths,depth)*(1-pNoAllelesToCall)
-}
-
-pCallHets <- function(depth, nallelesToCall) {
-  sum(pCallHetGivenDepth(depth,nallelesToCall))
-}
-
-pCallHetMultiSample <- function(depth, nallelesToCall, nsamples) {
-  1-(1-pCallHets(depth,nallelesToCall))^nsamples
-}