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ProgR_0.1_Functions.R
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library(IRanges)
library(Rsolnp)
library(MASS)
require('RColorBrewer')
##################################################################
##################################################################
##
## Load or read sample data
##
##################################################################
##################################################################
loadSample = function(i = NULL, info = NULL){
d = file.path(getwd(), 'HET', info$sample.name[i])
load(file = file.path(d, paste(info$sample.name[i], '_obj.RData', sep = '')))
obj
}
readCBS = function(obj, printfolder = NULL){
library(PSCBS)
library(aroma.affymetrix)
d = file.path(getwd(), 'AROMA', 'reports', 'SNP_segmentation', 'CBSfitsWithNormals')
da = loadObject(paste(d, '/fit', obj$i, '.RData', sep = ''))
mat = da$output
names(mat)[names(mat) == 'c1Mean'] = 'a1'
names(mat)[names(mat) == 'c2Mean'] = 'a2'
names(mat)[names(mat) == 'tcnStart'] = 'Start'
names(mat)[names(mat) == 'tcnEnd'] = 'End'
names(mat)[names(mat) == 'chromosome'] = 'Chromosome'
mat$length = mat$End - mat$Start
mat = subset(mat, !is.na(length) & Chromosome<23 & !is.na(a2) & !is.na(a1))
mat$W = mat$length/sum(mat$length)
mat$tot = pmax(mat$a1,0) + mat$a2
mat$imba = 2*(mat$a2/(mat$tot)-.5)
obj$matCBS = mat
#Figure
x = mat$tot
y = mat$imba
xlim = c(quantile(x, probs = c(0,.975), na.rm = T)*c(1, 1.2))
ylim = c(0,1)
plot(x, y, pch = 16, col = col.transp('blue', .3),
xlim = xlim, ylim = ylim, xlab = 'Total CN intensity',
ylab = 'Allelic Imbalance 2*|BAF - 0.5|', main = 'TAPS')
abline(v = 1, lty = 'dotted')
points(x[mat$a1<=0], y[mat$a1<=0], col = col.transp('red', .5))
tx = ifelse((obj$info$ind[i] + 100) %in% obj$info$ind & obj$i !=54, 'Sample has germline SNP array',
'Sample does not have germline SNP array')
mtext(tx)
if (!is.null(printfolder)) {
dir.create(printfolder, recursive = T, showWarnings = FALSE)
dev.print(png, file=file.path(printfolder, paste(obj$info$sample.name[i],'_1.2_Standardized_TAPS.png',
sep = '')), width=640, height=640)
}
obj
}
readHAPSEG = function(obj, printfolder = NULL){
info = obj$info
i = obj$i
source(file.path(getwd(), 'programs', 'make_HAPSEG_seg_obj.R'))
d = file.path(getwd(), 'ABSOLUTE', 'WithNormals','HAPSEG')
d = paste(d, obj$h, sep = '')
d = paste(d,'/', info$sample.name[i], sep = '')
seg.dat.fn = file.path(d, paste(info$sample.name[i], '_', info$arrname[i], '_segdat.RData', sep = ''))
seg.dat = HAPSEGMakeSegObj(seg.dat.fn, gender = 'Female', filter_segs=FALSE, min_probes=10, max_sd=100,
verbose=TRUE)
as.seg.dat = seg.dat$as.seg.dat
nr = as.integer(nrow(as.seg.dat))
mat = as.seg.dat[1:(nr/2),]
names(mat)[6] = 'a1'
mat$a2 = as.seg.dat$copy_num[(nr/2+1):(nr)]
mat$W = mat$W*2
mat$tot = mat$a1 + mat$a2
mat$imba = 2*(mat$a2/(mat$tot)-.5)
obj$mat = mat
obj$seg.dat = seg.dat
x = mat$tot
y = mat$imba
xlim = c(quantile(x, probs = c(.025,.975), na.rm = T)*c(.8, 1.2))
ylim = c(0,1)
plot(x, y, pch = 16, col = col.transp('blue', .3),
xlim = xlim, ylim = ylim, xlab = 'Total CN intensity',
ylab = 'Allelic Imbalance 2*|BAF - 0.5|',
main = paste('HAPSEG',obj$h, sep = ''))
points(x[mat$a1<=0], y[mat$a1<=0], col = col.transp('red', .5))
title(main = info$sample.name[i], outer = T, line = 0)
if (!is.null(printfolder)) {
dir.create(printfolder, recursive = T, showWarnings = FALSE)
dev.print(png,
file=file.path(printfolder, paste(info$sample.name[i],'_1.1_HAPSEG_TAPS.png',
sep = '')), width=640, height=640)
}
obj
}
##################################################################
##################################################################
##
## General
##
##################################################################
##################################################################
col.transp <- function(col1, alpha=0.5){
out = NULL
for (j in 1:length(col1)){
tmp=as.list(t(col2rgb(col1[j])))
tmp$alpha=alpha*255
tmp$maxColorValue=255
out = c(out, do.call('rgb',tmp))
}
out
}
colors.by = function(x, pal = brewer.pal(9,'YlOrRd'), span = range(na.omit(x)),
alpha = .5){
cols = colorRampPalette(pal)(100)
colbreaks = seq(min(span), max(span), length = length(cols))
delta = colbreaks[2] - colbreaks[1]
colind = pmax((x-colbreaks[1]) %/% delta + 1, 1)
colind[colind>length(cols)] = length(cols)
mycols = col.transp(cols[colind], alpha = alpha)
list(mycols=mycols, cols = cols)
}
dist = function(x, y){
if (is.null(dim(x))) x = matrix(x, ncol = 2)
if (is.null(dim(y))) y = matrix(y, ncol = 2)
sqrt((x[,1] - y[,1])**2 + (x[,2] - y[,2])**2)
}
##################################################################
##################################################################
##################################################################
##################################################################
##
## Rotation of CNs
##
##################################################################
##################################################################
startRotation = function(obj){
i = obj$i
d = file.path(obj$d,
paste(obj$info$sample.name[i], '_2_Start_values', sep = ''))
mystart = start.alphamax.f(obj$mat, printfile = d, colbychrom = T)
alpha = mystart$alphamax
F = mystart$F
plot.transformed(obj$mat, alpha,
F, xlim = NULL, ylim = NULL)
d = file.path(obj$d,
paste(info$sample.name[i], '_2_Start_values_4.png', sep = ''))
dev.print(png, file = d, width = 640, height = 500)
Fm1 = solve(F)
a1p = obj$mat$a1*Fm1[1,1] + obj$mat$a2 * Fm1[1,2]
a2p = obj$mat$a1*Fm1[2,1] + obj$mat$a2 * Fm1[2,2]
xlim = c(0, quantile(a2p, probs = .95, na.rm = T)*1.2)
ylim = c(0, quantile(a1p, probs = .95, na.rm = T)*1.2)
plot.transformed(obj$mat, alpha,
F, xlim = xlim, ylim = ylim)
d = file.path(obj$d,
paste(info$sample.name[i], '_2_Start_values_5.png', sep = ''))
dev.print(png, file = d, width = 640, height = 500)
mystart
}
start.alphamax.f = function(mat, maxCN = 24, maxlines = 30,
colbychrom = FALSE, printfile = FALSE){
# par(mfrow = c(1,2))
#x = mat$a1 + mat$a2
#y = 2*(mat$a2/(x)-.5)
#xlim = c(quantile(x, probs = c(.025,.975), na.rm = T))
#ylim = c(quantile(y, probs = c(0, 1), na.rm = T))
#plot(x, y, pch = 16, col = col.transp('grey', .3),
# xlim = xlim, ylim = ylim, xlab = 'Total CN intensity',
# ylab = 'Allelic Imbalance 2*|BAF - 0.5|', main = 'TAPS')
xlim = c(0, quantile(mat$a2, probs = c(.9)))
ylim = c(0, quantile(mat$a1, probs = c(.975)))
cols = col.transp('grey', .3)
if (colbychrom) cols = col.transp(as.numeric(mat$Chromosome), .3)
plot(mat$a2, mat$a1, pch = 16, col = cols,
xlim = xlim, ylim = ylim, xlab = 'Major intensity ratio a2',
ylab = 'Minor intensity ratio a1',
main = 'Minor and major CN intensity')
abline(0,1, col = 'red')
if (printfile != FALSE) dev.print(png, file = paste(printfile, '_1.png',
sep = ''), width = 640, height = 500)
print('Mark two subsequent CN clusters along a "vertical" line:')
posy = locator(2)
print('Mark two distant points in one "vertical" line:')
posl = locator(2)
print('Mark two subsequent CN clusters along a horizontal line:')
posx = locator(2)
print('Mark two distant points in one "horizontal" line:')
posk = locator(2)
print('Mark the allelic balance cluster with maximal CN:')
posb = locator(1)
if (!is.null(posy)) dy = abs(diff(posy$y))
if (!is.null(posx)) dx = abs(diff(posx$x))
l = lm(x~y, data = posl)$coef[2]
k = lm(y~x, data = posk)$coef[2]
lines(posy$x, posy$y)
lines(posl$x, posl$y, lty = 'dotted')
lines(posx$x, posx$y)
lines(posk$x, posk$y, lty = 'dotted')
points(posb$x, posb$y, pch = 4)
dx = (dx+dy)/2
dy = dx
f11 = dy#(dx+dy)/2
f22 = dx#(dx+dy)/2
if (printfile != FALSE) dev.print(png, file = paste(printfile, '_2.png',
sep = ''), width = 640, height = 500)
F = matrix(c(f11, l*f11, k*f22, f22), ncol = 2, nrow = 2)
Fm1 = solve(F)
###Boundaries for F values
LBf = c(0.5*f11, -Inf, -Inf, 0.5*f22)
UBf = c(1.5*f11, Inf, Inf, 1.5*f22)
#UBf[2]
ix = which.max(posl$y)
postmp = posl
postmp$x[ix] = postmp$x[ix]+dx
UBf[2] = lm(x~y, data = postmp)$coef[2]*UBf[1]
if (l<0) UBf[2] = min(0, UBf[2])
#UBf[3]
ix = which.max(posk$x)
postmp = posk
postmp$y[ix] = postmp$y[ix]+dy
UBf[3] = lm(y~x, data = postmp)$coef[2]*UBf[4]
if (k<0) UBf[3] = min(0, UBf[3])
#LBf[2]
ix = which.max(posl$y)
postmp = posl
postmp$x[ix] = postmp$x[ix]-dx
LBf[2] = lm(x~y, data = postmp)$coef[2]*LBf[1]
if (l>0) LBf[2] = max(0, LBf[2])
#LBf[3]
ix = which.max(posk$x)
postmp = posk
postmp$y[ix] = postmp$y[ix]-dy
LBf[3] = lm(y~x, data = postmp)$coef[2]*LBf[4]
if (k>0) LBf[3] = max(0, LBf[3])
### Starting value positions of lattice points in E scale
E1 = Fm1[1,1]*posy$y + Fm1[1,2]*posy$x
deltay = max(E1) - min(E1)
b = seq(max(E1), by = -deltay, to = 0)
by = b[length(b)]
ny = length(b)
E1 = seq(by, by = deltay, length.out = maxlines)
E2 = Fm1[2,1]*posx$y + Fm1[2,2]*posx$x
deltax = max(E2) - min(E2)
b = seq(max(E2), by = -deltax, to = 0)
bx = b[length(b)]
nx = length(b)
E2 = seq(bx, by = deltax, length.out = maxlines)
E1 = rep(E1, maxlines)
E2 = rep(E2, each = maxlines)
### Starting value positions of lattice points in observed scale
### Before adjusted to common alpha
e1 = F[1,1] * E1 + F[1,2]*E2
e2 = F[2,1] * E1 + F[2,2]*E2
points(e2, e1)
### Estimate startvalue of maximum alpha which is <1
balance.x = Fm1[2,1]*posb$y + Fm1[2,2]*posb$x
balance.y = Fm1[1,1]*posb$y + Fm1[1,2]*posb$x
dists = dist(x = c(balance.y, balance.x), y = cbind(E1, E2))
closest = which.min(dists)
bbx = seq(E2[closest], by = -deltax, to = 0)
bby = seq(E1[closest], by = -deltay, to = 0)
n = min(length(bbx), length(bby))
nxs = which(bbx>=0)[(length(bbx)-n+1):length(bbx)]
nys = which(bby>=0)[(length(bby)-n+1):length(bby)]
alphax = (1)/(bx+nxs)
alphay = 1/(by+nys)
print(paste('Purity estimates 1:', alphay[1]))
print(paste('Purity estimates 2:', alphax[1]))
alphamax = mean(c(alphax[1], alphay[1]))
### Re-estimate starting value positions of Es and F from alpha
f11 = dy/alphamax#(dx+dy)/2/alphamax#
f22 = dx/alphamax#(dx+dy)/2/alphamax#
F = matrix(c(f11, l*f11, k*f22, f22), ncol = 2, nrow = 2)
Fm1 = solve(F)
A2 = 0:maxCN
A1 = matrix(A2, ncol = maxCN + 1, nrow = maxCN + 1)
A2 = t(A1)
E1 = c(alphamax*A1 + (1-alphamax))
E2 = c(alphamax*A2 + (1-alphamax))
### Starting value positions of lattice points in observed scale
e1 = F[1,1] * E1 + F[1,2]*E2
e2 = F[2,1] * E1 + F[2,2]*E2
# points(e2, e1, col = 'blue')
### Plot starting values on transformed scale
par(mfrow = c(1,1))
x = cbind(mat$a1, mat$a2)
a1 = Fm1[1,1] * x[,1] + Fm1[1,2]*x[,2]
a2 = Fm1[2,1] * x[,1] + Fm1[2,2]*x[,2]
xlim = c(0, quantile(a2, probs = c(1)))
ylim = c(0, quantile(a1, probs = c(1)))
plot(a2, a1, pch = 16, col = col.transp('grey', .3),
xlab = 'Rotated a2', ylab = 'Rotated a1',
main = 'Minor and major CN intensity',
xlim = xlim, ylim = ylim)
lines(range(E2), range(E1), col = 'red')
for (j in 1:maxCN){
lines(range(E2), rep(E1[j], 2), lty = 'dotted', col = 'grey20')
lines(rep(unique(E2)[j], 2), range(E1), lty = 'dotted', col = 'grey20')
}
if (printfile != FALSE) dev.print(png, file = paste(printfile, '_3.png',
sep = ''), width = 640, height = 500)
#Adjust F if allelic balance clusters are not on diagonal:
# print('Mark the allelic balance point with maximal CN or Esc:')
# posb = locator(1)
# if (!is.null(posb)){
# Fm1[2,] = Fm1[2,]*posb$y/posb$x
# F = solve(Fm1)
# }
#Make sure to stay within boundaries:
#F = pmin(F, UBf)
#F = pmax(F, LBf)
#Fm1 = solve(F)
list(alphamax = alphamax, F=F, LBf = LBf, UBf = UBf)
}
rho.alphamax.f = function(pars, x, w, maxCN=24, rhofunc = 'tukey',
rhoargs = NULL){
alphamax = pars[1]
F = matrix(pars[-1], ncol = 2, nrow = 2)
#Position of latice points on observed scale
A2 = 0:maxCN
A1 = matrix(A2, ncol = maxCN + 1, nrow = maxCN + 1)
A2 = t(A1)
E1 = c(alphamax*A1 + (1-alphamax))
E2 = c(alphamax*A2 + (1-alphamax))
e1 = F[1,1] * E1 + F[1,2]*E2
e2 = F[2,1] * E1 + F[2,2]*E2
#Assign observations to closest lattice point
alldist = apply(x, 1, dist, y = cbind(e1, e2))
closest = apply(alldist, 2, which.min)
#Calculate residuals
e = cbind(e1, e2)[closest,]
r = dist(x, e)*w
sum(do.call(rhofunc, args = list(x = r, args = rhoargs)))
}
setWeights = function(obj, weight.constant = 500000, rhofunc = 'tukey',
nmad = 3){
obj$w = 1-exp(-obj$mat$length/weight.constant)
obj$rhofunc = rhofunc
obj$nmad = nmad
evaluate.weights(obj$mat, obj$mystart$alphamax, obj$mystart$F,
nmad = nmad, w=obj$w)
title(paste('w = 1-exp(-segment length/', weight.constant, ')',
sep = ''), adj = 1, outer = T, line = -2, font.main = 1)
dev.print(png, file=file.path(obj$d, paste(obj$info$sample.name[i],'_3.1_evaluate_weights.png',
sep = '')), width=900, height=900)
obj$weight.constant = weight.constant
print("Change weight constant until 1'000'000 length segs have almost top weight")
print("Change nmad until Contrib to rho gets constant at good Distance to lattice point")
obj
}
evaluate.weights = function(mat, alpha, F, rhofunc = 'tukey',
nmad = 1, maxCN = 24, w=1){
#Recalculate transformed lattice points from F and alpha
x = cbind(mat$a1, mat$a2)
Fm1 = solve(F)
A2 = 0:maxCN
A1 = matrix(A2, ncol = maxCN + 1, nrow = maxCN + 1)
A2 = t(A1)
E1 = c(alpha*A1 + (1-alpha))
E2 = c(alpha*A2 + (1-alpha))
### Positions of lattice points in observed scale and
### each segment's distance to it's lattice point -> weight
e1 = F[1,1] * E1 + F[1,2]*E2
e2 = F[2,1] * E1 + F[2,2]*E2
alldist = apply(x, 1, dist, y = cbind(e1, e2))
closest = apply(alldist, 2, which.min)
e = cbind(e1, e2)[closest,]
di = dist(x, e)
r = di*w
rhoargs = list(k = nmad*mad(di), c = nmad*mad(di))
eachrho = do.call(rhofunc, args = list(x = r, args = rhoargs))
#Plot
par(mfrow=c(2,2), mar = c(4, 4, 3, .1))
plot(r, eachrho, log = 'xy', xlab = 'r = Distance * Weight',
ylab = 'Contribution to rho')
plot(di, eachrho, log = 'xy', xlab = 'Distance to lattice point',
ylab = 'Contribution to rho')
plot(mat$length, w, log = 'xy', xlab = 'Segment length',
ylab = 'Weight')
plot(di, r, log = 'xy', ylab = 'r = Distance * Weight',
xlab = 'Distance to lattice point')
title(main = 'Evaluation of rho function and weights', line = -1,
outer = T)
}
rho = function(pars, x, w, EE, rhofunc = 'huber', rhoargs = NULL){
F = matrix(pars, ncol = 2, nrow = 2)
ee1 = F[1,1] * EE[,1] + F[1,2]*EE[,2]
ee2 = F[2,1] * EE[,1] + F[2,2]*EE[,2]
#Assign observations to closest lattice point
alldist = apply(x, 1, dist, y = cbind(ee1, ee2))
closest = apply(alldist, 2, which.min)
#Calculate residuals
e = cbind(ee1, ee2)[closest,]
r = dist(x, e)*w
sum(do.call(rhofunc, args = list(x = r, args = rhoargs)))
}
huber = function(x, args, ...){
k = args$k
out = x*x/2
ix = which(abs(x)>k)
out[ix] = k*(abs(x[ix])-k/2)
out
}
tukey = function(x, args, ...){
c = args$c
out = c*c/6*(1-(1-(x/c)^2)^3)
ix = which(abs(x)>c)
out[ix] = c*c/6
out
}
leastsq = function(x, ...){
x^2
}
getdist = function(mat, alpha, F, maxCN = 24){
x = cbind(mat$a1, mat$a2)
Fm1 = solve(F)
A2 = 0:maxCN
A1 = matrix(A2, ncol = maxCN + 1, nrow = maxCN + 1)
A2 = t(A1)
E1 = c(alpha*A1 + (1-alpha))
E2 = c(alpha*A2 + (1-alpha))
### Positions of lattice points in observed scale and
### each segment's distance to it's lattice point -> weight
e1 = F[1,1] * E1 + F[1,2]*E2
e2 = F[2,1] * E1 + F[2,2]*E2
alldist = apply(x, 1, dist, y = cbind(e1, e2))
closest = apply(alldist, 2, which.min)
e = cbind(e1, e2)[closest,]
di = dist(x, e)
di
}
optimizeRotation = function(obj){
mat = obj$mat
mystart = obj$mystart
di = getdist(mat, mystart$alphamax, mystart$F)
obj$rhoargs = list(c = mad(di)*obj$nmad)
x = cbind(mat$a1, mat$a2)
pars = solnp(pars = c(mystart$alphamax, mystart$F), fun = rho.alphamax.f, x = x,
w = obj$w, maxCN = 24, rhofunc = obj$rhofunc,
rhoargs = obj$rhoargs)$pars
alpha = pars[1]
F = matrix(pars[-1], ncol = 2, nrow = 2)
Fm1 = solve(F)
a1p = obj$mat$a1*Fm1[1,1] + obj$mat$a2 * Fm1[1,2]
a2p = obj$mat$a1*Fm1[2,1] + obj$mat$a2 * Fm1[2,2]
xlim = c(0, quantile(a2p, probs = .95, na.rm = T)*1.2)
ylim = c(0, quantile(a1p, probs = .95, na.rm = T)*1.2)
plot.transformed(mat, alpha, F, w=obj$w, rhoargs=obj$rhoargs, rhofunc = obj$rhofunc,
xlim = xlim, ylim = ylim)
dev.print(png, file=file.path(obj$d, paste(info$sample.name[i],'_3.2_rotated_grid.png',
sep = '')), width=640, height=500)
#All possible alphas and Fs
maxCN = 24
#All possible alphas
EEs = alpha*c(0:maxCN) + (1-alpha)
alphas = NULL
fs = NULL
for (j in 1:length(EEs)){
alphas = c(alphas, alpha/(EEs[j]+alpha))
if (j == 1) fs = 1 else {
fs = c(fs, fs[j-1]*alphas[j-1]/alphas[j])
}
}
obj$alphas = alphas
obj$fs = fs
obj$rotation.pars = pars
obj
}
####################
###Old
####################
transform.startval = function(a1, a2, alpha, maxCN=24){
#Standard setup
A2 = 0:maxCN
A1 = matrix(A2, ncol = maxCN + 1, nrow = maxCN + 1)
A2 = t(A1)
ix.na = A1>A2
A1[ix.na] = NA
A2[ix.na] = NA
E1 = alpha*A1 + (1-alpha)
E2 = alpha*A2 + (1-alpha)
#Plot
xlim = c(quantile(a2, probs = c(.025,.975)))
ylim = c(quantile(a1, probs = c(.025,.975)))
plot(a2, a1, pch = 16, col = col.transp('grey', .3),
xlim = xlim, ylim = ylim, xlab = 'a2', ylab = 'a1')
abline(0,1, col = 'red')
title(main = i)
print('Mark two subsequent CN clusters along a "vertical" line:')
pos = locator(2)
dy = abs(diff(pos$y))
print('Mark two distant points in one "vertical" line:')
pos = locator(2)
l = lm(x~y, data = pos)$coef[2]
print('Mark two subsequent CN clusters along a horizontal line:')
pos = locator(2)
dx = abs(diff(pos$x))
print('Mark two distant points in one "horizontal" line:')
pos = locator(2)
k = lm(y~x, data = pos)$coef[2]
f11 = dy/alpha
f22 = dx/alpha
F = matrix(c(f11, l*f11, k*f22, f22), ncol = 2, nrow = 2)
### Starting value positions of lattice points
e1 = F[1,1] * E1 + F[1,2]*E2
e2 = F[2,1] * E1 + F[2,2]*E2
ee1 = c(e1)[!is.na(c(e1))]
ee2 = c(e2)[!is.na(c(e2))]
###Assign observations to closest lattice point
x = cbind(a1, a2)
alldist = apply(x, 1, dist, y = cbind(ee1, ee2))
closest = apply(alldist, 2, which.min)
points(a2, a1, pch = 16, col = col.transp(closest, .3), cex = .7)
points(ee2, ee1)
F
}
##################################################################
##################################################################
##################################################################
##################################################################
##
## Read mutations and set rotation/scaling
##
##################################################################
##################################################################
readMutations = function(obj){
###Read mutations
maf.fn = paste(getwd(),
'/AROMA/rawData/responsify/Exome/MAF/AllVarintsMutectOncotatorPlusMutsigCategNo5214_mindepth10_withvaf', '_',
obj$info$sample.name[obj$i], '.maf', sep = '')
maf = read.delim(maf.fn, row.names = NULL, stringsAsFactors = FALSE,
check.names = FALSE, na.strings = c("NA", "---"),
blank.lines.skip = TRUE, comment.char = "#")
maf$depth = maf$t_alt_count + maf$t_ref_count
#Assign mutations to segments
mat = obj$mat
maf$segment = NA
for (seg in 1:nrow(mat)){
ix = which(maf$Start_position >= mat$Start.bp[seg] &
maf$Start_position <= mat$End.bp[seg]
& as.character(maf$Chromosome) == as.character(mat$Chromosome[seg]))
maf$segment[ix] = seg
}
muts = unique(subset(maf, select = c('Chromosome','Start_position','End_position',
't_alt_count','t_ref_count','segment', 'depth',
'effect', 'Hugo_Symbol')))
print(paste(nrow(muts), 'mutations in sample'))
muts$af = muts$t_alt_count/(muts$t_alt_count+muts$t_ref_count)
muts = subset(muts, !is.na(segment) )
muts = subset(muts, muts$depth>=20)
print(paste('Keeping', nrow(muts), 'mutations with read depth at least 20') )
obj$muts = muts
obj
}
plotAfByCN = function(obj, ns = 1:4){
mat = obj$mat
muts = obj$muts
i = obj$i
par(mfrow = c(2,2))
for (n in ns){
F = matrix(obj$rotation.pars[-1], ncol = 2, nrow = 2)
alpha = obj$alphas[n]
F = F*obj$fs[n]
Fm1 = solve(F)
c1 = mat$a1*Fm1[1,1] + mat$a2 * Fm1[1,2]
c2 = mat$a1*Fm1[2,1] + mat$a2 * Fm1[2,2]
c1 = (c1 - (1 - alpha))/alpha
c2 = (c2 - (1 - alpha))/alpha
minor = alpha*c1/(alpha*(c1+c2) + (1-alpha)*2)
major = alpha*c2/(alpha*(c1+c2) + (1-alpha)*2)
both = alpha*(c1+c2)/(alpha*(c1+c2) + (1-alpha)*2)
ord = order(both, major)
plot(both[ord], ylim = c(-0.2,1), ylab = 'Allele fraction', xlab = 'CN segment',
main = paste('CN = 0 at gridline',n, 'from bottom/left'))
points(major[ord], col = 'blue')
points(minor[ord], col = 'red')
muts$index = NA
for (j in 1:nrow(muts)){
if (!is.na(muts$segment[j])) muts$index[j] =
which(ord == muts$segment[j])
}
points(muts$index, muts$af, col = 'green', pch = 16)
abline(h = 0)
}
dev.print(png, file=file.path(obj$d, paste(obj$info$sample.name[i],'_4.1_minor_major_af.png',
sep = '')), width=900, height=900)
}
plotAfByPosition = function(obj, ns = 2:3, restrict = FALSE){
###Plot by genome position
par(mfrow = c(2,1))
mat = obj$mat
muts = obj$muts
i = obj$i
for (n in ns){
F = matrix(obj$rotation.pars[-1], ncol = 2, nrow = 2)
alpha = obj$alphas[n]
F = F*obj$fs[n]
Fm1 = solve(F)
c1 = mat$a1*Fm1[1,1] + mat$a2 * Fm1[1,2]
c2 = mat$a1*Fm1[2,1] + mat$a2 * Fm1[2,2]
c1 = (c1 - (1 - alpha))/alpha
c2 = (c2 - (1 - alpha))/alpha
mult = muts$af*(alpha*((c1 + c2)[muts$segment])+(1-alpha)*2)/alpha
plot.CNmut(plotmat = mat, plotmuts = muts, c1 = c1, c2 = c2, mult = mult,
main = paste('n =', n, ', purity', round(alpha*100), '%'),
restrict = restrict)
}
dev.print(png, file=file.path(obj$d, paste(obj$info$sample.name[i],'_4.2_CNmuts_preliminary.png',
sep = '')), width=900, height=900)
}
setRotation = function(obj, n){
mat = obj$mat
alpha = obj$alphas[n]
F = matrix(obj$rotation.pars[-1], ncol = 2, nrow = 2)
F = F*obj$fs[n]
Fm1 = solve(F)
mat$a1r = obj$mat$a1*Fm1[1,1] + obj$mat$a2 * Fm1[1,2]
mat$a2r = obj$mat$a1*Fm1[2,1] + obj$mat$a2 * Fm1[2,2]
xlim = c(0, quantile(mat$a2r, probs = .95, na.rm = T)*1.2)
ylim = c(0, quantile(mat$a1r, probs = .95, na.rm = T)*1.2)
plot.transformed(mat, alpha, F = F, w=obj$w, rhoargs=obj$rhoargs, rhofunc = obj$rhofunc,
xlim = xlim, ylim = ylim)
mtext(paste('Purity', round(alpha*100), '%'),adj = 0.1, outer = T, line = -2)
dev.print(png, file=file.path(obj$d, paste(info$sample.name[i],'_4.3_rotated_grid_chosen.png',
sep = '')), width=640, height=500)
#Save settings
obj$alpha = alpha
obj$F = F
obj$Fm1 = Fm1
obj$n = n
obj$mat = mat
obj
}
setCNs = function(obj, linearize = FALSE){
alpha = obj$alpha
mat = obj$mat
vari1 = mat$a1r
vari2 = mat$a2r
if (linearize) {
vari1 = mat$a1rl
vari2 = mat$a2rl
}
c1 = (vari1 - (1 - alpha))/alpha
c2 = (vari2 - (1 - alpha))/alpha
mat$c1 = pmin(c1, c2)
mat$c2 = pmax(c1, c2)
obj$ploidy = sum((mat$c1 + mat$c2)*mat$W)
par(mfrow = c(1,1), mar = c(3, 3, 2, .1))
xlim = c(-1, quantile(mat$c2, probs = c(.95))*c(1.2))
ylim = c(-1, quantile(mat$c1, probs = c(.95))*c(1.2))
plot.cn(x = mat$c2, y = mat$c1, mycols = col.transp('blue', alpha = .5),
xlim = xlim, ylim = ylim, xlab = 'Major CN', ylab = 'Minor CN')
mtext(paste('Ploidy =', format(round(obj$ploi,1),nsmall = 1)), adj = 0)
dev.print(png, file=file.path(obj$d,
paste(info$sample.name[i],'_5.4_CNs.png',
sep = '')), width=400, height=400)
obj$mat = mat
obj
}
setMultiplicity = function(obj){
muts = obj$muts
mat = obj$mat
#Express vaf as number of copies per cell (cellular multiplicity)
alpha = obj$alpha
muts$mult = muts$af/alpha*
(alpha*((mat$c1 + mat$c2)[muts$segment])+(1-alpha)*2)
obj$muts = muts
obj
}
##################################################################
##################################################################
##################################################################
##################################################################
##
## Linearization
##
##################################################################
##################################################################
getpeaks = function(val, alpha, maxCN=10){
EEs = alpha*c(0:maxCN) + (1-alpha)
delta = EEs[2] - EEs[1]
peaks = numeric(length(EEs))
expected <- EEs[1]
den = density(val,kernel="gaussian", bw = .03)
ix = abs(den$x - expected) < (delta/4)
tp = try((den$x[ix])[which.max(den$y[ix])], silent = T)
peaks[1] = ifelse(is.na(tp[1]) | inherits(tp, 'try-error'), expected, tp)
for (j in 2:length(EEs)){
expected = peaks[j-1] + delta
ix = abs(den$x - expected) < (delta/4)
tp = try((den$x[ix])[which.max(den$y[ix])], silent = T)
peaks[j] = ifelse(is.na(tp[1]) | inherits(tp, 'try-error'), expected, tp)
delta = peaks[j] - peaks[j-1]
}
peaks
}
linearize = function(obj, vari = 'a1r',
output = FALSE, logis = FALSE, asymporig = FALSE, none = FALSE, plot = FALSE){
y = obj$mat[,vari]
if (none) return(y)
alpha = obj$alpha
maxCN = 6
bw = .03
if (output & logis & asymporig) return(print('Cannot return both logis and asymporig estimates.'))
#Find peaks
EEs = alpha*c(0:maxCN) + (1-alpha)
ypeaks = getpeaks(y, alpha = alpha, maxCN = maxCN)
par(mfrow = c(2,1), mar = c(3,3,1,1))
yden = density(y,kernel="gaussian", bw = bw)
yden$y = log(yden$y+1)
plot(1, 1, xlim = c(0, max(EEs)), ylim = range(yden$y),
type = 'n', yaxt = 'n')
title(ylab = 'Smoothed frequency', xlab = 'Array intensity', line = 2)
legend('topleft', lty = c('solid', 'dotted'), col = c(1,2),
legend = c('Suggested peak positons','Linear peak positions'),
bty = 'n', cex = .8)
polygon(yden$x, yden$y, col = col.transp(1, .5), border = NA)
abline(v = ypeaks)
abline(v = EEs, col = 'red', lty = 'dotted')
plot(EEs, ypeaks, xlim = c(0, max(EEs)), ylim = c(0, max(EEs)))
title(xlab = 'Linear peak position', ylab = 'Suggested peak position', line = 2)
abline(0,1, col = 'red')
df = data.frame(x = EEs, y = ypeaks)
#Logistic regression
leg = 'No linear adjustment'
cols = 2
if (asymporig){
try({
out = NULL
leg = c(leg, 'Asymptotic regression through origin')
cols = c(cols, 4)
mod <- nls(ypeaks ~ SSasympOrig(EEs, Asym, lrc), data = df)
g = function(x) coef(mod)['Asym']*(1-exp(-x*exp(coef(mod)['lrc'])))
lines(EEs, g(EEs), col = 'blue')
gm1 = function(y, mod) {
y = pmin(coef(mod)['Asym']*.9, y)
- exp(-coef(mod)['lrc'])*log(1-y/coef(mod)['Asym'])
breakat = .9
ybreak = coef(mod)['Asym']*breakat
xbreak = - exp(-coef(mod)['lrc'])*log(1-ybreak/coef(mod)['Asym'])
ix = y>ybreak
out = numeric(length(y))
out[!ix] = - exp(-coef(mod)['lrc'])*log(1-y[!ix]/coef(mod)['Asym'])
yvec = ybreak*.99
xvec = - exp(-coef(mod)['lrc'])*log(1-yvec/coef(mod)['Asym'])
yvec = c(yvec, ybreak)
xvec = c(xvec, xbreak)
linmod = lm(xvec~yvec)
out[ix] = predict(linmod, newdata = data.frame(yvec = y[ix]))
out
}
out = gm1(y, mod)
if (is.null(out)) leg[length(leg)] = paste(leg[length(leg)], 'not converging')
}, silent = T)
}
if (logis){
try({
out = NULL
leg = c(leg, 'Logistic regression')
cols = c(cols, 3)
mod <- nls(ypeaks ~ SSlogis(EEs, Asym, xmid, scal), data = df)
g = function(x) coef(mod)['Asym']/(1+exp((coef(mod)['xmid']-x)/coef(mod)['scal']))
lines(EEs, g(EEs), col = 'green')
gm1 = function(y, mod) {
breakat = .9
ybreak = coef(mod)['Asym']*breakat
xbreak = coef(mod)['xmid'] - coef(mod)['scal']*
log((coef(mod)['Asym']-ybreak)/
ybreak)
ix = y>ybreak
out = numeric(length(y))
out[!ix] = coef(mod)['xmid'] - coef(mod)['scal']*
log((coef(mod)['Asym']-y[!ix])/y[!ix])
yvec = ybreak*.99
xvec = coef(mod)['xmid'] - coef(mod)['scal']*
log((coef(mod)['Asym']-yvec)/yvec)
yvec = c(yvec, ybreak)
xvec = c(xvec, xbreak)
linmod = lm(xvec~yvec)
out[ix] = predict(linmod, newdata = data.frame(yvec = y[ix]))
out
}
out = gm1(y, mod)
if (is.null(out)) leg[length(leg)] = paste(leg[length(leg)], 'not converging')
}, silent = T)
}
legend('topleft', lty = rep('solid', length(leg)), col = c(2,4,3),
legend = leg,
bty = 'n', cex = .8)
if (length(grep('1', vari))>0 & plot) dev.print(png, file=file.path(obj$d,
paste(obj$info$sample.name[obj$i],'_5.1_nonlinearlity.png',
sep = '')), width=900, height=900)
if (length(grep('2', vari))>0 & plot) dev.print(png, file=file.path(obj$d,
paste(obj$info$sample.name[obj$i],'_5.2_nonlinearlity.png',
sep = '')), width=900, height=900)
if (output) return(out)
NULL
}
plotLin = function(obj){
mat = obj$mat
alpha = obj$alpha
maxCN = 24
#Calculate lattice points from alpha
A2 = 0:maxCN
A1 = matrix(A2, ncol = maxCN + 1, nrow = maxCN + 1)
A2 = t(A1)
E1 = c(alpha*A1 + (1-alpha))
E2 = c(alpha*A2 + (1-alpha))
EEs = unique(E1)
#Plot CN lattice
par(mfrow = c(2,2))
xlim = c(0, quantile(mat$a2r, probs = .95)*1.2)
ylim = c(0, quantile(mat$a1r, probs = .95)*1.2)
plot.cn(x = mat$a2r, y = mat$a1r,
xlim = xlim, ylim = ylim, grid = FALSE,
xlab = 'Rotated a2',
ylab = 'Rotated a1')
for (j in 1:length(EEs)){
lines(range(EEs), rep(EEs[j], 2), lty = 'dotted', col = 'grey20')
lines(rep(EEs[j], 2), range(EEs), lty = 'dotted', col = 'grey20')
}
plot.cn(x = mat$a2rl, y = mat$a1rl,
xlim = xlim, ylim = ylim, grid = FALSE,
xlab = 'Rotated a2 after non-linearity fix',
ylab = 'Rotated a1 after non-linearity fix')
for (j in 1:length(EEs)){
lines(range(EEs), rep(EEs[j], 2), lty = 'dotted', col = 'grey20')
lines(rep(EEs[j], 2), range(EEs), lty = 'dotted', col = 'grey20')
}
### OVERLAY PLOT Without reattenuation
### Each segment's distance to it's lattice point
x = cbind(mat$a1r, mat$a2r)
alldist = apply(x, 1, dist, y = cbind(E1, E2))
closest = apply(alldist, 2, which.min)
e = cbind(E1, E2)[closest,]
di = dist(x, e)
xcoord = x[,2]-e[,2]
ycoord = x[,1]-e[,1]
smoothScatter(xcoord, ycoord, xlim = c(-alpha*3/4, alpha*3/4),
ylim = c(-alpha*3/4, alpha*3/4), main = 'Not linearized')
abline(v=0, h=0)
rect(xleft = -alpha/2, ybottom = -alpha/2, xright = alpha/2,
ytop = alpha/2, lty = 'dotted')
### OVERLAY PLOT With reattenuation
### Each segment's distance to it's lattice point
x = cbind(mat$a1rl, mat$a2rl)
alldist = apply(x, 1, dist, y = cbind(E1, E2))
closest = apply(alldist, 2, which.min)
e = cbind(E1, E2)[closest,]
di = dist(x, e)
xcoord = x[,2]-e[,2]
ycoord = x[,1]-e[,1]
smoothScatter(xcoord, ycoord, xlim = c(-alpha*3/4, alpha*3/4),
ylim = c(-alpha*3/4, alpha*3/4), main = 'Linearized')
abline(v=0, h=0)
rect(xleft = -alpha/2, ybottom = -alpha/2, xright = alpha/2,
ytop = alpha/2, lty = 'dotted')
dev.print(png, file=file.path(obj$d, paste(info$sample.name[i],'_5.3_linearization.png',
sep = '')), width=900, height=900)
}
nonlinAdj.asympOrig = function(y, alpha = 1, maxCN = 10, bw = .03){
EEs = alpha*c(0:maxCN) + (1-alpha)
ypeaks = getpeaks(y, alpha = alpha, maxCN = maxCN)
par(mfrow = c(2,1), mar = c(3,3,1,1))
#Single scale density y
yden = density(y,kernel="gaussian", bw = bw)
yden$y = log(yden$y+1)
plot(1, 1, xlim = c(0, max(EEs)), ylim = range(yden$y),
type = 'n', yaxt = 'n')
title(ylab = 'Smoothed frequency', xlab = 'Array intensity', line = 2)
legend('topleft', lty = c('solid', 'dotted'), col = c(1,2),