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Absolute.r
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> RunAbsolute = function (seg.dat.fn, sigma.p, max.sigma.h, min.ploidy, max.ploidy,
primary.disease, platform, sample.name, results.dir, max.as.seg.count,
max.non.clonal, max.neg.genome, copy_num_type, maf.fn = NULL,
min.mut.af = NULL, output.fn.base = NULL, verbose = FALSE)
{
kQ = 15
kTauDom = c(min.ploidy, max.ploidy)
kSigmaHDom = c(0, max.sigma.h)
kPiSomThetaQ = c(100, 50, rep(2, (kQ - 2)))
mut_class_w = list(SM = 0.5, GL = 0, SC = 0.5, OL = 0.001,
Pi_SM = 15, Pi_SC = 15)
platform = match.arg(platform, c("SNP_6.0", "Illumina_WES",
"SNP_250K_STY"))
if (platform %in% c("SNP_6.0", "SNP_250K_STY")) {
filter_segs = FALSE
} else if (platform == "Illumina_WES") {
filter_segs = TRUE
} else {
stop("Unsupported platform: ", platform)
}
min_probes = 10
max_sd = 100
copy_num_type = match.arg(copy_num_type, c("allelic", "total"))
if (copy_num_type == "total") {
pi_theta_qz = list(W = c(1, 25, 100, 25, 10, 5, rep(1,
kQ - 6), 10), PC = 0.05)
ABSOLUTE:::set_total_funcs()
} else if (copy_num_type == "allelic") {
pi_theta_qz = list(W = c(25, 100, 25, 10, 5, rep(1, kQ -
5), 10), PC = 0.05)
ABSOLUTE:::set_allelic_funcs()
} else {
stop("Unsupported copy number type: ", copy_num_type)
}
SubclonalPost <<- ABSOLUTE:::UnifSubclonalPost
sigma.h = sigma.p
tmp.dir = file.path(results.dir, "tmp")
dir.create(tmp.dir, recursive = TRUE, showWarnings = FALSE)
dir.create(results.dir, recursive = TRUE, showWarnings = FALSE)
seg.dat = MakeSegObj(seg.dat.fn, min_probes = min_probes,
max_sd = max_sd, filter_segs = filter_segs, verbose = verbose)
seg.dat[["primary.disease"]] = primary.disease
seg.dat[["group"]] = ABSOLUTE:::DetermineGroup(primary.disease)
seg.dat[["platform"]] = platform
seg.dat[["sample.name"]] = as.character(sample.name)
if (is.null(seg.dat$array.name)) {
seg.dat$array.name = seg.dat$sample.name
}
seg.dat[["maf.fn"]] = maf.fn
seg.dat[["obs.scna"]] = ExtractSampleObs(seg.dat)
seg = seg.dat[["obs.scna"]][["segtab"]]
e.cr = sum(seg[, "W"] * seg[, "copy_num"])
if (verbose) {
print(paste("Expected copy-ratio = ", round(e.cr, 5),
sep = ""))
}
mode.res = list(mode.flag = NA)
if (nrow(seg) > max.as.seg.count) {
mode.res[["mode.flag"]] = "OVERSEG"
}
if ((e.cr < 0.75) || (e.cr > 1.25)) {
mode.res[["mode.flag"]] = "E_CR_SCALE"
}
if (is.na(mode.res[["mode.flag"]])) {
maf = NULL
if ((!is.null(maf.fn)) && (file.exists(maf.fn))) {
maf = read.delim(maf.fn, row.names = NULL, stringsAsFactors = FALSE,
check.names = FALSE, na.strings = c("NA", "---"),
blank.lines.skip = TRUE, comment.char = "#")
} else {
if (verbose) {
print(paste("MAF file: ", maf.fn, " not found.",
sep = ""))
}
}
data(ChrArmsDat, package = "ABSOLUTE")
if ((!is.null(maf)) && (nrow(maf) > 0)) {
if (is.null(min.mut.af)) {
stop("min.mut.af must be defined")
}
mut.cn.dat = ABSOLUTE:::CreateMutCnDat(maf, seg.dat, min.mut.af,
verbose = verbose)
mut = list(mut_CN_dat = mut.cn.dat, Pi_som_theta_Q = kPiSomThetaQ,
mut_class_W = mut_class_w)
} else {
mut = NA
}
mode.res = ABSOLUTE:::FitSample(seg.dat, mut, kQ, pi_theta_qz, sigma.h,
kTauDom, kSigmaHDom, chr.arms.dat, verbose = verbose)
mode.res = myapply_subclonal_scna_model(segobj = seg.dat, mode_res = mode.res,
verbose = verbose)
if (inherits(mode.res, "try-error")) {
mode.res = list(mode.flag = "FAIL")
}
}
if (is.na(mode.res[["mode.flag"]])) {
bad.ix = ABSOLUTE:::GenomeHetFilter(seg.dat[["obs.scna"]], mode.res,
max.non.clonal, max.neg.genome, kQ, verbose = verbose)
if (sum(bad.ix) == nrow(mode.res[["mode.tab"]])) {
mode.res = list(mode.flag = "ALPHA_TAU_DOM")
} else {
mode.res = ABSOLUTE:::ReorderModeRes(mode.res, !bad.ix)
}
}
if (is.na(mode.res[["mode.flag"]])) {
data(ChrArmPriorDb, package = "ABSOLUTE")
model.id = ifelse(seg.dat[["group"]] %in% names(train.obj),
seg.dat[["group"]], "Primary")
mode.res = ABSOLUTE:::ApplyChrArmPrior(mode.res, model.id, train.obj,
verbose = verbose)
if ((!is.null(maf)) && (nrow(maf) > 0)) {
if (is.null(min.mut.af)) {
stop("maf was not NULL, but min.mut.af was")
}
seg.dat[["mut.cn.dat"]] = mut.cn.dat
mode.res = ABSOLUTE:::ApplySomaticMutsModel(mode.res, seg.dat$obs_SCNA,
mut.cn.dat, kPiSomThetaQ, mut_class_w, kQ, verbose = verbose)
}
mode.res = ABSOLUTE:::WeighSampleModes(mode.res)
mode.res[["call.status"]] = ABSOLUTE:::GetCallStatus(mode.res, seg.dat[["obs.scna"]][["W"]])
}
seg.dat[["mode.res"]] = mode.res
if (is.null(output.fn.base)) {
output.fn.base = ifelse(is.null(seg.dat$array.name),
sample.name, seg.dat$array.name)
}
file.base = paste(output.fn.base, ".ABSOLUTE", sep = "")
if (is.na(mode.res[["mode.flag"]])) {
sample.pdf.fn = file.path(results.dir, paste(file.base,
"plot.pdf", sep = "_"))
ABSOLUTE:::AbsoluteResultPlot(sample.pdf.fn, seg.dat)
} else {
if (verbose) {
print("Mode flag is NA, not generating plots. Sample has failed ABSOLUTE")
}
}
seg.dat$version = 1.1
save(seg.dat, file = file.path(results.dir, paste(file.base,
"RData", sep = ".")))
return(TRUE)
}
<environment: namespace:ABSOLUTE>
> ABSOLUTE:::CreateMutCnDat
function (maf, seg.dat, min.mut.af, verbose = FALSE)
{
mut.cn.dat <- maf
if ("total_normals_called" %in% colnames(mut.cn.dat)) {
ix <- mut.cn.dat[, "total_normals_called"] > 1
if (verbose) {
print(paste("Removing ", sum(ix), " of ", length(ix),
" mutations due to seen in > 1 normals", sep = ""))
}
mut.cn.dat <- mut.cn.dat[!ix, ]
}
if ("dbSNP_Val_Status" %in% colnames(mut.cn.dat)) {
mut.cn.dat[["dbSNP_Val_Status"]][is.na(mut.cn.dat[["dbSNP_Val_Status"]])] <- ""
}
cols <- colnames(mut.cn.dat)
cix <- which(cols %in% c("i_t_ref_count", "t_ref_count"))
if (length(cix) > 0) {
colnames(mut.cn.dat)[cix] <- "ref"
}
else {
stop("Malformed MAF file, no ref column supplied")
}
cix <- which(cols %in% c("i_t_alt_count", "t_alt_count"))
if (length(cix) > 0) {
colnames(mut.cn.dat)[cix] <- "alt"
}
else {
stop("Malformed MAF file, no alt column supplied")
}
cix <- which(cols == "dbSNP_Val_Status")
if (length(cix) > 0) {
colnames(mut.cn.dat)[cix] <- "dbSNP"
}
else {
stop("Malformed MAF file, no dbSNP_Val_Status column supplied")
}
cix <- which(cols == "Tumor_Sample_Barcode")
if (length(cix) > 0) {
colnames(mut.cn.dat)[cix] <- "sample"
}
else {
stop("Malformed MAF file, no Tumor_Sample_Barcode column supplied")
}
na.ix <- apply(is.na(mut.cn.dat[, c("ref", "alt")]), 1, sum) >
0
if (verbose) {
print(paste("Removing ", sum(na.ix), " of ", length(na.ix),
" mutations with NA coverage", sep = ""))
}
mut.cn.dat <- mut.cn.dat[!na.ix, ]
af <- mut.cn.dat[, "alt"]/(mut.cn.dat[, "alt"] + mut.cn.dat[,
"ref"])
ix <- af < min.mut.af
if (verbose) {
print(paste("Removing ", sum(ix), " of ", length(ix),
" mutations due to allelic fraction < ", min.mut.af,
sep = ""))
}
mut.cn.dat <- mut.cn.dat[!ix, ]
ix = mut.cn.dat[, "Hugo_Symbol"] %in% c("ADAM6")
if (verbose) {
print(paste("Removing ", sum(ix), " mutations in IG regions",
sep = ""))
}
if (sum(!ix) == 0) {
stop("no mutations left!")
}
mut.cn.dat = mut.cn.dat[!ix, , drop = FALSE]
mut.seg.ix <- GetMutSegIx(mut.cn.dat, seg.dat[["obs.scna"]][["segtab"]])
ix <- apply(is.na(mut.seg.ix), 1, sum) == 0
if (verbose && (sum(!ix) > 0)) {
print(paste("Removing ", sum(!ix), " unmapped mutations on Chrs: ",
sep = ""))
print(mut.cn.dat[!ix, "Chromosome"])
}
if (sum(ix) == 0) {
stop("No mutations left")
}
mut.cn.dat <- mut.cn.dat[ix, ]
mut.seg.ix <- mut.seg.ix[ix, , drop = FALSE]
mut.cn.dat <- cbind(mut.cn.dat, mut.seg.ix)
return(mut.cn.dat)
}
<environment: namespace:ABSOLUTE>
> ABSOLUTE:::ApplySomaticMutsModel
function (mode.res, obs_scna, mut.cn.dat, pi.som.theta.q, mut_class_w,
Q, verbose = FALSE)
{
q <- dim(mode.res[["theta.q.tab"]])[2]
if (verbose) {
print(paste("Evaluating ", nrow(mut.cn.dat), " mutations over ",
nrow(mode.res[["mode.tab"]]), " purity/ploidy modes: ",
sep = ""))
}
for (j in 1:nrow(mode.res[["mode.tab"]])) {
alpha <- mode.res[["mode.tab"]][j, "alpha"]
seg.qz.hat <- mode.res[["seg.qz.tab"]][j, , ]
seg.q.hat <- mode.res[["seg.q.tab"]][j, , ]
subclonal_scna_tab = mode.res$subclonal_SCNA_res$subclonal_SCNA_tab[j,
, ]
ccf_dens = mode.res$subclonal_SCNA_res$CCF_dens[j, ,
]
res = FitSomaticMuts(mut.cn.dat, mode.res$mode.tab[j,
], subclonal_scna_tab, ccf_dens, obs_scna, seg.qz.hat,
seg.q.hat, pi.som.theta.q, mut_class_w, Q, verbose = verbose)
muts.post.prs <- res[["muts.post.prs"]]
som.theta.q.map <- res[["som.theta.q.map"]]
if (j == 1) {
mode.res[["muts.post.prs"]] <- array(NA, dim = c(nrow(mut.cn.dat),
ncol(muts.post.prs) + 1, nrow(mode.res[["mode.tab"]])))
dimnames(mode.res[["muts.post.prs"]])[[2]] <- c(colnames(muts.post.prs),
"purity")
dimnames(mode.res[["muts.post.prs"]])[[1]] <- rownames(muts.post.prs)
}
mode.res[["muts.post.prs"]][, , j] <- cbind(muts.post.prs,
purity = alpha)
mode.res[["mode.tab"]][j, "somatic.mut.ll"] <- sum(muts.post.prs[,
"LL"], na.rm = TRUE) + dDirichlet(som.theta.q.map,
pi.som.theta.q, log.p = TRUE)
if (verbose) {
cat(".")
}
}
if (verbose) {
cat("\n")
}
new.ll <- mode.res[["mode.tab"]][, "clust_LL"] + mode.res[["mode.tab"]][,
"log.partition"] + mode.res[["mode.tab"]][, "somatic.mut.ll"]
mode.res[["mode.tab"]][, "post_LL"] <- new.ll
return(mode.res)
}
<environment: namespace:ABSOLUTE>
>
> ABSOLUTE:::FitSomaticMuts
function (mut.cn.dat, mode_info, subclonal_scna_tab, scna_ccf_dens,
obs_scna, seg.qz.hat, seg.q.hat, pi.som.theta.q, mut_class_w,
Q, verbose = FALSE)
{
mut.cn.dat = get_muts_nearest_clonal_scna(mut.cn.dat, seg.qz.hat,
seg.q.hat, Q)
mult_res = dir_post_fit_somatic_multiplicity(mut.cn.dat,
mode_info, mut_class_w, pi.som.theta.q, print_fit = verbose)
som_theta_q_map = mult_res$som_theta_Q_MAP
post_prs = mult_res$post_Prs
clonal_scna_mut_ix = !get_subclonal_scna_mut_ix(mut.cn.dat,
subclonal_scna_tab)
if (FALSE) {
clonal_scna_mult_res = dir_post_fit_somatic_multiplicity(mut.cn.dat[clonal_scna_mut_ix,
], mode_info, mut_class_w, pi.som.theta.q, print_fit = verbose)
som_theta_q_map = clonal_scna_mult_res$som_theta_Q_MAP
post_prs = matrix(NA, nrow = nrow(mut.cn.dat), ncol = ncol(clonal_scna_mult_res$post_prs))
colnames(post_prs) = colnames(clonal_scna_mult_res$post_Prs)
post_prs[clonal_scna_mut_ix, ] = clonal_scna_mult_res$post_Prs
subclonal_scna_mult_res = calc_sample_muts_on_subclonal_scna(mut.cn.dat[!clonal_scna_mut_ix,
], subclonal_scna_tab, scna_ccf_dens, mode_info,
obs_scna, mut_class_w, som_theta_q_map)
post_prs[!clonal_scna_mut_ix, ] = subclonal_scna_mult_res$post_Prs
}
var_classes = ClassifySomaticVariants(post_prs, 0.5)
q_s = post_prs[, "modal_q_s"]
mut_ccf_res = calc_snv_ccf(mut.cn.dat, mode_info, q_s, post_prs[,
"Pr_somatic_clonal"])
muts.post.prs <- cbind(as.matrix(mut.cn.dat[, c("q_hat",
"HS_q_hat_1", "HS_q_hat_2"), drop = FALSE]), post_prs,
var_classes, mut_ccf_res, subclonal_SCNA = !clonal_scna_mut_ix)
return(list(muts.post.prs = muts.post.prs, som.theta.q.map = som_theta_q_map))
}
<environment: namespace:ABSOLUTE>
> ABSOLUTE:::calc_snv_ccf
function (mut_dat, mode_info, modal_q_s, pr_somatic_clonal)
{
muts = mut_dat
alpha = mode_info["alpha"]
cov = rowSums(muts[, c("alt", "ref")])
f = muts[, "alt"]/cov
Q = muts[, "q_hat"]
q_s = rep(NA, length(pr_somatic_clonal))
c.ix = pr_somatic_clonal > 0.5
q_s[c.ix] = modal_q_s[c.ix]
q_s[!c.ix] = 1
som_delta = alpha/(2 * (1 - alpha) + alpha * Q)
f_s = q_s * som_delta
mut_scale = 1/f_s
cell_frac = f * mut_scale
cell_mult = f/som_delta
ccf_grid = seq(0, 1, by = 0.01)
ccf_dens = matrix(NA, nrow = nrow(mut_dat), ncol = length(ccf_grid))
ccf_ci95 = matrix(NA, nrow = nrow(ccf_dens), ncol = 2)
ccf_hat = rep(NA, nrow(mut_dat))
for (i in seq_len(nrow(mut_dat))) {
if (mut_dat[i, "q_hat"] == 0) {
next
}
ccf_dens[i, ] = calc_ccf_posterior_grid(mut_dat[i, "alt"],
mut_dat[i, "ref"], alpha, mut_dat[i, "q_hat"], ccf_grid)
ccf_hat[i] = ccf_grid[which.max(ccf_dens[i, ])]
ecdf = cumsum(ccf_dens[i, ])
ccf_ci95[i, ] = approx(x = ecdf, y = ccf_grid, xout = c(0.025,
0.975))$y
}
nix1 = is.na(ccf_ci95[, 1])
ccf_ci95[nix1, 1] = min(ccf_grid)
nix2 = is.na(ccf_ci95[, 2])
ccf_ci95[nix2, 2] = max(ccf_grid)
ix = ccf_ci95[, 2] > ccf_grid[length(ccf_grid) - 1]
ccf_ci95[ix, 2] = 1
ix = mut_dat[i, "q_hat"] == 0
ccf_ci95[ix, ] = NA
res = cbind(cell_mult, cell_frac, ccf_hat, ccf_ci95)
colnames(res) = c("cell_mult", "old_cancer_cell_frac", "cancer_cell_frac",
"ccf_CI95_low", "ccf_CI95_high")
return(res)
}
<environment: namespace:ABSOLUTE>
> ABSOLUTE:::dir_post_fit_somatic_multiplicity
function (mut_cn_dat, mode_info, mut_class_w, pi_som_theta_q,
print_fit = TRUE)
{
som_theta_Q_mode = (pi_som_theta_q - 1)/(sum(pi_som_theta_q) -
length(pi_som_theta_q))
mcv = c(mut_class_w$Pi_SM, mut_class_w$Pi_SC)
mcw = (mcv - 1)/(sum(mcv) - length(mcv))
mut_class_w$SM = mcw[1]
mut_class_w$SC = mcw[2]
mult_res = CalcSampleMutsPostPr(mut_cn_dat, mode_info, mut_class_w,
som_theta_Q_mode)
mut_mat = mult_res$som_mut_Q_tab * matrix(mult_res$post_Prs[,
"Pr_somatic_clonal"], nrow = nrow(mult_res$som_mut_Q_tab),
ncol = ncol(mult_res$som_mut_Q_tab), byrow = TRUE)
nix = mult_res$post_Prs[, "Pr_somatic_clonal"] == 0
mut_mat = mut_mat[!nix, , drop = FALSE]
if (nrow(mut_mat) > 0) {
som_q = colSums(mut_mat, na.rm = TRUE)
}
else {
som_q = rep(0, length(pi_som_theta_q))
}
som_theta_q_map = (pi_som_theta_q + som_q - 1)/(sum(pi_som_theta_q +
som_q) - length(pi_som_theta_q))
if (print_fit) {
cat("som_theta_Q_MAP: ")
print(round(som_theta_q_map, 5))
}
mut_w = mult_res$post_Prs[, c("Pr_somatic_clonal", "Pr_subclonal"),
drop = FALSE]
mut_w = mut_w/rowSums(mut_w)
mut_pr = colSums(mut_w, na.rm = TRUE)
mcw = (mcv + mut_pr - 1)/(sum(mcv + mut_pr) - length(mcv))
mut_class_w$SM = mcw[1]
mut_class_w$SC = mcw[2]
if (print_fit) {
print(mut_class_w)
}
mult_res = CalcSampleMutsPostPr(mut_cn_dat, mode_info, mut_class_w,
som_theta_q_map)
mult_res$som_theta_Q_MAP = som_theta_q_map
return(mult_res)
}
<environment: namespace:ABSOLUTE>
> ABSOLUTE:::ApplySomaticMutsModel
function (mode.res, obs_scna, mut.cn.dat, pi.som.theta.q, mut_class_w,
Q, verbose = FALSE)
{
q <- dim(mode.res[["theta.q.tab"]])[2]
if (verbose) {
print(paste("Evaluating ", nrow(mut.cn.dat), " mutations over ",
nrow(mode.res[["mode.tab"]]), " purity/ploidy modes: ",
sep = ""))
}
for (j in 1:nrow(mode.res[["mode.tab"]])) {
alpha <- mode.res[["mode.tab"]][j, "alpha"]
seg.qz.hat <- mode.res[["seg.qz.tab"]][j, , ]
seg.q.hat <- mode.res[["seg.q.tab"]][j, , ]
subclonal_scna_tab = mode.res$subclonal_SCNA_res$subclonal_SCNA_tab[j,
, ]
ccf_dens = mode.res$subclonal_SCNA_res$CCF_dens[j, ,
]
res = FitSomaticMuts(mut.cn.dat, mode.res$mode.tab[j,
], subclonal_scna_tab, ccf_dens, obs_scna, seg.qz.hat,
seg.q.hat, pi.som.theta.q, mut_class_w, Q, verbose = verbose)
muts.post.prs <- res[["muts.post.prs"]]
som.theta.q.map <- res[["som.theta.q.map"]]
if (j == 1) {
mode.res[["muts.post.prs"]] <- array(NA, dim = c(nrow(mut.cn.dat),
ncol(muts.post.prs) + 1, nrow(mode.res[["mode.tab"]])))
dimnames(mode.res[["muts.post.prs"]])[[2]] <- c(colnames(muts.post.prs),
"purity")
dimnames(mode.res[["muts.post.prs"]])[[1]] <- rownames(muts.post.prs)
}
mode.res[["muts.post.prs"]][, , j] <- cbind(muts.post.prs,
purity = alpha)
mode.res[["mode.tab"]][j, "somatic.mut.ll"] <- sum(muts.post.prs[,
"LL"], na.rm = TRUE) + dDirichlet(som.theta.q.map,
pi.som.theta.q, log.p = TRUE)
if (verbose) {
cat(".")
}
}
if (verbose) {
cat("\n")
}
new.ll <- mode.res[["mode.tab"]][, "clust_LL"] + mode.res[["mode.tab"]][,
"log.partition"] + mode.res[["mode.tab"]][, "somatic.mut.ll"]
mode.res[["mode.tab"]][, "post_LL"] <- new.ll
return(mode.res)
}
<environment: namespace:ABSOLUTE>
>
> ABSOLUTE:::GetCallStatus
function (mode.res, seg.w)
{
status = "called"
q.tab = mode.res[["seg.qz.tab"]][1, , ]
q.tab = q.tab[, c(1:(ncol(q.tab)))]
Q = ncol(q.tab) - 1
max.q = (apply(q.tab, 1, which.max))
peak_masses = rep(0, Q)
for (i in 1:Q) {
ix = which(max.q == i)
peak_masses[i] = sum(seg.w[ix])
}
b = mode.res[["mode.tab"]][1, "b"]
if (peak_masses[1] < 0.01 & b < 0.15) {
peak_masses[1] = 0
}
six = order(peak_masses, decreasing = TRUE)
if (peak_masses[six[3]] < 1e-04) {
if (peak_masses[six[2]] < 1e-04) {
if (mode.res[["mode.tab"]][1, "sigma.h.hat"] < 0.02 &
!is.null(mode.res[["muts.post.prs"]])) {
status = "non-aneuploid"
}
else {
status = "low purity"
}
}
}
if (mode.res[["mode.tab"]][1, "entropy"] > 0.2) {
status = "high entropy"
}
if (mode.res[["mode.tab"]][1, "frac.het"] > 0.2) {
status = "high non-clonal"
}
return(status)
}
<environment: namespace:ABSOLUTE>
myCreateReviewObject = function (obj.name, absolute.files, indv.results.dir, copy_num_type,
plot.modes = TRUE, verbose = FALSE, myModes = NULL)
{
if (copy_num_type == "total") {
ABSOLUTE:::set_total_funcs()
} else if (copy_num_type == "allelic") {
ABSOLUTE:::set_allelic_funcs()
} else {
stop("Unsupported copy number type: ", copy_num_type)
}
dir.create(indv.results.dir, recursive = TRUE)
nm = file.path(indv.results.dir, paste(obj.name, ".PP-modes",
sep = ""))
modesegs.fn = paste(nm, ".data.RData", sep = "")
pdf.fn = paste(nm, ".plots.pdf", sep = "")
failed.pdf.fn = paste(nm, "FAILED_plots.pdf", sep = ".")
failed.tab.fn = paste(nm, "FAILED_tab.txt", sep = ".")
call.tab.fn = file.path(indv.results.dir, paste(obj.name,
"PP-calls_tab.txt", sep = "."))
segobj.list = vector(mode = "list", length = length(absolute.files))
failed.list = vector(mode = "list", length = length(absolute.files))
so.ix = 1
fa.ix = 1
sids = character()
for (i in seq_along(absolute.files)) {
seg.out.fn = absolute.files[i]
if (!file.exists(seg.out.fn)) {
if (verbose) {
cat("\n")
print(paste("sample #", i, " result not found",
sep = ""))
}
next
}
load(absolute.files[i])
SID = seg.dat$sample.name
if (SID %in% sids) {
stop("Sample names must be unique")
}
sids = c(sids, SID)
if (is.null(seg.dat$array.name)) {
seg.dat$array.name = SID
}
if (is.na(seg.dat[["mode.res"]][["mode.flag"]])) {
segobj.list[[so.ix]] = seg.dat
names(segobj.list)[so.ix] = SID
so.ix = so.ix + 1
if (verbose) {
cat(".")
}
} else {
if (verbose) {
print(paste("Failed list got updated", i))
}
failed.list[[fa.ix]] = seg.dat
names(failed.list)[fa.ix] = SID
failed.list[[fa.ix]][["sample.name"]] = seg.dat[["sample.name"]]
fa.ix = fa.ix + 1
if (verbose) {
cat("-")
}
}
}
segobj.list = segobj.list[c(1:(so.ix - 1))]
failed.list = failed.list[c(1:(fa.ix - 1))]
mode.ent = rep(NA, length(segobj.list))
names(mode.ent) = names(segobj.list)
for (i in seq_along(segobj.list)) {
mtab = segobj.list[[i]][["mode.res"]][["mode.tab"]]
ix = which.max(mtab[, "post_LL"])
mode.ent[i] = mtab[ix, "entropy"]
}
samples = names(sort(mode.ent))
segobj.list = segobj.list[samples]
save(segobj.list, file = modesegs.fn)
ABSOLUTE:::PrintPpCallTable(segobj.list, call.tab.fn)
if (plot.modes) {
n.print = length(myModes)
myPlotModes(segobj.list, pdf.fn, n.print = n.print, myModes = myModes)
}
if (!is.null(failed.list[[1]])) {
ABSOLUTE:::PlotFailedSamples(failed.list, failed.pdf.fn)
ABSOLUTE:::PrintFailedTable(failed.list, failed.tab.fn)
}
return(TRUE)
}
myPlotModes = function (segobj.list, pdf.fn, n.print = NA, write.tab = TRUE,
debug.info = TRUE, add = FALSE, fig.mode = FALSE, sideways = FALSE,
low.w = FALSE, called.mode.ix = NA, verbose = FALSE, myModes = myModes)
{
pal <- colorRampPalette(c("chocolate4", "cyan4"))
if (fig.mode) {
add <- TRUE
debug.info <- FALSE
}
Q <- 15
alpha.dom <- c(0, 1)
tau.dom <- c(1, 10)
mode.colors <- ABSOLUTE:::GetModeColors()
x.max <- 2.25
binW <- 0.025
if (!add) {
pdf(pdf.fn, 3.5 * (1 + 2), 15)
par(mfrow = c(5, 4))
}
for (s in seq_len(length(segobj.list))) {
mode.tab <- segobj.list[[s]][["mode.res"]][["mode.tab"]]
seg.z.tab <- segobj.list[[s]][["mode.res"]][["seg.z.tab"]]
if (nrow(mode.tab) == 0) {
for (i in seq_len(n.print + 2)) {
frame()
}
next
}
obs <- ExtractSampleObs(segobj.list[[s]])
SN <- segobj.list[[s]][["sample.name"]]
if (is.na(n.print)) {
s.n.print <- nrow(mode.tab)
}
else {
s.n.print <- min(nrow(mode.tab), n.print)
}
s.mix <- s.n.print
if (!fig.mode) {
model.id <- segobj.list[[s]][["group"]]
ABSOLUTE:::PostPlot(mode.tab[myModes,], mode.colors, alpha.dom, tau.dom,
sample.name = SN, obs, called.mode.ix, debug.info, call.status =
segobj.list[[s]][["mode.res"]][["call.status"]],
model.id = model.id)
ABSOLUTE:::PpModeScorerBarplot(mode.tab[myModes,], mode.colors, obs, n.print)
if (length(segobj.list) == 1) {
frame()
frame()
}
}
n.plot <- min(n.print, NROW(mode.tab))
for (i in seq_len(n.plot)) {
tau <- mode.tab[myModes[i], "tau"]
alpha <- mode.tab[myModes[i], "alpha"]
delta <- alpha/(2 * (1 - alpha) + alpha * tau)
b <- 2 * (1 - alpha)/(2 * (1 - alpha) + alpha * tau)
mode.info <- mode.tab[myModes[i], ]
obs[["error.model"]][["fit.at"]] <- mode.tab[myModes[i], "AT"]
comb <- InvTxData(obs[["error.model"]], GetCopyRatioComb(Q,
delta, b, obs[["error.model"]]))
seg.dat <- InvTxData(obs[["error.model"]], obs[["d.tx"]])
last.plot <- ifelse(i == n.plot, TRUE, FALSE)
mid.plot <- ifelse(i == ceiling(n.plot/2), TRUE,
FALSE)
first.plot <- ifelse(i == 1, TRUE, FALSE)
if ((length(segobj.list) > 1) && (!is.null(segobj.list[[s]][["mut.cn.dat"]])) &&
(i > 1)) {
frame()
frame()
}
if (!is.null(segobj.list[[s]][["mode.res"]][["ab.tab"]])) {
comb.ab <- segobj.list[[s]][["mode.res"]][["ab.tab"]][myModes[i], ]
}
else {
comb.ab <- NA
}
ABSOLUTE:::ModeCombPlot(seg.dat, obs[["W"]], obs[["d.stderr"]],
mode.info, seg.z.tab[myModes[i], ], comb.ab, mode.colors[i],
comb, last.plot, mid.plot, first.plot, x.max,
debug.info, fig.mode, sideways, pal)
if (!is.na(called.mode.ix)) {
if (i == called.mode.ix) {
mtext(text = "*", side = 3, at = 0, col = "red",
line = -1, cex = 3 * par("cex") * par("cex.axis"))
}
}
if (!is.null(segobj.list[[s]][["mut.cn.dat"]])) {
mut.cn.dat <- segobj.list[[s]][["mut.cn.dat"]]
modeled <- segobj.list[[s]][["mode.res"]][["muts.post.prs"]][,
, myModes[i], drop = TRUE]
if (is.null(dim(modeled))) {
modeled <- matrix(modeled, nrow = 1)
colnames(modeled) <- dimnames(segobj.list[[s]][["mode.res"]][["muts.post_prs"]])[[2]]
}
modeled.mut.dat <- cbind(mut.cn.dat, modeled)
ABSOLUTE:::PlotSomaticMutDensities(modeled.mut.dat, segobj.list[[s]],
1, mode.colors[i], min.cov = 3, verbose = verbose)
}
}
if ((length(segobj.list) > 1) && (is.null(segobj.list[[s]][["mut.cn.dat"]]))) {
if (i < n.print) {
for (j in seq_len(n.print - i)) {
frame()
}
}
frame()
}
}
if (!add) {
dev.off()
}
}
ABSOLUTE:::ModeCombPlot
function (d, W, seg.stderr, mode.info, seg.z, comb.ab, mode.color,
comb, last.plot, mid.plot, first.plot, x.max, debug.info,
fig.mode, sideways, pal)
{
ix <- (d >= 0) & (d < x.max)
colpal <- pal(1000)
col <- FALSE
old.mar <- par("mar")
xlab = "Copy ratio"
if (debug.info) {
par(mar = old.mar + c(0, 0, 1, 0))
}
if (fig.mode) {
x.max <- 2
ix <- (d >= 0) & (d < x.max)
if (col) {
x.ax.labs <- ifelse(last.plot, TRUE, FALSE)
}
else {
x.ax.labs <- TRUE
}
if (col) {
xlab <- ifelse(last.plot, "Allelic copy ratio", "")
}
else {
xlab <- ifelse(first.plot, "Allelic copy ratio",
"")
}
if (col) {
ylab <- "Genomic fraction"
}
else {
ylab <- ifelse(mid.plot, "Genomic fraction", "")
}
if (!col) {
y.ax.labs <- first.plot
}
else {
y.ax.labs <- last.plot
}
if (sideways) {
tmp <- xlab
xlab <- ylab
ylab <- tmp
}
if (sideways) {
tmp <- x.ax.labs
x.ax.labs <- y.ax.labs
y.ax.labs <- tmp
}
if (!fig.mode) {
order.by <- W[ix]
}
else {
order.by <- seg.z[ix]
}
PlotSeglenHist(d[ix], W[ix], color.by = seg.z[ix], order.by = order.by,
color.range = c(0, 1), use.pal = colpal, bin.w = 0.04,
x.max = x.max, data.whiskers = debug.info, ylab = ylab,
xlab = xlab, x.ax.labs = x.ax.labs, y.ax.labs = y.ax.labs,
sideways = sideways, border = !fig.mode)
if (!col) {
if (first.plot) {
mtext("Candidate interpretations of copy profile",
side = 3, line = 0, adj = 0, cex = par("cex.axis"))
}
}
}
else {
PlotSeglenHist(d[ix], W[ix], color.by = seg.z[ix], color.range = c(0,
1), use.pal = colpal, bin.w = 0.025, x.max = x.max,
data.whiskers = FALSE, xlab = xlab, sideways = sideways)
}
msg1 <- substitute(paste(purity(hat(alpha)) == x1, ", ",
"ploidy: ", hat(tau) == x2, ", ", hat(tau)[g] == x3,
sep = ""), list(x1 = round(mode.info["alpha"], 2), x2 = round(mode.info["tau"],
2), x3 = round(mode.info["genome mass"], 2)))
msg2 <- substitute(paste(hat(sigma[H]) == x1, ", ", hat(theta[Z]) ==
x2, ", ", "%", Het == x3, sep = ""), list(x1 = round(mode.info["sigma.h.hat"],
3), x2 = round(mode.info["theta.z.hat"], 2), x3 = round(100 *
mode.info["frac.het"], 2)))
msg3 <- substitute(paste("SCNAs" == x6, ", ", "Kar" == x7,
", ", "SSNVs" == x8, sep = ""), list(x6 = round(mode.info["log.partition"],
2), x7 = round(mode.info["clust_LL"], 2), x8 = round(mode.info["somatic.mut.ll"],
2)))
if (debug.info) {
mtext(msg1, line = 4, cex = 0.75, adj = 0)
mtext(msg2, line = 3, cex = 0.75, adj = 0)
mtext(msg3, line = 2, cex = 0.75, adj = 0)
}
extra <- (comb[2] - comb[1])/2
for (q in seq_along(comb)) {
if (comb[q] > x.max) {
break
}
q_ix <- ifelse(q > 1, q - 1, q)
seg.ix <- d < comb[q_ix] - extra
cum_gen <- sum(W[seg.ix])
if (cum_gen > 0.99) {
break
}
}
max.q <- q - 1
top.axis <- FALSE
if (top.axis) {
axis(side = 3, at = comb[c(1:max.q)], labels = c(1:max.q) -
1, col = mode.color, col.ticks = mode.color)
}
for (q in seq_along(comb)) {
if (q > max.q) {
next
}
if (!sideways) {
abline(v = comb[q], lwd = 1.5, lty = 3, col = mode.color)
}
else {
abline(h = comb[q], lwd = 1.5, lty = 3, col = mode.color)
}
side <- ifelse(!sideways, 3, 4)
if (!top.axis) {
if (q - 1 < 10 | (q - 1)%%2 == 1) {
mtext(text = (q - 1), side = side, at = comb[q],
col = mode.color, line = 0.2, cex = par("cex") *
par("cex.axis"))
}
}
if (debug.info & !is.na(comb.ab)) {
q.ab <- round(comb.ab[q], 2)
if (is.finite(q.ab) & q.ab > 0) {
mtext(text = paste("(", q.ab, ")", sep = ""),
at = comb[q], col = 1, line = 1, cex = 0.5)
}
}
}
par(mar = old.mar)
}
PlotSeglenHist = function (D, W, color.by, color.range = NA, x.max = NA, y.max = NA,
bin.w = 0.05, order.by = NA, use.pal = NA, data.whiskers = TRUE,
xlab = "Observed allelic copy-ratio", x.axis = TRUE, y.axis = TRUE,
x.ax.labs = TRUE, y.ax.labs = TRUE, xlim = NA, ylab = "Genomic fraction",
border = TRUE, sideways = FALSE, add = FALSE)
{
P <- length(D)
if (is.na(x.max)) {
x.max <- max(D)
}
if (length(bin.w) == 1) {
res <- hist(D, breaks = seq(0, x.max + bin.w, bin.w),
plot = FALSE)
breaks <- res[["breaks"]]
}
else {
breaks <- bin.w
}
heights <- matrix(0, ncol = length(breaks), nrow = length(D))
seg.colors <- matrix(0, ncol = length(breaks), nrow = length(D))
if (is.na(use.pal)) {
col.scale <- 1000
use.pal <- heat.colors(col.scale)
}
else {
col.scale <- length(use.pal)
}
if (is.na(color.range)) {
color.range <- range(color.by, na.rm = TRUE)
}
pal.idx <- floor((color.by - color.range[1])/(color.range[2] -
color.range[1]) * (col.scale - 1))
for (p in seq_len(P)) {
bin <- max(which(breaks <= D[p]))
heights[p, bin] <- W[p]
seg.colors[p, bin] <- use.pal[pal.idx[p] + 1]
}
seg.colors[is.na(seg.colors)] <- "grey"
for (i in seq_len(ncol(heights))) {
if (is.na(order.by)) {
res <- sort(heights[, i], decreasing = TRUE, index.return = TRUE)
}
else {
res <- sort(order.by, index.return = TRUE)
}
heights[, i] <- heights[res[["ix"]], i]
seg.colors[, i] <- seg.colors[res[["ix"]], i]
}
seg.colors[heights == 0] <- NA
heights[heights == 0] <- NA
MyBarplot(heights, seg.colors, ylab, xlab, x.max, y.max,
add = add, breaks = breaks, xlim = xlim, border = border,
sideways = sideways)
if (data.whiskers) {
side <- ifelse(!sideways, 1, 2)
axis(side = side, at = D, labels = FALSE, line = -0.5,
col = "grey")
axis(side = side, at = D[!is.na(color.by)], labels = FALSE,
line = -0.5, col = "red")
x.axis.line <- 1
}
else {
x.axis.line <- NA
}
if (x.axis) {
if (!sideways) {
axis(side = 1, line = x.axis.line, labels = x.ax.labs)
}
else {
axis(side = 2, line = x.axis.line, labels = x.ax.labs)
}
}
if (y.axis) {
if (!sideways) {
par(ylog = FALSE)
axis(side = 2, labels = y.ax.labs, las = 2)
par(ylog = FALSE)
}
else {
par(xlog = FALSE)
axis(side = 1, labels = y.ax.labs, las = 1)
par(xlog = FALSE)
}
}
}
ABSOLUTE:::FitSample
function (seg.obj, mut, Q, pi_theta_qz, sigma.h, tau.dom, sigma.h.dom,
chr.arms.dat, verbose = FALSE)
{
kThetaQz = rep(1/(Q + 1), Q + 1)
obs = seg.obj[["obs.scna"]]
res = FindLocationModes(obs, mut, Q, kThetaQz, sigma.h, tau.dom,
verbose = verbose)
if (!is.null(res[["mode.flag"]])) {
return(list(mode.flag = res[["mode.flag"]]))
}
else {
mode.tab = res[["mode.tab"]]
}
if (is.na(mode.tab)) {
return(list(mode.flag = "ERROR"))
}
n.modes = nrow(mode.tab)
theta.qz.hat = matrix(NA, nrow = n.modes, ncol = Q + 1)
theta.q.tab = array(NA, dim = c(n.modes, Q))
if (verbose) {
print(paste("Optimizing LL(data, theta.qz, sigma.h | comb) for ",
n.modes, " modes: ", sep = ""))
}
seg.z.tab = array(NA, dim = c(n.modes, length(obs[["W"]])))
seg.qz.tab = array(NA, dim = c(n.modes, length(obs[["W"]]),
Q + 1))
seg.q.tab = array(NA, dim = c(n.modes, length(obs[["W"]]),
Q))
if (obs[["data.type"]] == "ALLELIC") {
ab.tab = array(NA, dim = c(n.modes, Q + 1))
chr.arm.tab = array(NA, dim = c(n.modes, 2, nrow(chr.arms.dat),
Q))
}
if (obs[["data.type"]] == "TOTAL") {
ab.tab = NULL
chr.arm.tab = array(NA, dim = c(n.modes, 1, nrow(chr.arms.dat),
Q))
}
dimnames(chr.arm.tab)[[3]] = rownames(chr.arms.dat)
mode.tab = cbind(mode.tab, NA, NA, NA, NA, NA, NA, NA, NA)
colnames(mode.tab)[c((ncol(mode.tab) - 7):ncol(mode.tab))] = c("genome mass",
"sigma.h.hat", "theta.z.hat", "frac.het", "log.partition",
"entropy", "clust_LL", "post_LL")
mode.tab = cbind(mode.tab, somatic.mut.ll = NA)
for (i in 1:n.modes) {
delta = mode.tab[i, "delta"]
b = mode.tab[i, "b"]
obs[["error.model"]][["fit.at"]] = mode.tab[i, "AT"]
comb = GetCopyRatioComb(Q, delta, b, obs[["error.model"]])
res = OptThetaQzSigmaH(obs, comb, sigma.h.dom, pi_theta_qz,
verbose = verbose)
mode.tab[i, "sigma.h.hat"] = res[["sigma.h.hat"]]
mode.tab[i, "log.partition"] = res[["LL"]]
mode.tab[i, "post_LL"] = res[["LL"]]
lambda.qz.res = res[["lambda.qz.res"]]
mode.tab[i, "theta.z.hat"] = res$theta_qz_hat[(Q + 1)]
res = ABSOLUTE:::GetSegQzPost(obs[["d.tx"]], obs[["d.stderr"]],
obs[["W"]], comb, NA, mode.tab[i, "sigma.h.hat"],
theta.qz = res[["theta_qz_hat"]])
seg.z.tab[i, ] = res[["QZ"]][, (Q + 1)]
seg.qz.tab[i, , ] = res[["QZ"]]