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adding post.sample.col #34

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b4f71f7
adding post.sample.col
Oct 21, 2019
d01c9a5
fix combined p
Nov 6, 2019
091abf4
unfix
Nov 9, 2019
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6 changes: 5 additions & 1 deletion R/clonevol.r
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Original file line number Diff line number Diff line change
Expand Up @@ -1451,6 +1451,7 @@ cross.rule.score <- function(x, meta.p.method='fisher', exhaustive.mode=FALSE,
for (s in samples){
mi = x$models[[s]][[x$match$index[i,s]]]
mi = mi[!mi$excluded & !is.na(mi$parent), c('lab', 'p.value')]

colnames(mi) = c('lab', s)
if (is.null(p)){
p = mi
Expand Down Expand Up @@ -2149,6 +2150,7 @@ infer.clonal.models <- function(c=NULL, variants=NULL,
ignore.clusters=NULL,
vaf.col.names=NULL,
ccf.col.names=NULL,
post.sample.col=NULL,
vaf.in.percent=TRUE,
depth.col.names=NULL,
weighted=FALSE,
Expand Down Expand Up @@ -2303,6 +2305,7 @@ infer.clonal.models <- function(c=NULL, variants=NULL,
boot = generate.boot(variants, vaf.col.names=vaf.col.names,
depth.col.names=depth.col.names,
vaf.in.percent=vaf.in.percent,
post.sample.col=post.sample.col,
num.boots=num.boots,
bootstrap.model=subclonal.test.model,
cluster.center.method=cluster.center,
Expand Down Expand Up @@ -3526,7 +3529,7 @@ sum.polyclonal <- function(x){
#' @export merge.samples
#' @examples
#' x2 = merge.samples(x, 1, c('C', 'M1197'), 'new', 'P', c('C_ref', 'M1197_ref'), c('C_var', 'M1197_var'))
merge.samples <- function(x, samples, new.sample, new.sample.group,
merge.samples <- function(x, samples, new.sample, new.sample.group, post.sample.col,
ref.cols=NULL, var.cols=NULL){
if (!all(samples %in% names(x$models))){
stop('ERROR: Sample not found when merging!')
Expand Down Expand Up @@ -3604,6 +3607,7 @@ merge.samples <- function(x, samples, new.sample, new.sample.group,

# update bootstrap samples for the merged sample
boot = generate.boot(x$variants, vaf.col.names=new.sample,
post.sample.col=post.sample.col,
vaf.in.percent=x$params$vaf.in.percent,
num.boots=x$params$num.boots,
bootstrap.model=x$params$bootstrap.model,
Expand Down
15 changes: 11 additions & 4 deletions R/generate.boot.r
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Expand Up @@ -37,6 +37,7 @@
generate.boot <- function(variants,
cluster.col.name='cluster',
cluster.center.method='mean',
post.sample.col=NULL,
vaf.col.names=NULL,
depth.col.names=NULL,
vaf.in.percent=TRUE,
Expand Down Expand Up @@ -209,8 +210,13 @@ generate.boot <- function(variants,

# generate bootstrap samples for each cluster, each sample
num.variants.per.cluster = table(variants[[cluster.col.name]])
#print(num.variants.per.cluster)


if (is.null(post.sample.col)) {
num.sample.per.cluster = num.variants.per.cluster
} else {
num.sample.per.cluster = table(variants[variants[[post.sample.col]] == FALSE,][[cluster.col.name]])
}

if(!is.null(random.seed)){
set.seed(random.seed)
}
Expand All @@ -226,6 +232,7 @@ generate.boot <- function(variants,

for (cl in clusters){
boot.size = num.variants.per.cluster[cl]
boot.sample.size = num.sample.per.cluster[cl]
vafs = v[[cl]][[vaf.col.name]]

# learn zero samples from data,
Expand Down Expand Up @@ -352,10 +359,10 @@ generate.boot <- function(variants,
# hdng: allow mean or median for non-parametric
# TODO: allow this for all other models
if (cluster.center.method == 'mean'){
s.mean = mean(sample(v[[cl]][[vaf.col.name]], boot.size,
s.mean = mean(sample(v[[cl]][[vaf.col.name]], boot.sample.size,
replace=TRUE, prob=depth))
}else if (cluster.center.method == 'median'){
s.mean = median(sample(v[[cl]][[vaf.col.name]], boot.size,
s.mean = median(sample(v[[cl]][[vaf.col.name]], boot.sample.size,
replace=TRUE, prob=depth))

}
Expand Down
1 change: 0 additions & 1 deletion simulations/sim.r
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Expand Up @@ -316,7 +316,6 @@ if (!is.na(rand.seed)){
set.seed(rand.seed)
}


# read ground truth CCF
x = read.table(subpop.file, header=TRUE, sep='\t', stringsAsFactors=FALSE,
na.strings='')
Expand Down