hdng
commented
5 years ago min.cluster.vaf = 0.00 is the culprit. This param is to determine if a cluster of variants is present in a sample. Clusters with mean (or median) VAF < min.cluster.vaf in a sample will be excluded from the sample before inferring trees for that sample. This should be set at a value > 0, typically representing the error rate in sequencing, eg. 0.01.
Another issue is in sample TD30, cluster 2 have higher VAF compared with cluster 1 likely due to low number of variants (see the image). Setting sum.p = 0.01 allows for more relax error tolerance that yields one pruned consensus tree (see code).
pdf('box.pdf', width = 5, height = 8, useDingbats = FALSE, title='')
pp <- plot.variant.clusters(x,
cluster.col.name = 'cluster',
show.cluster.size = FALSE,
cluster.size.text.color = 'blue',
vaf.col.names = vaf.col.names,
vaf.limits = 70,
sample.title.size = 20,
violin = FALSE,
box = FALSE,
jitter = TRUE,
jitter.shape = 1,
jitter.color = clone.colors,
jitter.size = 1,
jitter.alpha = 1,
jitter.center.method = 'median',
jitter.center.size = 1,
jitter.center.color = 'darkgray',
jitter.center.display.value = 'none',
highlight = 'is.driver',
highlight.shape = 21,
highlight.color = 'blue',
highlight.fill.color = 'green',
highlight.note.col.name = 'gene',
highlight.note.size = 2,
order.by.total.vaf = FALSE)
dev.off()
y = infer.clonal.models(variants = x,
cluster.col.name = 'cluster',
vaf.col.names = vaf.col.names,
sample.groups = sample.groups,
cancer.initiation.model='monoclonal',
subclonal.test = 'bootstrap',
subclonal.test.model = 'non-parametric',
num.boots = 1000,
founding.cluster = 1,
cluster.center = 'mean',
ignore.clusters = NULL,
clone.colors = clone.colors,
min.cluster.vaf = 0.01,
# min probability that CCF(clone) is non-negative
sum.p = 0.01,
# alpha level in confidence interval estimate for CCF(clone)
alpha = 0.05)
Hello,
I got some problems when running clonevol with 6 samples which have been divided into 11 clusters. It dose not report any errors but keep stayings in the state as follows more than 24h. I doubt weather there exits a limit to the number of clusters for clonevol.
There's no any progress but the following information:
There were 14 warnings (use warnings() to see them) null device 1 There were 14 warnings (use warnings() to see them) null device 1 null device 1 Sample 1: TD25 <-- TD25 Sample 2: TD26 <-- TD26 Sample 3: TD27 <-- TD27 Sample 4: TD28 <-- TD28 Sample 5: TD29 <-- TD29 Sample 6: TD30 <-- TD30 Using monoclonal model Note: all VAFs were divided by 100 to convert from percentage to proportion. Generating non-parametric boostrap samples... TD25 : Enumerating clonal architectures... Determining if cluster VAF is significantly positive... Exluding clusters whose VAF < min.cluster.vaf=0 Non-positive VAF clusters
The code and input file are as follows:
x=read.table("/data/quantumclone/sci_clonevol_140_0123_no123.tsv", header=TRUE, sep="\t") library(clonevol)
preparation
shorten vaf column names as they will be
vaf.col.names <- grep('.vaf', colnames(x), value=TRUE) sample.names <- gsub('.vaf', '', vaf.col.names) x[, sample.names] <- x[, vaf.col.names] vaf.col.names <- sample.names
prepare sample grouping
sample.groups <- c('1', '2', '3', '4', '5', '6'); names(sample.groups) <- vaf.col.names
setup the order of clusters to display in various plots (later)
x <- x[order(x$cluster),] clone.colors <- c('#999793', '#8d4891', '#f8e356', '#fe9536', '#d7352e', "#FF3030", '#FFE4E1', '#2F4F4F', '#191970', '#6495ED', '#7FFF00' )
clone.colors <- NULL
pdf('box.pdf', width = 3, height = 3, useDingbats = FALSE, title='') pp <- plot.variant.clusters(x, cluster.col.name = 'cluster', show.cluster.size = FALSE, cluster.size.text.color = 'blue', vaf.col.names = vaf.col.names, vaf.limits = 70, sample.title.size = 20, violin = FALSE, box = FALSE, jitter = TRUE, jitter.shape = 1, jitter.color = clone.colors, jitter.size = 3, jitter.alpha = 1, jitter.center.method = 'median', jitter.center.size = 1, jitter.center.color = 'darkgray', jitter.center.display.value = 'none', highlight = 'is.driver', highlight.shape = 21, highlight.color = 'blue', highlight.fill.color = 'green', highlight.note.col.name = 'gene', highlight.note.size = 2, order.by.total.vaf = FALSE) dev.off()
plot clusters pairwise-ly
plot.pairwise(x, col.names = vaf.col.names, out.prefix = 'variants.pairwise.plot', colors = clone.colors)
plot mean/median of clusters across samples (cluster flow)
pdf('flow.pdf', width=3, height=3, useDingbats=FALSE, title='') plot.cluster.flow(x, vaf.col.names = vaf.col.names, sample.names = c('1', '2', '3', '4', '5', '6'), colors = clone.colors) dev.off()
infer consensus clonal evolution trees
y = infer.clonal.models(variants = x, cluster.col.name = 'cluster', vaf.col.names = vaf.col.names, sample.groups = sample.groups, cancer.initiation.model='monoclonal', subclonal.test = 'bootstrap', subclonal.test.model = 'non-parametric', num.boots = 1000, founding.cluster = 1, cluster.center = 'mean', ignore.clusters = NULL, clone.colors = clone.colors, min.cluster.vaf = 0.00,
min probability that CCF(clone) is non-negative
map driver events onto the trees
y <- transfer.events.to.consensus.trees(y, x[x$is.driver,], cluster.col.name = 'cluster', event.col.name = 'gene')
prepare branch-based trees
y <- convert.consensus.tree.clone.to.branch(y, branch.scale = 'sqrt')
plot variant clusters, bell plots, cell populations, and trees
plot.clonal.models(y,
box plot parameters
plot trees only
pdf('trees.pdf', width = 3, height = 5, useDingbats = FALSE) plot.all.trees.clone.as.branch(y, branch.width = 0.5, node.size = 1, node.label.size = 0.5) dev.off()
Inputfiles: sci_clonevol_140_0123_no123.txt
I look forward to your help!