Closed jial2 closed 6 years ago
You should also check if Pyclone CCF is barred by 100% that could hamper clonal fraction estimates. See more here: https://github.com/hdng/clonevol/issues/4.
Just play with them until you get some good plot.
Thanks for the reply.
I am still confused on the tree.
clone1 in primary should be the founding clone, why clone 1 in the relapse was found clone? In primary, there are three clones with about 100% CCF in the cluster plot, i wonder if this is the reason the program doesn't pick clone 1 from primary as founding clone. Another confusion is that cluster 3 in primary shows 100% CCF, but it get to almost 0% in relapse, for this case, the clone 3 is clonal or subclonal?
I didn't plot the tree using plot.clonal.model because it is so crowd if there are more samples. I used plot.all.trees.clone.as.branch to generate the tree only, but I don't see branch.angle to adjust the angles between branches. Could you please point me to the parameter or function to do this?
Thanks
Sorry for late response.
Given that you have an evolution tree as: 1-->2-->3-->4, and with a single sample from primary tumor that only captured cells in clone 4. The result is that cluster 1,2,3,4 all have CCF of variants = 100%, but CCF of clones 1,2,3 = 0 while CCF of clone 4 = 100%. It is important to distinguish CCF of clones from CCF of variants when interpreting clonal evolution models and tumor heterogeneity. Our paper describes how CCF of clone and CCF of cluster of variants are related, see https://academic.oup.com/annonc/article/28/12/3076/4110375. The tree is annotated with clones where CCF of the clone (not the cluster) is non-zero.
You can try to provide/adjust the following parameters to plot.all.trees.clone.as.branch as:
plot.all.trees.clone.as.branch(..., angle=15,branch.width=1,
branch.text.size=0.3, node.size=3, node.label.size=0.75,
node.text.size=0.5)
or even better, I have created a wrapper for better plotting of the trees but haven't documented in the vignette. Given y is output of infer.clonal.models, you can try:
y = convert.consensus.tree.clone.to.branch(y, branch.scale='sqrt')
y = overlayPrunedTrees(y)
plotTreesBellsCells(y, trees.per.page=3, tree.branch.angle=30,
show.event=T, show.frame=F, tree.branch.width=0.75,
out.pdf.file='trees-fishes-cells.pdf')
Hi Ha,
I am running 3 samples on clonevol, they are primary, metastasis and relapse. In the analysis, considering of the copy number aberrations, we used pyclone to do cluster.
I have few questions on the plotted tree.
one question on clonal mutations. Based on the variant clusters on CCF, cluster 1,2 and 3 in primary shown almost 100% in CCF, but in 4191 (relapse), the mutations in cluster 2 and 3 showed almost 0. I just don't understand if we say the mutations in cluster 1,2,3 are clonal mutations, then CCF in cluster 2 and 3 in 4191 should not be zero or very low percentage. If we say the mutations in cluster 2 and 3 are not clonal mutations, the CCF should be as high as 1 in both clusters in primary. I also found you had the similar results in your paper.
Looking forward to hearing from you.
Thanks, Emily