hdng / clonevol

Inferring and visualizing clonal evolution in multi-sample cancer sequencing
GNU General Public License v3.0
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No consensus multi-sample models provided. #56

Open pushpa-itagi opened 2 years ago

pushpa-itagi commented 2 years ago

Hi, I am trying to find a consensus tree for a three sample patient data. It works for some patients, and I do get the individual sample clonal architecture models, but 0 consensus trees. This has 10 clusters (as Cyclone-VI gives) and founding cluster is 1. y = infer.clonal.models(variants = x, cluster.col.name = "cluster", ccf.col.names = ccf.col.names,

vaf.col.names = vaf.col.names,

sample.groups = sample.groups,

sample.groups = NULL, cancer.initiation.model='polyclonal', 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 = NULL,

min probability that CCF(clone) is non-negative

sum.p = 0.05,

alpha level in confidence interval estimate for CCF(clone)

alpha = 0.05) print("flow done")

Output y = infer.clonal.models(variants = x, cluster.col.name = "cluster", ccf.col.names = ccf.col.names,

vaf.col.names = vaf.col.names,

sample.groups = sample.groups,

sample.groups = NULL, cancer.initiation.model='polyclonal', 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 = NULL,

min probability that CCF(clone) is non-negative

sum.p = 0.05,

alpha level in confidence interval estimate for CCF(clone)

alpha = 0.05) print("flow done")