[Help..] Found 0 consensus model(s)

[biosurgeon]

Hi, Dang, H. X.,

I'm trying to use Clonevol, but I cannot get consensus model when I tried to run "_infer.clonal.model_s". In this example, I have 6 samples (3 primary, 3 metastasis), and I ran Pyclone and VAF was calculated as cellular_prevalence *100/2, as recommended by tutorial. I filtered out clones that had <5 variants, and I used below commend for infer.clonal.models. I reviewed every issues in here, but did not figure out why clonevol did not infer consensus models for this case. I tried tuning every possible options, but I failed.. To be honest, I tried some of other samples (5~8 samples per case), but I did not success to infer consensus clonal model. could you help to figure it out? I attached the data file, code I used, and results of it. Also variant_clone plot is attached.

Thank you,

Young

====code=======

y = infer.clonal.models(variants = clonevol_012,
                        cluster.col.name = 'cluster',
                        vaf.col.names = vaf.col.names, ##for CCF, ccf.col.names could be used
                        sample.groups = NULL,
                        cancer.initiation.model='monoclonal',  ##could select "polyclonal"
                        subclonal.test = 'bootstrap',
                        subclonal.test.model = 'non-parametric', ## could be "beta-binomial"non-parametric
                        num.boots = 1000,
                        founding.cluster = 1, ## clusters of variants that are found in all samples,  drivers/founder clusters
                        cluster.center = 'mean',
                        ignore.clusters = NULL,
                        clone.colors = clone.colors,
                        ##seeding.aware.tree.pruning = TRUE, merge.similar.samples = TRUE,
                        min.cluster.vaf = 0.01,
                        score.model.by="prabability",
                        # min probability that CCF(clone) is non-negative
                        sum.p = 0.01,
                        # alpha level in confidence interval estimate for CCF(clone)
                        alpha=0.01)

======results===

Sample 1: M <-- M
Sample 2: Sm <-- Sm
Sample 3: S <-- S
Sample 4: Ov1 <-- Ov1
Sample 5: Ov2 <-- Ov2
Sample 6: Per <-- Per
Using monoclonal model
Note: all VAFs were divided by 100 to convert from percentage to proportion.
Generating non-parametric boostrap samples...
M : Enumerating clonal architectures...
Determining if cluster VAF is significantly positive...
Exluding clusters whose VAF < min.cluster.vaf=0.01
Non-positive VAF clusters:  
M : 40 clonal architecture model(s) found

Sm : Enumerating clonal architectures...
Determining if cluster VAF is significantly positive...
Exluding clusters whose VAF < min.cluster.vaf=0.01
Non-positive VAF clusters:  
Sm : 32 clonal architecture model(s) found

S : Enumerating clonal architectures...
Determining if cluster VAF is significantly positive...
Exluding clusters whose VAF < min.cluster.vaf=0.01
Non-positive VAF clusters:  
S : 7 clonal architecture model(s) found

Ov1 : Enumerating clonal architectures...
Determining if cluster VAF is significantly positive...
Exluding clusters whose VAF < min.cluster.vaf=0.01
Non-positive VAF clusters: 3 
Ov1 : 3 clonal architecture model(s) found

Ov2 : Enumerating clonal architectures...
Determining if cluster VAF is significantly positive...
Exluding clusters whose VAF < min.cluster.vaf=0.01
Non-positive VAF clusters:  
Ov2 : 16 clonal architecture model(s) found

Per : Enumerating clonal architectures...
Determining if cluster VAF is significantly positive...
Exluding clusters whose VAF < min.cluster.vaf=0.01
Non-positive VAF clusters:  
Per : 3 clonal architecture model(s) found

Finding consensus models across samples...
Found  0 consensus model(s)
Found 0 consensus model(s)
Scoring models...
Pruning consensus clonal evolution trees....
Seeding aware pruning is:  off
Number of unique pruned consensus trees: 0 

GCMET_012_clonevol.txt 012.pdf

[jliu2019]

Hi,

I met the same problem. It seems the author didn't respond to these issues. Do you have any solution?

[biosurgeon]

No. I did not solve it....