Closed gunjangala closed 6 years ago
Your clustering looks untypical. Looking at it, I couldn't figure out which clone should be the founding clone (assuming monoclonal cancer initiation). The founding clone typically has many variants and is present in all samples with "highest" VAF. Your samples also seem to have low purity (VAF is almost zero in G and very low in D). Could you provide more detail on the samples, data types, and how the variants were processed prior to running clonevol?
See: box.pdf
Sorry for late reply. The clustering was done using kmeans algorithm. We just have allele frequencies for D, B and R. G was the initial stage and that is the reason we consider the frequency as zero.
Without the details of the data/samples, I can only give generic advice. Not sure if you filtered variants prior to clustering, but if you did, I would try relaxing the filter to get more variants, and plot their VAFs (without clustering) to see their range and distribution. If most variants have low VAFs, either all of your samples have low purity (which is a barrier to this analysis) or there are only a few founding mutations (which will require careful clustering to reveal). It is also worth to try SciClone and Pyclone which are better tools for variant clustering, compared to kmeans.
Hello, I get the following error when running infer.clonal.models(). Can you please help me fix this? I have attached the input data.txt file for your reference.
x <- data.txt read here
Thanks, Gunjan