PyClone without copy number data

I want to use PyClone using Single Nucleotide Variants called from Whole-exome sequencing of matched primary and metastatic tissue samples.

I do not have copy number data, and the PyClone usage page says:

If you do not major and minor copy number information you should set the minor copy number to 0, and the major copy number to the predicted total copy number. If you do this make sure to use the total_copy_number for the --var_prior flag of the build_mutations_file command. DO NOT use the parental copy number information method as it assumes you have knowledge of the minor and major copy number.

I have two questions:

1) Can I predict total copy number with Whole-exome sequencing data?

2) If not, is it sensible to set the major copy number to 2?

pyclone next-gen tumor_phylogeny

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updated 10.7 years ago by fongchunchan •

• written 10.7 years ago by Alejandro Jimenez Sanchez

1 answer

Yes there are several tools out there that can do it (e.g. TitanCNA). If possible, get parental copy number as this greatly helps PyClone. Something like TitanCNA can do this. I would look at this thread.
If you believe that the tumour contains little to no somatic sCNAs, then you could do this. But if you have WES, you should estimate copy number and use that information.

At the very least, you could estimate copy number and only use diploid heterozygous positions. Then use this for PyClone. Or something like SciClone would work too if you go with this approach.

Hope that helps,

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updated 6.7 years ago by Ram

• written 10.7 years ago by fongchunchan •

Hi @fongchunchan. I'm trying to extract minor and mayor cn info from sciClone but I'm not able to differentiate that value correspond to each one because this R package only give me an unique value of cn not two, could you help me?

   chr        st 799_D.ref 799_D.var 799_D.vaf 799_D.cn 799_D.cleancn 799_D.depth 799_SP.ref 799_SP.var 799_SP.vaf 799_SP.cn 799_SP.cleancn
1    1 156263928        25        20 0.4444444     2.00             2        4500         18          9  0.3333333      2.00              2
2    1 214171091        31        41 0.5694444     2.00             2        7200         23         10  0.3030303      2.00              2
3   10  50957831        25        21 0.4565217     2.00             2        4600         10          5  0.3333333      2.00              2

Thanks in advance.

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written 7.1 years ago by garcesj •