Hi!

I have tried to test Clomial R package to infer the clonal structure of panel sequenced tumor samples but I am facing some problems when trying to run my samples with commands. I have tried to debug it but so far I have not been able to solve this. I believe that reason for this odd behavior is related to my low variant coverage (see example below). To confirm this, I am kindly requesting some technical support to help me overcome this problem. Package seems to be easy to use and I would be more than happy to utilize it in my research project.

> library(Clomial)
> set.seed(1)

> Dc = read.table(“sample_Dc.txt”, header=T, row.names=1)
> head(Dc)
                 sampleA   sampleB
chr1:27056288         86    415
chr1:27057762        507    611
chr1:27088785         97    277
chr2:178107590       174    673
chr2:198257616         9     55
chr2:198267216       174    289

> Dt = read.table(“sample_Dt.txt”, header=T, row.names=1)

> head(Dt)
                 sampleA   sampleB
chr1:27056288         12         0
chr1:27057762         45         0
chr1:27088785          6         0 
chr2:178107590        16         0
chr2:198257616         0         8
chr2:198267216        18         0


> clomialResults = Clomial(Dc=Dc, Dt=Dt, maxIt=4, C=4, binomTryNum=2)

Error in optim(par = Wj, fn = objective, gr = gradient, lower = lowers, :
      L-BFGS-B need finite values of ‘fn’
Error in optim(par = Wj, fn = objective, gr = gradient, lower = lowers, :
      L-BFGS-B need finite values of ‘fn’
Error in optim(par = Wj, fn = objective, gr = gradient, lower = lowers, :
      L-BFGS-B need finite values of ‘fn’
Error in optim(par = Wj, fn = objective, gr = gradient, lower = lowers, :
      L-BFGS-B need finite values of ‘fn’
Error in optim(par = Wj, fn = objective, gr = gradient, lower = lowers, :
      L-BFGS-B need finite values of ‘fn’
…

Warning messages;
1:  In log(row.z.likelihoods[i, state]) : NaNs produced
2:  In log(row.z.likelihoods[i, state]) : NaNs produced
3:  In log(row.z.likelihoods[i, state]) : NaNs produced
4:  In log(row.z.likelihoods[i, state]) : NaNs produced
5:  In log(row.z.likelihoods[i, state]) : NaNs produced

> systemInfo()

R version 3.2.2 (2015-08-14)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS release 6.9 (Final)

locale:
[1] LC_TYPE=en_US.UTF-8
[2] LC_NUMERIC=C
[3] LC_TIME=en_US:UTF-8
[4] LC_COLLATE=en_US:UTF-8
[5] LC_MONETARY=en_US:UTF-8
[6] LC_MESSAGES=en_US:UTF-8
[7] LC_PAPER=en_US:UTF-8
[8] LC_NAME=C
[9] LC_ADDRESS=C
[10] LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US:UTF-8
[12] LC_IDENTIFICATION=C

attached base packages:
[1] stats
[2] graphics
[3] qrDevices utils
[4] datasets
[5] methods
[6] base

other attached packages:
[1] Clomial_1.6.0       matrixStats_0.52.2

loaded via a namespace (and not attached):
[1] tools_3.2.2

Best regards,

Jouni

1- Two samples are not enough to infer the clonal composition.

2- There are 4 samples in the data you sent me by email. The mutation ratios seem to be very low (~0.01).

plot(rowSums(Dc), rowSums(Dt),xlim=c(0,1000), ylim=c(0,30))

Your data has 169 mutations, and with only 4 samples, it's not easy to fit the binomial model to the data. The system is underconditioned. I could run Clomial using 50 random mutations with no error:

ClomialRes50 <- Clomial(Dc=Dc[1:50,], Dt=Dt[1:50,], maxIt=4, C=4, binomTryNum=2)

I recommend increasing the number of samples if possible. If not, filter the mutations. Say, include those mutations with a frequency of at least 2% in at least one of the samples.