TitanCNA utility scripts
Description
R script will run the R component of the TITAN analysis using the TitanCNA R/Bioconductor package.
Input files
This script assumes that the necessary input files have been generated.
- GC-corrected, normalized read coverage using the HMMcopy suite
- Tumour allelic read counts at heterozygous SNPs (identifed from the normal sample).
The easiest way to generate these files is by using the downloadable pipeline KRONOS.
Running the R script
- Clone the git repo and locate the folder containing the Rscript
git clone [email protected]:gavinha/TitanCNA-utils.git
cd TitanCNA-utils/titan_scripts/
- Look at the usage of the R script
# from the command line
> Rscript titanCNA_v1.10.1.R --help
Usage: Rscript titanCNA_v1.10.1.R [options]
Options:
--id=ID
Sample ID
--hetFile=HETFILE
File containing allelic read counts at HET sites. (Required)
--cnFile=CNFILE
File containing normalized coverage as log2 ratios. (Required)
--outDir=OUTDIR
Output directory to output the results. (Required)
--numClusters=NUMCLUSTERS
Number of clonal clusters. (Default: 1)
--numCores=NUMCORES
Number of cores to use. (Default: 1)
--ploidy_0=PLOIDY_0
Initial ploidy value; float (Default: 2)
--estimatePloidy=ESTIMATEPLOIDY
Estimate ploidy; TRUE or FALSE (Default: TRUE)
--normal_0=NORMAL_0
Initial normal contamination (1-purity); float (Default: 0.5)
--estimateNormal=ESTIMATENORMAL
Estimate normal contamination method; string {'map', 'fixed'} (Default: map)
--maxCN=MAXCN
Maximum number of copies to model; integer (Default: 8)
...
- Example usage of R script
# normalized coverage file: test.cn.txt
# allelic read count file: test.het.txt
Rscript titanCNA_v1.10.1.R --id test --hetFile test.het.txt --cnFile test.cn.txt \
--numClusters 1 --numCores 1 --normal_0 0.5 --ploidy_0 2 \
--chrs "c(1:22, \"X\")" --estimatePloidy TRUE --outDir ./
Additional arguments to consider are the following:
These arguments can be used to tune the model based on variance in the read coverage data and data-type (whole-exome sequencing or whole-genome sequencing).
```
--alphaK=ALPHAK
Hyperparameter on Gaussian variance; for WES, use 1000; for WGS, use 10000;
float (Default: 10000)
--alphaKHigh=ALPHAKHIGH
Hyperparameter on Gaussian variance for extreme copy number states;
for WES, use 1000; for WGS, use 10000; float (Default: 10000)
```