TitanCNA-utils

TitanCNA utility scripts

Instructions for using titanCNA_v1.x.y.R script

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.

1. GC-corrected, normalized read coverage using the HMMcopy suite
2. 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

1. Clone the git repo and locate the folder containing the Rscript

git clone git@github.com:gavinha/TitanCNA-utils.git
cd TitanCNA-utils/titan_scripts/

2. 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)
      ...

3. 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)
```