CLONETv2 (version 2.2.1)

compute_scna_clonality_table: Function to compute clonality of somatic copy number data

Description

This function takes the beta table of a tumor sample together with the associated ploidy and admixtures tables and computes the clonality of each segment in the beta table.

Usage

compute_scna_clonality_table(beta_table, ploidy_table, admixture_table,
  error_tb = error_table, clonality_threshold = 0.85,
  beta_threshold = 0.9, n_digits = 3, n_cores = 1, debug = F)

Arguments

beta_table

data.frame formatted as the output of function compute_beta_table

ploidy_table

data.frame formatted as the output of function compute_ploidy

admixture_table

data.frame formatted as the output of function compute_dna_admixture

error_tb

data.frame that reports for each combination of coverage and number informative SNPs the expected estimation error around beta. The data.frame error_tb must contains 3 columns:

mean.cov

mean coverage

n.info.snps

number of informative SNPs

adm.estimation.error

estimated error on computed beta on a segment with coverage mean.cov and n.info.snps informative SNPs

Package CLONETv2 have built in error_tb named error_table (default=error_table)

clonality_threshold

threshold to discretize continuous clonality value (default=0.85)

beta_threshold

threshold on beta value to determine clonality direction (default=0.90)

n_digits

number of digits in the output table (default=3)

n_cores

number of cores (default=1)

debug

return extra columns for debugging (default=F)

Value

A data.frame that extends input beta_table with columns

clonality

estimated fraction of tumor cell with log2 copy number

clonality.min

minum estimated fraction of tumor cell with log2 copy number

clonality.max

minum estimated fraction of tumor cell with log2 copy number

clonality.status

discretized clonality status into five values: clonal, large majority of the tumor cells has the same copy number; subclonal, not all the tumor cells has the same copy number; not.analysed, is is not possible to determine clonality; uncertain.clonal and uncertain.subclonal correspond respectively to clonal and subclonal populations but with less reliable clonality estimate

Examples

# NOT RUN {
## Compute clonality table with default parameters
scna_clonality_table_toy <- compute_scna_clonality_table(beta_table = bt_toy,
  ploidy_table = pl_table_toy, admixture_table = adm_table_toy)
# }