Compute model posterior and entropy
Usage
compute_posterior(
NV,
DP,
gene,
priors = NULL,
tumor_type,
purity,
entropy_cutoff,
rho = 0.01,
karyotypes
)Arguments
- NV
Number of reads with the variant.
- DP
Sequencing coverage of the mutated genome site.
- gene
Gene name or symbol.
- priors
Prior distribution.
- tumor_type
Tumor type of the sample.
- purity
Purity of the sample.
- entropy_cutoff
Cut-off on entropy for Tier-1/Tier-2 distinction.
- rho
The over-dispersion parameter.
- karyotypes
Karyotypes to be included among the possible classes.
Examples
compute_posterior(
NV = 170,
DP = 200,
gene = 'TP53',
priors = pcawg_priors,
tumor_type = 'PAAD',
purity = 0.9,
entropy_cutoff = 0.2,
rho = 0.01,
karyotypes = c("1:0", "1:1", "2:0", "2:1", "2:2")
)
#> # A tibble: 1,600 × 11
#> NV value Major minor ploidy multiplicity karyotype label peak entropy
#> <int> <dbl> <int> <int> <int> <int> <chr> <chr> <dbl> <dbl>
#> 1 1 3.45e-55 1 0 1 1 1:0 1N (… 0.818 1.42e-11
#> 2 1 1.02e-24 1 1 2 1 1:1 2N (… 0.45 1.42e-11
#> 3 1 1.02e-24 2 0 2 1 2:0 2N (… 0.45 1.42e-11
#> 4 1 3.16e-65 2 0 2 2 2:0 2N (… 0.9 1.42e-11
#> 5 1 2.79e-17 2 1 3 1 2:1 3N (… 0.310 1.42e-11
#> 6 1 4.68e-38 2 1 3 2 2:1 3N (… 0.621 1.42e-11
#> 7 1 3.44e-13 2 2 4 1 2:2 4N (… 0.237 1.42e-11
#> 8 1 4.00e-27 2 2 4 2 2:2 4N (… 0.474 1.42e-11
#> 9 2 1.30e-53 1 0 1 1 1:0 1N (… 0.818 1.17e-10
#> 10 2 1.84e-23 1 1 2 1 1:1 2N (… 0.45 1.17e-10
#> # ℹ 1,590 more rows
#> # ℹ 1 more variable: state <chr>