Logistic regression of metastatic propensity based on INCOMMON classes.

Usage

met_propensity(x, gene, tumor_type)

Arguments

x: An object of class 'INCOMMON' containing the classification results as produced by function classify.
gene: The gene on which patient's stratification is based.
tumor_type: The tumor type of patients to stratify.

Value

An object of class 'INCOMMON' containing an additional object survival.

Examples

# First load example classified data
data(MSK_classified)
# Estimate the metastatic propensity associated with mutant TP53 with vs without CNA in BRCA.
MSK_classified = met_propensity(x = MSK_classified, tumor_type = 'BRCA', gene = 'TP53')
#> ℹ There are 20289 different genotypes
#> ℹ The most abundant genotypes are:
#> • Mutant TP53 with LOH (562 Samples, Frequency 0.02)
#> • Mutant KRAS without AMP (199 Samples, Frequency 0.01)
#> • Mutant KRAS without AMP,Mutant TP53 with LOH (149 Samples, Frequency 0.01)
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> # A tibble: 1 × 6
#>   gene  class                   OR   low    up p.value
#>   <chr> <chr>                <dbl> <dbl> <dbl>   <dbl>
#> 1 TP53  Mutant TP53 with LOH 0.514 0.145  1.43   0.242