Visualize metastatic propnesity odds ratio in a volcano plot fashion.

Visualize metastatic propnesity odds ratio in a volcano plot fashion.

Usage

plot_met_volcano(x, tumor_type)

Arguments

x

An object of class 'INCOMMON' containing the classification results, as produced by function classify.

tumor_type

The tumour type for which classified data is available. If 'PANCA' it pools from multiple tumour types into a pan-cancer visualisation.

Value

An object or a list of objects of class 'ggplot2'.

Examples

# First load example classified data
data(MSK_classified)
# Estimate the metastatic propensity associated with mutant TP53, PIK3CA and CDH1 with vs without CNA in BRCA.
for(g in c('TP53', 'PIK3CA', 'CDH1')){MSK_classified = met_propensity(x = MSK_classified, tumor_type = 'BRCA', gene = g)}
#> 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
#> 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 PIK3CA Mutant PIK3CA with AMP  2.12  1.48  3.05 0.0000449
#> 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 CDH1  Mutant CDH1 with LOH  2.45  1.19  5.17  0.0163
# Plot results in a volcano plot
plot_met_volcano(x = MSK_classified, tumor_type = 'BRCA')