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Plot Histogram of Variant Allele Frequency (VAF)

Source: R/plot_vaf_histogram.R
plot_VAF_histogram.Rd

This function generates a histogram showing the distribution of Variant Allele Frequency (VAF) across samples and chromosomes.

Usage

plot_VAF_histogram(
  seq_result,
  chromosomes = NULL,
  samples = NULL,
  labels = NULL,
  binwidth = NULL,
  cuts = c(0, 1)
)

Arguments

seq_result

A data frame containing sequencing results.

chromosomes

A character vector specifying the chromosomes to include in the plot (default: all the chromosomes in seq_res).

samples

A character vector specifying the sample names to include in the plot. When set to NULL, the function includes all samples except the "normal_sample" (default: NULL).

binwidth

The width of the plot bins. When set to NULL, the function computes the most convinient bin width according to the maximum coverage reported in the dataframe (default: NULL).

cuts

A numeric vector specifying the range of VAF values to include in the plot (default: c(0, 1)).

colour_by

A character indicating whether to color the histogram bars by "causes" or "classes" (default: "causes").

Value

A ggplot2 object showing the VAF histogram.

See also

plot_VAF_marginals(), plot_VAF()

Examples

# set the seed of the random number generator
set.seed(0)

sim <- SpatialSimulation()
sim$add_mutant(name = "A",
               growth_rates = 0.1,
               death_rates = 0.0)
sim$place_cell("A", 500, 500)
sim$run_up_to_time(100)
#> 
 [████████████████████████████████████████] 100% [00m:00s] Saving snapshot                                        


# sampling tissue
n_w <- n_h <- 10
ncells <- 0.8 * n_w * n_h
bbox <- sim$search_sample(c("A" = ncells), n_w, n_h)
sim$sample_cells("SampleA", bbox$lower_corner, bbox$upper_corner)

# adding second mutant
sim$add_mutant(name = "B",
               growth_rates = 0.3,
               death_rates = 0.0)
sim$mutate_progeny(sim$choose_cell_in("A"), "B")
sim$run_up_to_time(300)
#> 
 [████████████████████████████████████████] 100% [00m:00s] Saving snapshot                                        


# sampling tissue again
bbox <- sim$search_sample(c("B" = ncells), n_w, n_h)
sim$sample_cells("SampleB", bbox$lower_corner, bbox$upper_corner)

forest <- sim$get_samples_forest()

# placing mutations
m_engine <- MutationEngine(setup_code = "demo")
#> Downloading reference genome...
#> Reference genome downloaded
#> Decompressing reference file...done
#> Downloading SBS file...
#> SBS file downloaded
#> Downloading indel file...
#> indel file downloaded
#> Downloading driver mutation file...
#> Driver mutation file downloaded
#> Downloading passenger CNAs file...
#> Passenger CNAs file downloaded
#> Downloading germline mutations...
#> Germline mutations downloaded
#> Building context index...
#> 
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 [█████████████████████████████████-------] 81% [00m:02s] Processing chr. 22                                      

 [████████████████████████████████████████] 100% [00m:02s] Context index built                                    

#> 
 [█---------------------------------------] 0% [00m:00s] Saving context index                                     

 [████████████████████████████████████████] 100% [00m:00s] Context index saved                                    

#> done
#> Building repeated sequence index...
#> 
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#> 
 [█---------------------------------------] 0% [00m:00s] Saving RS index                                          

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done
#> 
 [████████████████████████████████████████] 100% [00m:02s] RS index saved                                         

#> 
 [█---------------------------------------] 0% [00m:00s] Loading germline                                         

 [████████████████████████████████████████] 100% [00m:00s] Germline loaded                                        

#> 
 [█---------------------------------------] 0% [00m:00s] Saving germline                                          

 [████████████████████████████████████████] 100% [00m:00s] Germline saved                                         


m_engine$add_mutant(mutant_name="A", passenger_rates=c(SNV=5e-8))
#> 
 [█---------------------------------------] 0% [00m:00s] Retrieving "A" SNVs                                      

 [████████████████████████████████████████] 100% [00m:00s] "A" SNVs retrieved                                     

m_engine$add_mutant(mutant_name="B", passenger_rates=c(SNV=5e-9))
#> 
 [█---------------------------------------] 0% [00m:00s] Retrieving "B" SNVs                                      

 [████████████████████████████████████████] 100% [00m:00s] "B" SNVs retrieved                                     

m_engine$add_exposure(c(SBS1 = 0.2, SBS5 = 0.8))

phylo_forest <- m_engine$place_mutations(forest, 10, 10)
#> 
 [█---------------------------------------] 0% [00m:00s] Placing mutations                                        

 [████████████████████████████████████████] 100% [00m:00s] Mutations placed                                       


# simulating sequencing without the normal sample
seq_results <- simulate_seq(phylo_forest, coverage = 10, write_SAM = F,
                            with_normal_sample = FALSE)
#> 
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library(dplyr)

# filter germinal mutations
f_seq <- seq_results$mutations %>% dplyr::filter(classes!="germinal")

# plotting the VAF histogram
plot_VAF_histogram(f_seq, cuts = c(0.02, 1))


# plotting the VAF histogram with labels
plot_VAF_histogram(f_seq, labels = f_seq["causes"], cuts = c(0.02, 1))


# deleting the mutation engine directory
unlink('demo', recursive = T)