A four-mutants example • ProCESS

Disclaimer: RACES/ProCESS internally implements the probability distributions using the C++11 random number distribution classes. The standard does not specify their algorithms, and the class implementations are left free for the compiler. Thus, the simulation output depends on the compiler used to compile RACES, and because of that, the results reported in this article may differ from those obtained by the reader.

We build a tumour with a single mutant “A” and grow it up to 10 cells.

library(ProCESS)

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

sim <- TissueSimulation(width = 600, height = 600)

# set the delta time between two time series samples
sim$history_delta <- 10

# avoid drift
sim$death_activation_level <- 50

sim$add_mutant(name = "A", growth_rates = 0.12, death_rates = 0.05)

sim$place_cell("A", 300, 300)
sim$run_up_to_size("A", 10)
#>  [████████████████████████████████████████] 100% [00m:00s] Saving snapshot

We add a new mutant “B” as sub-clone of “A” and let the simulation evolve until “B” reaches 30 cells.

sim$add_mutant(name = "B", growth_rates = 0.145, death_rates = 0.06)
sim$mutate_progeny(sim$choose_cell_in("A"), "B")

sim$run_up_to_size("B", 30)
#>  [████████████████████████████████████████] 100% [00m:00s] Saving snapshot

We add a further mutants “C” as sub-clones for “A” and let the tumour grow again up to the point in which “C” consists of 25000 cells.

sim$add_mutant(name = "C", growth_rates = 0.15, death_rates = 0.06)
sim$mutate_progeny(sim$choose_cell_in("A"), "C")

sim$run_up_to_size("C", 25000)
#>  [█████████-------------------------------] 22% [00m:00s] Cells: 19851 [███████████████-------------------------] 35% [00m:00s] Cells: 29278 [███████████████████---------------------] 46% [00m:01s] Cells: 37438 [███████████████████████-----------------] 55% [00m:03s] Cells: 43555 [█████████████████████████---------------] 62% [00m:04s] Cells: 48117 [████████████████████████████------------] 69% [00m:05s] Cells: 52552 [███████████████████████████████---------] 77% [00m:06s] Cells: 58075 [██████████████████████████████████------] 84% [00m:07s] Cells: 62557 [█████████████████████████████████████---] 92% [00m:08s] Cells: 67351 [████████████████████████████████████████] 98% [00m:09s] Cells: 71564 [████████████████████████████████████████] 100% [00m:09s] Saving snapshot

Then, we plot the tissue and the simulation state.

plot_tissue(sim)

The tissue of a tumour having three mutants: A, B, and C. Both B, and C are sub-clones of A. The three mutants have different grow rates and appears at different simulated time.

plot_state(sim)

The current ratio of cells per species over all tumour cells.

Tissue sampling level

We can collect three samples: “S1” and “S2”.

sim$sample_cells("S1", c(145, 230), c(215, 300))
sim$sample_cells("S2", c(350, 300), c(420, 370))

We can plot the tissue simulation after the sampling. We label the sampled region to improve readability, but it is not mandatory.

plot_tissue(sim) +
  ggplot2::annotate("text", x = (145 + 215) / 2, y = (230 + 300) / 2,
                    label = "S1", parse = TRUE) +
  ggplot2::annotate("text", x = (350 + 420) / 2, y = (300 + 370) / 2,
                    label = "S2", parse = TRUE)

The three-mutants tumour just after collecting two samples: S1 and S2. The former mainly contains C cells; the latter A cells.

After the sampling, we can add a new mutant “D” as sub-clone of “B”, let the simulation further evolve until the sum of the cardinalities of the mutants “C” and “D” is 100000, and sample the tissue again.

sim$add_mutant(name = "D", growth_rates = 0.8, death_rates = 0.01)
sim$mutate_progeny(sim$choose_cell_in("B", c(240, 130), c(250, 150)), "D")

sim$run_until(sim$var("C") + sim$var("D") == 100000)
#>  [██--------------------------------------] 3% [00m:00s] Cells: 68664 [███-------------------------------------] 6% [00m:00s] Cells: 74402 [█████-----------------------------------] 10% [00m:01s] Cells: 80338 [██████----------------------------------] 14% [00m:02s] Cells: 85936 [███████---------------------------------] 17% [00m:03s] Cells: 89719 [██████████------------------------------] 23% [00m:04s] Cells: 96457 [████████████----------------------------] 28% [00m:05s] Cells: 102464 [██████████████--------------------------] 34% [00m:06s] Cells: 108389 [████████████████------------------------] 39% [00m:07s] Cells: 113472 [██████████████████----------------------] 44% [00m:08s] Cells: 118874 [█████████████████████-------------------] 51% [00m:09s] Cells: 125151 [███████████████████████-----------------] 56% [00m:10s] Cells: 130617 [█████████████████████████---------------] 62% [00m:11s] Cells: 136192 [████████████████████████████------------] 68% [00m:12s] Cells: 141103 [██████████████████████████████----------] 73% [00m:13s] Cells: 146172 [████████████████████████████████--------] 79% [00m:14s] Cells: 151374 [██████████████████████████████████------] 84% [00m:15s] Cells: 156101 [████████████████████████████████████----] 89% [00m:16s] Cells: 160541 [██████████████████████████████████████--] 94% [00m:17s] Cells: 164922 [████████████████████████████████████████] 99% [00m:18s] Cells: 169465 [████████████████████████████████████████] 100% [00m:19s] Saving snapshot

sim$sample_cells("S3", c(360, 80), c(430, 150))

plot_tissue(sim) +
  ggplot2::annotate("text", x = (360 + 430) / 2, y = (80 + 150) / 2,
                    label = "S3", parse = TRUE)

After collecting the samples S1 and S2, a more aggressive mutant D arises from B and a further sample S3, containing both D and C cells, is collected.


plot_state(sim)

The simulation state after collecting S3.

The time-series plot represents the species cardinalities along the simulation.

plot_timeseries(sim)
#> Ignoring unknown labels:
#> • alpha : "Epistate"

The time series plot represent the species cardinalities along the simulation.

The Muller plot, instead, overviews the percentage of cells in each species along the simulation evolution.

plot_muller(sim)

The Muller plot overviews the percentage of cells in each species along the simulation evolution.

We can build the sample forest and plot it.

sample_forest <- sim$get_sample_forest()

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

plot_forest(sample_forest) %>%
  annotate_forest(sample_forest)

The sample forest of the collected samples.

Cell genetic characterisation level

We first build the mutation engine to label each cell in the sample forest. The mutation engine pre-defined setups automatically download all the needed file. All the pre-configured setups, but “demo” requires a COSMIC account to download the signature files from the COSMIC site.

# build the mutation engine according to the pre-defined setup "demo"
m_engine <- MutationEngine(setup_code="demo",
                           COSMIC_account = list(email = "foo@bar.com",
                                                 password = "#########"))
#>  [█---------------------------------------] 0% [00m:00s] Loading context index [████████████████████████████████████████] 100% [00m:00s] Context index loaded
#>  [█---------------------------------------] 0% [00m:00s] Loading RS index [██████████████████----------------------] 44% [00m:01s] Loading RS index [█████████████████████████████-----------] 72% [00m:02s] Loading RS index [████████████████████████████████████████] 100% [00m:02s] RS index loaded
#>  [█---------------------------------------] 0% [00m:00s] Loading germline [████████████████████████████████████████] 100% [00m:00s] Germline loaded

We must genomically characterise the tissue simulation mutants providing passenger mutation rates and the list of driver mutations.

# genetically characterise the mutants
m_engine$add_mutant("A", passenger_rates = c(SNV = 2e-9, indel = 2e-9),
                    drivers = list(Mutation("22", 16085675, "GCCTCCCGA", "G"),
                                   CNA("D", "22", 22453799, 200000,
                                       allele = 1)))
#>  [█---------------------------------------] 0% [00m:00s] Retrieving "A" SIDs [█---------------------------------------] 0% [00m:00s] Found 22 [█---------------------------------------] 0% [00m:00s] Reading 22 [████████--------------------------------] 19% [00m:01s] Reading 22 [████████████████------------------------] 38% [00m:02s] Reading 22 [███████████████████████-----------------] 57% [00m:03s] Reading 22 [███████████████████████████████---------] 76% [00m:04s] Reading 22 [███████████████████████████████████████-] 95% [00m:05s] Reading 22 [████████████████████████████████████████] 100% [00m:05s] "A"'s SIDs validated

m_engine$add_mutant("B", passenger_rates = c(SNV = 1e-9, indel = 2e-9,
                                             CNA = 1e-9),
                    drivers = list(SNV("22", 16050185, "C", allele = 1),
                                   CNA(type = "A", chr = "22",
                                       chr_pos = 16485130, len = 200000)))
#>  [█---------------------------------------] 0% [00m:00s] Retrieving "B" SIDs [████████████████████████████████████████] 100% [00m:00s] "B"'s SIDs validated

m_engine$add_mutant("C", passenger_rates = list(SNV = 2e-8, indel = 2e-9),
                    drivers = list(SNV("22", 32786322, "G"),
                                   list("DGCR8 P26L", allele = 1)))
#>  [█---------------------------------------] 0% [00m:00s] Retrieving "C" SIDs [████████████████████████████████████████] 100% [00m:00s] "C"'s SIDs validated

m_engine$add_mutant("D", passenger_rates = list(SNV = 2e-9, indel = 2e-9),
                    drivers = list(SNV("22", 51240420, "T")))
#>  [█---------------------------------------] 0% [00m:00s] Retrieving "D" SIDs [████████████████████████████████████████] 100% [00m:00s] "D"'s SIDs validated

We also need to declare the exposures along the simulation.

# add SNV and indel default exposures. This will be used from simulated time 0
# up to the successive exposure change.
m_engine$add_exposure(coefficients = c(SBS13 = 0.2, SBS1 = 0.8))
m_engine$add_exposure(c(ID2 = 0.6, ID13 = 0.2, ID21 = 0.2))

# add a new SNV exposure that will be used from simulated
# time 100 up to the successive exposure change.
m_engine$add_exposure(time = 100, c(SBS5 = 0.3, SBS2 = 0.2, SBS3 = 0.5))
m_engine$add_exposure(time = 120, c(SBS5 = 0.3, SBS2 = 0.2, SBS3 = 0.5,
                                    ID1 = 0.8, ID9 = 0.2))

m_engine
#> MutationEngine
#>  Passenger rates
#>    "A": {SNV: 2e-09, indel: 2e-09}
#>    "B": {SNV: 1e-09, indel: 2e-09, CNA: 1e-09}
#>    "C": {SNV: 2e-08, indel: 2e-09}
#>    "D": {SNV: 2e-09, indel: 2e-09}
#> 
#>  Driver mutations
#>    "A":
#>         (chr22(16085675)[GCCTCCCGA>G]) on random allele
#>        CNA("D",chr22(22453799), len: 200000, allele: 1)
#>    "B":
#>         (chr22(16050185)[A>C]) on allele 1
#>        CNA("A",chr22(16485130), len: 200000)
#>    "C":
#>         (chr22(32786322)[T>G]) on random allele
#>        DGCR8 P26L (chr22(20073563)[C>T]) on allele 1
#>    "D":
#>         (chr22(51240420)[G>T]) on random allele
#> 
#>  Timed Exposure
#>    SBS Timed Exposures
#>      [0, 100[: {"SBS1": 0.8, "SBS13": 0.2}
#>      [100, 120[: {"SBS2": 0.2, "SBS3": 0.5, "SBS5": 0.3}
#>      [120, ∞[: {"SBS2": 0.2, "SBS3": 0.5, "SBS5": 0.3}
#> 
#>    indel Timed Exposures
#>      [0, 120[: {"ID13": 0.2, "ID2": 0.6, "ID21": 0.2}
#>      [120, ∞[: {"ID1": 0.8, "ID9": 0.2}

We are now ready to build the phylogenetic forest.

# place mutations on the sample forest assuming 1000 pre-neoplastic SNVs and
# 500 indels
phylo_forest <- m_engine$place_mutations(sample_forest, 1000, 500)
#>  [█---------------------------------------] 0% [00m:00s] Placing mutations [████████████████------------------------] 39% [00m:01s] Placing mutations [█████████████████████████████-----------] 72% [00m:02s] Placing mutations [███████████████████████████████████-----] 85% [00m:03s] Placing mutations [███████████████████████████████████████-] 97% [00m:04s] Placing mutations [████████████████████████████████████████] 100% [00m:04s] Mutations placed

The phylogenetic forest maintains the genome mutations (SBSs, indels, and CNAs) of all the sampled cells.

mutations <- phylo_forest$get_sampled_cell_mutations()

head(mutations)
#>   cell_id chr  chr_pos allele ref alt  type cause          class
#> 1  683005  22 16080424      0 GTA   G indel   ID1 pre-neoplastic
#> 2  683005  22 16129596      0   T   A   SNV  SBS1 pre-neoplastic
#> 3  683005  22 16130490      0   G   A   SNV  SBS1 pre-neoplastic
#> 4  683005  22 16131174      0   A   G   SNV  SBS1 pre-neoplastic
#> 5  683005  22 16156621      0   T  TG indel   ID1 pre-neoplastic
#> 6  683005  22 16192291      0  TC   T indel   ID1 pre-neoplastic

CNAs <- phylo_forest$get_sampled_cell_CNAs()

head(CNAs)
#>   cell_id type chr    begin      end allele src.allele  class
#> 1  683005    D  22 22453799 22653798      1         NA driver
#> 2  695907    D  22 22453799 22653798      1         NA driver
#> 3  695922    D  22 22453799 22653798      1         NA driver
#> 4  717557    D  22 22453799 22653798      1         NA driver
#> 5  724240    D  22 22453799 22653798      1         NA driver
#> 6  724361    D  22 22453799 22653798      1         NA driver

Sequencing level

We can simulate the sequencing of the collected samples by using the phylogenetic forest. For each SBS and indel mutation in the phylogenetic forest, the output reports the number of occurrences in the simulated reads, the coverage of each mutation locus, and the corresponding VAF in each of the samples.

seq_results <- simulate_seq(phylo_forest, coverage = 30,
                            with_normal_sample = FALSE)
#>  [█---------------------------------------] 0% [00m:00s]  [█---------------------------------------] 0% [00m:00s] Processing chr. 22 [█---------------------------------------] 0% [00m:04s] Processing chr. 22 [█---------------------------------------] 0% [00m:05s] Processing chr. 22 [█---------------------------------------] 1% [00m:06s] Processing chr. 22 [█---------------------------------------] 1% [00m:07s] Processing chr. 22 [█---------------------------------------] 1% [00m:08s] Processing chr. 22 [█---------------------------------------] 2% [00m:09s] Processing chr. 22 [█---------------------------------------] 2% [00m:10s] Processing chr. 22 [██--------------------------------------] 3% [00m:11s] Processing chr. 22 [██--------------------------------------] 3% [00m:12s] Processing chr. 22 [██--------------------------------------] 4% [00m:13s] Processing chr. 22 [███-------------------------------------] 5% [00m:14s] Processing chr. 22 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[███████████████████████-----------------] 57% [01m:59s] Processing chr. 22 [███████████████████████-----------------] 57% [02m:00s] Processing chr. 22 [████████████████████████----------------] 58% [02m:01s] Processing chr. 22 [████████████████████████----------------] 58% [02m:02s] Processing chr. 22 [████████████████████████----------------] 58% [02m:03s] Processing chr. 22 [████████████████████████----------------] 58% [02m:04s] Processing chr. 22 [█████████████████████████---------------] 60% [02m:05s] Processing chr. 22 [█████████████████████████---------------] 60% [02m:06s] Processing chr. 22 [█████████████████████████---------------] 61% [02m:07s] Processing chr. 22 [█████████████████████████---------------] 61% [02m:08s] Processing chr. 22 [█████████████████████████---------------] 62% [02m:09s] Processing chr. 22 [█████████████████████████---------------] 62% [02m:10s] Processing chr. 22 [██████████████████████████--------------] 63% [02m:11s] Processing chr. 22 [██████████████████████████--------------] 63% [02m:12s] Processing chr. 22 [██████████████████████████--------------] 64% [02m:13s] Processing chr. 22 [██████████████████████████--------------] 64% [02m:14s] Processing chr. 22 [███████████████████████████-------------] 65% [02m:15s] Processing chr. 22 [███████████████████████████-------------] 65% [02m:16s] Processing chr. 22 [███████████████████████████-------------] 66% [02m:17s] Processing chr. 22 [███████████████████████████-------------] 66% [02m:18s] Processing chr. 22 [███████████████████████████-------------] 67% [02m:19s] Processing chr. 22 [███████████████████████████-------------] 67% [02m:20s] Processing chr. 22 [████████████████████████████------------] 68% [02m:21s] Processing chr. 22 [████████████████████████████------------] 68% [02m:22s] Processing chr. 22 [████████████████████████████------------] 69% [02m:23s] Processing chr. 22 [████████████████████████████------------] 69% [02m:24s] Processing chr. 22 [█████████████████████████████-----------] 70% [02m:25s] Processing chr. 22 [█████████████████████████████-----------] 70% [02m:26s] Processing chr. 22 [█████████████████████████████-----------] 71% [02m:27s] Processing chr. 22 [█████████████████████████████-----------] 71% [02m:28s] Processing chr. 22 [█████████████████████████████-----------] 72% [02m:29s] Processing chr. 22 [█████████████████████████████-----------] 72% [02m:30s] Processing chr. 22 [██████████████████████████████----------] 73% [02m:31s] Processing chr. 22 [██████████████████████████████----------] 73% [02m:32s] Processing chr. 22 [██████████████████████████████----------] 74% [02m:33s] Processing chr. 22 [██████████████████████████████----------] 74% [02m:34s] Processing chr. 22 [███████████████████████████████---------] 75% [02m:35s] Processing chr. 22 [███████████████████████████████---------] 75% [02m:36s] Processing chr. 22 [███████████████████████████████---------] 76% [02m:37s] Processing chr. 22 [███████████████████████████████---------] 76% [02m:38s] Processing chr. 22 [███████████████████████████████---------] 77% [02m:39s] Processing chr. 22 [███████████████████████████████---------] 77% [02m:40s] Processing chr. 22 [████████████████████████████████--------] 78% [02m:41s] Processing chr. 22 [████████████████████████████████--------] 78% [02m:42s] Processing chr. 22 [████████████████████████████████--------] 79% [02m:43s] Processing chr. 22 [████████████████████████████████--------] 79% [02m:44s] Processing chr. 22 [█████████████████████████████████-------] 80% [02m:45s] Processing chr. 22 [█████████████████████████████████-------] 80% [02m:46s] Processing chr. 22 [█████████████████████████████████-------] 81% [02m:47s] Processing chr. 22 [█████████████████████████████████-------] 81% [02m:48s] Processing chr. 22 [█████████████████████████████████-------] 82% [02m:49s] Processing chr. 22 [█████████████████████████████████-------] 82% [02m:50s] Processing chr. 22 [██████████████████████████████████------] 83% [02m:51s] Processing chr. 22 [██████████████████████████████████------] 83% [02m:52s] Processing chr. 22 [██████████████████████████████████------] 84% [02m:53s] Processing chr. 22 [██████████████████████████████████------] 84% [02m:54s] Processing chr. 22 [███████████████████████████████████-----] 85% [02m:55s] Processing chr. 22 [███████████████████████████████████-----] 85% [02m:56s] Processing chr. 22 [███████████████████████████████████-----] 86% [02m:57s] Processing chr. 22 [███████████████████████████████████-----] 86% [02m:58s] Processing chr. 22 [███████████████████████████████████-----] 87% [02m:59s] Processing chr. 22 [███████████████████████████████████-----] 87% [03m:00s] Processing chr. 22 [████████████████████████████████████----] 88% [03m:01s] Processing chr. 22 [████████████████████████████████████----] 88% [03m:02s] Processing chr. 22 [████████████████████████████████████----] 89% [03m:03s] Processing chr. 22 [████████████████████████████████████----] 89% [03m:04s] Processing chr. 22 [█████████████████████████████████████---] 90% [03m:05s] Processing chr. 22 [█████████████████████████████████████---] 90% [03m:06s] Processing chr. 22 [█████████████████████████████████████---] 91% [03m:07s] Processing chr. 22 [█████████████████████████████████████---] 91% [03m:08s] Processing chr. 22 [█████████████████████████████████████---] 92% [03m:09s] Processing chr. 22 [█████████████████████████████████████---] 92% [03m:10s] Processing chr. 22 [██████████████████████████████████████--] 93% [03m:11s] Processing chr. 22 [██████████████████████████████████████--] 93% [03m:12s] Processing chr. 22 [██████████████████████████████████████--] 94% [03m:13s] Processing chr. 22 [██████████████████████████████████████--] 94% [03m:14s] Processing chr. 22 [███████████████████████████████████████-] 95% [03m:15s] Processing chr. 22 [███████████████████████████████████████-] 95% [03m:16s] Processing chr. 22 [███████████████████████████████████████-] 96% [03m:17s] Processing chr. 22 [███████████████████████████████████████-] 96% [03m:18s] Processing chr. 22 [███████████████████████████████████████-] 97% [03m:19s] Processing chr. 22 [███████████████████████████████████████-] 97% [03m:20s] Processing chr. 22 [████████████████████████████████████████] 98% [03m:21s] Processing chr. 22 [████████████████████████████████████████] 98% [03m:22s] Processing chr. 22 [████████████████████████████████████████] 99% [03m:23s] Processing chr. 22 [████████████████████████████████████████] 99% [03m:24s] Processing chr. 22 [████████████████████████████████████████] 100% [03m:26s] Reads simulated

head(seq_results$mutations)
#>   chr  chr_pos ref   alt causes  classes S1.occurrences S1.coverage    S1.VAF
#> 1  22 16050185   A     C      B   driver              0          26 0.0000000
#> 2  22 16052080   G     A        germinal             13          33 0.3939394
#> 3  22 16052167   A AAAAC        germinal             12          30 0.4000000
#> 4  22 16052986   C     A        germinal             20          40 0.5000000
#> 5  22 16053444   A     T        germinal             18          30 0.6000000
#> 6  22 16053659   A     C        germinal             35          35 1.0000000
#>   S2.occurrences S2.coverage    S2.VAF S3.occurrences S3.coverage    S3.VAF
#> 1              0          30 0.0000000             26          42 0.6190476
#> 2             25          37 0.6756757             24          38 0.6315789
#> 3             27          43 0.6279070             11          28 0.3928571
#> 4             11          27 0.4074074             16          33 0.4848485
#> 5             16          26 0.6153846             19          33 0.5757576
#> 6             32          32 1.0000000             24          24 1.0000000

ProCESS allows to visualise the sequencing results.

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

plot_VAF_histogram(f_seq, labels = f_seq["classes"], cuts = c(0.02, 1))
#> Ignoring unknown labels:
#> • colour : "c(\"passenger\", \"passenger\", \"pre-neoplastic\", \"driver\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"passenger\", \"passenger\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"driver\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"passenger\", \"passenger\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"passenger\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"passenger\", \"passenger\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\",
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#>   \"pre-neoplastic\", \"passenger\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\",
#>   \"pre-neoplastic\", \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"passenger\", \"pre-neoplastic\", \"pre-neoplastic\", \"pre-neoplastic\",
#>   \"driver\")"

The VAF histogram labelled by mutation class.

Marginal distributions can also be plotted.

plot_VAF_marginals(f_seq, samples = c("S1", "S2", "S3"),
                   labels = f_seq["classes"])
#> [[1]]

The VAF marginal distribution S1 vs S2 labelled by mutation class.

#> 
#> [[2]]

The VAF marginal distribution S1 vs S3 labelled by mutation class.

#> 
#> [[3]]

The VAF marginal distribution S2 vs S3 labelled by mutation class.

In S1 vs S3 figure, we can some spot passenger mutations occurring in both S1 and S3. Let us identify these mutations.

f_seq %>% filter(classes == "passenger" & S1.VAF > 0 & S3.VAF > 0)
#>   chr  chr_pos ref alt causes   classes S1.occurrences S1.coverage    S1.VAF
#> 1  22 16917151   A   C   SBS1 passenger             20          34 0.5882353
#>   S2.occurrences S2.coverage S2.VAF S3.occurrences S3.coverage    S3.VAF
#> 1              0          27      0             12          23 0.5217391