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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.

Once a phylogenetic forest has been computed, as detailed in vignette("mutations"), ProCESS can simulate the sequencing of the samples in the forest and return the observed data.

Sequencing Simulation

Let us reload the phylogenetic forest produced in vignette("mutations").

library(ProCESS)

phylo_forest <- load_phylogenetic_forest("phylo_forest.sff")
#>  [█---------------------------------------] 0% [00m:00s] Loading forest [████████--------------------------------] 18% [00m:01s] Loading forest [███████████████-------------------------] 35% [00m:02s] Loading forest [█████████████████████-------------------] 52% [00m:03s] Loading forest [████████████████████████████------------] 69% [00m:04s] Loading forest [███████████████████████████████████-----] 86% [00m:05s] Loading forest [████████████████████████████████████████] 100% [00m:06s] Forest loaded

phylo_forest
#> PhylogeneticForest
#>   # of trees: 1
#>   # of nodes: 18845
#>   # of leaves: 5396
#>   samples: {"S_1_1", "S_1_2", "S_2_1", "S_2_2"}
#> 
#>   # of emerged SNVs and indels: 15101
#>   # of emerged CNAs: 37

The loaded phylogenetic forest models the cell evolution of 4 different samples: S_1_1, S_1_2, S_2_1, and S_2_2.

We can simulate the sequencing of these samples with coverage of 2.5X by as it follows.

# let us simulate a 30x sequencing of the four sample
seq_results <- simulate_seq(phylo_forest, coverage = 30)
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#>  [████████████████████████████████████████] 100% [04m:44s] Processing chr. 22
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#>  [████████████████████████████████████████] 100% [04m:44s] Processing chr. 22
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#>  [████████████████████████████████████████] 100% [04m:44s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [04m:44s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [04m:44s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [04m:44s] Processing chr. 22
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#>  [████████████████████████████████████████] 100% [04m:44s] Reads simulated

# let us load the dplyr library to filter the `simulate_seq` output
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

seq_results$parameters
#> $sequencer
#> list()
#> 
#> $reference_genome
#> NULL
#> 
#> $chromosomes
#> NULL
#> 
#> $coverage
#> [1] 30
#> 
#> $read_size
#> [1] 150
#> 
#> $insert_size_mean
#> [1] 0
#> 
#> $insert_size_stddev
#> [1] 10
#> 
#> $output_dir
#> [1] "ProCESS_SAM"
#> 
#> $write_SAM
#> [1] FALSE
#> 
#> $update_SAM
#> [1] FALSE
#> 
#> $cell_labelling
#> NULL
#> 
#> $purity
#> [1] 1
#> 
#> $with_normal_sample
#> [1] TRUE
#> 
#> $filename_prefix
#> [1] "chr_"
#> 
#> $template_name_prefix
#> [1] "r"
#> 
#> $include_non_sequenced_mutations
#> [1] FALSE
#> 
#> $seed
#> [1] -1800664448

seq_results$mutations %>% head
#>   chr  chr_pos ref   alt causes  classes S_1_1.occurrences S_1_1.coverage
#> 1  22 16051493   G     A        germinal                20             33
#> 2  22 16052080   G     A        germinal                16             32
#> 3  22 16052167   A AAAAC        germinal                30             30
#> 4  22 16053659   A     C        germinal                24             24
#> 5  22 16053863   G     A        germinal                25             37
#> 6  22 16054740   A     G        germinal                31             31
#>   S_1_1.VAF S_1_2.occurrences S_1_2.coverage S_1_2.VAF S_2_1.occurrences
#> 1 0.6060606                10             22 0.4545455                10
#> 2 0.5000000                23             37 0.6216216                12
#> 3 1.0000000                34             34 1.0000000                26
#> 4 1.0000000                30             30 1.0000000                24
#> 5 0.6756757                18             32 0.5625000                18
#> 6 1.0000000                20             20 1.0000000                35
#>   S_2_1.coverage S_2_1.VAF S_2_2.occurrences S_2_2.coverage S_2_2.VAF
#> 1             23 0.4347826                16             35 0.4571429
#> 2             27 0.4444444                16             31 0.5161290
#> 3             26 1.0000000                23             23 1.0000000
#> 4             24 1.0000000                21             21 1.0000000
#> 5             37 0.4864865                22             35 0.6285714
#> 6             35 1.0000000                32             32 1.0000000
#>   normal_sample.occurrences normal_sample.coverage normal_sample.VAF
#> 1                        14                     32         0.4375000
#> 2                         6                     23         0.2608696
#> 3                        24                     24         1.0000000
#> 4                        24                     24         1.0000000
#> 5                        15                     33         0.4545455
#> 6                        28                     28         1.0000000

The function simulate_seq() returns named list of two elements: a data frame whose rows represent the mutations observed in the simulated reads (name “mutations”) and the calling parameters (name “parameters”).

The number of columns in the data frame “mutations” depends on the number of samples in the phylogenetic forest. The first four columns describe the mutation and report the chromosome and the position in the chromosome of the mutation (columns chr and chr_pos, respectively), the genetic context in which the mutation occurs (column context), and the new base (column alt_base). Then, there are three columns for each of the samples: they contain the number of simulated reads affected by the mutation, the sequencing coverage of the mutation locus, and the ratio between these two last columns (columns <sample name> occurrences, <sample name> coverage, and <sample name> VAF, respectively).

Plotting Sequencing Data

ProCESS/RACE provides functions to plot the Variant Allele Frequency (VAF), the B-Allele Frequency (BAF), and the Depth Ratio (DR) of a sample.

library(dplyr)

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

# plot the VAF of the mutations sequenced on the sample S_2_2
# over the reference genome
plot_VAF(f_seq, sample = "S_2_2")

A plot depicting the Variant Allele Frequency (VAF) of a sequenced sample.


# plot the BAF of the mutations sequenced on the sample S_2_2
# over the reference genome
plot_BAF(f_seq, sample = "S_2_2")

A plot depicting the B-Allele Frequency (BAF) of a sequenced sample.


# plot the DR of the mutations sequenced on the sample S_2_2
# over the reference genome
plot_DR(f_seq, sample = "S_2_2")

A plot depicting the Depth Ratio (DR) of a sequenced sample.

The VAF and the VAF marginal distributions can be plot too.

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

The VAF histogram of all samples.


# plotting the VAF marginals
plot_VAF_marginals(f_seq, samples = c("S_1_1", "S_1_2", "S_2_2"))
#> [[1]]
#> Ignoring unknown labels:
#>  colour : "labels"

The VAF marginal distribution S_1_1 vs S_1_2.

#> 
#> [[2]]
#> Ignoring unknown labels:
#>  colour : "labels"

The VAF marginal distribution S_1_1 vs S_2_2.

#> 
#> [[3]]
#> Ignoring unknown labels:
#>  colour : "labels"

The VAF marginal distribution S_1_2 vs S_2_2.

The VAF and the VAF marginal plots can be labelled.

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

The VAF histogram labelled by mutation class.


# plotting the VAF marginals and labelling it
plot_VAF_marginals(f_seq, samples = c("S_1_1", "S_1_2", "S_2_2"),
                   labels = f_seq["classes"])
#> [[1]]

The VAF marginal distribution S_1_1 vs S_1_2 labelled by mutation class.

#> 
#> [[2]]

The VAF marginal distribution S_1_1 vs S_2_2 labelled by mutation class.

#> 
#> [[3]]

The VAF marginal distribution S_1_2 vs S_2_2 labelled by mutation class.

Saving the Simulated Reads

ProCESS/RACE can also save the simulated reads in the SAM format (see (Li et al. 2009)). By setting the optional parameter write_SAM to TRUE, the function simulate_seq() creates the directory ProCESS_SAM and saves the SAM files in it. Each file is named after one of the reference genome chromosomes and contains the simulated reads. The reads are split into read groups corresponding to the collected samples.

seq_results <- simulate_seq(phylo_forest, coverage = 2.5, write_SAM = TRUE)
#>  [█---------------------------------------] 0% [00m:00s]  [█---------------------------------------] 0% [00m:00s] Processing chr. 22 [█---------------------------------------] 0% [00m:03s] Processing chr. 22 [███-------------------------------------] 5% [00m:04s] Processing chr. 22 [█████-----------------------------------] 11% [00m:05s] Processing chr. 22 [███████---------------------------------] 17% [00m:06s] Processing chr. 22 [██████████------------------------------] 23% [00m:06s] Processing chr. 22 [████████████----------------------------] 28% [00m:07s] Processing chr. 22 [██████████████--------------------------] 34% [00m:08s] Processing chr. 22 [█████████████████-----------------------] 40% [00m:09s] Processing chr. 22 [███████████████████---------------------] 46% [00m:10s] Processing chr. 22 [█████████████████████-------------------] 50% [00m:11s] Processing chr. 22 [██████████████████████------------------] 52% [00m:12s] Processing chr. 22 [███████████████████████-----------------] 57% [00m:13s] Processing chr. 22 [█████████████████████████---------------] 60% [00m:14s] Processing chr. 22 [██████████████████████████--------------] 63% [00m:15s] Processing chr. 22 [███████████████████████████-------------] 67% [00m:16s] Processing chr. 22 [█████████████████████████████-----------] 70% [00m:17s] Processing chr. 22 [██████████████████████████████----------] 74% [00m:18s] Processing chr. 22 [███████████████████████████████---------] 77% [00m:19s] Processing chr. 22 [████████████████████████████████--------] 79% [00m:20s] Processing chr. 22 [█████████████████████████████████-------] 80% [00m:21s] Processing chr. 22 [█████████████████████████████████-------] 81% [00m:22s] Processing chr. 22 [██████████████████████████████████------] 83% [00m:23s] Processing chr. 22 [██████████████████████████████████------] 84% [00m:24s] Processing chr. 22 [███████████████████████████████████-----] 85% [00m:25s] Processing chr. 22 [███████████████████████████████████-----] 86% [00m:26s] Processing chr. 22 [████████████████████████████████████----] 88% [00m:27s] Processing chr. 22 [████████████████████████████████████----] 89% [00m:28s] Processing chr. 22 [█████████████████████████████████████---] 90% [00m:29s] Processing chr. 22 [█████████████████████████████████████---] 92% [00m:30s] Processing chr. 22 [██████████████████████████████████████--] 93% [00m:31s] Processing chr. 22 [██████████████████████████████████████--] 94% [00m:32s] Processing chr. 22 [███████████████████████████████████████-] 96% [00m:33s] Processing chr. 22 [███████████████████████████████████████-] 97% [00m:34s] Processing chr. 22 [████████████████████████████████████████] 98% [00m:35s] Processing chr. 22 [████████████████████████████████████████] 99% [00m:36s] Processing chr. 22 [████████████████████████████████████████] 99% [00m:37s] Processing chr. 22 [████████████████████████████████████████] 100% [00m:38s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:38s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:38s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:38s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:38s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:38s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:38s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:38s] Reads simulated

sam_files <- list.files("ProCESS_SAM/")

ex_file <- paste("ProCESS_SAM/", sam_files[1], sep = "")
for (line in readLines(ex_file, n = 10)) {
  cat(paste(line, "\n"))
}
#> @HD  VN:1.6  SO:unknown 
#> @SQ  SN:22   LN:51304566 AN:chr22, chromosome22,chromosome_22,chr_22 
#> @RG  ID:S_1_1    SM:S_1_1    PL:ILLUMINA 
#> @RG  ID:S_1_2    SM:S_1_2    PL:ILLUMINA 
#> @RG  ID:S_2_1    SM:S_2_1    PL:ILLUMINA 
#> @RG  ID:S_2_2    SM:S_2_2    PL:ILLUMINA 
#> @RG  ID:normal_sample    SM:normal_sample    PL:ILLUMINA 
#> r2200000000002   16  22  37688272    93  150M    *   0   0   GAATGCGTGCCTTCAGCGAGTGTTTAACTGAGTGCTTATGACATGTCAGACTCTTTCTAGGTGCTGGGGACACAGTAGTGAGCAAAAGAGTAGAACAACTGAACTCTCATGGAGCCGAGAGTCTAGCGGGAGGGACAGAAAAGATGCAAG  FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF  RG:Z:S_1_1  NM:i:0  CB:Z:0000025I 
#> r2200000000003   16  22  23211437    93  150M    *   0   0   CTCATACTTGCAGTGATTTCCAATGCAGTAGGAATTCTCTGGTGCTGAGGAGTCTACTAGGAATATAATTTCAATGCCTTTAAAATTTTTCAGTAGCCACATTTTAAAAGTAATAAAGAACGGGTGGAAGCAACTTTCGTACTATATTTT  FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF  RG:Z:S_1_1  NM:i:0  CB:Z:0000025I 
#> r2200000000005   0   22  30432954    93  150M    *   0   0   CATACCCTCATCCAAAAAGAACAGGACCTGAAAAAGCCTCTTAGAGGCAACCCATTGAAAATGGAGACCAGTGTTTCATACAGCACATAGTGAAAAAGGTTTGATCCGAATTTAATCTTGTAAAATGAAACTATTATTGAAATAAGTATC  FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF  RG:Z:S_1_1  NM:i:0  CB:Z:0000025I

Each SAM file contains the reads produced by simulating the sequencing of all the samples. The command-line tools samtools can be used to split the reads by sample.

foo@bar % samtools split  -f "%*_%\!.sam" ProCESS_SAM/chr_22.sam
foo@bar % ls chr_22_*
chr_22_S_1_1.sam        chr_22_S_1_2.sam        chr_22_S_2_1.sam        chr_22_S_2_2.sam

The resulting files are named after the samples, each containing the reads of only one sample.

Please refer to the samtools split manual for more details.

The simulate_seq() parameter output_dir set the SAM output directory. See the following section for usage examples of this parameter.

Sample Purity

The sample purity represents the concentration of tumour cells in a sample. More formally, it is the ratio between the number of tumour cells in the sample and that of all the sample cells, either tumour or normal (i.e., cells having germline and pre-neoplastic mutations).

ProCESS can simulate different sample purity in sequencing simulations.

# simulate the sequencing of a sample in which 70% of the cells are
# tumour cells
seq_results <- simulate_seq(phylo_forest, coverage = 2.5, purity = 0.7,
                            write_SAM = TRUE, output_dir = "SAM_0.7")
#>  [█---------------------------------------] 0% [00m:00s]  [█---------------------------------------] 0% [00m:00s] Processing chr. 22 [█---------------------------------------] 0% [00m:02s] Processing chr. 22 [███-------------------------------------] 5% [00m:03s] Processing chr. 22 [█████-----------------------------------] 11% [00m:04s] Processing chr. 22 [███████---------------------------------] 17% [00m:05s] Processing chr. 22 [█████████-------------------------------] 22% [00m:06s] Processing chr. 22 [████████████----------------------------] 28% [00m:07s] Processing chr. 22 [██████████████--------------------------] 34% [00m:08s] Processing chr. 22 [█████████████████-----------------------] 40% [00m:09s] Processing chr. 22 [███████████████████---------------------] 45% [00m:10s] Processing chr. 22 [████████████████████--------------------] 49% [00m:11s] Processing chr. 22 [██████████████████████------------------] 52% [00m:12s] Processing chr. 22 [███████████████████████-----------------] 57% [00m:13s] Processing chr. 22 [█████████████████████████---------------] 60% [00m:14s] Processing chr. 22 [██████████████████████████--------------] 64% [00m:15s] Processing chr. 22 [████████████████████████████------------] 68% [00m:16s] Processing chr. 22 [█████████████████████████████-----------] 71% [00m:17s] Processing chr. 22 [███████████████████████████████---------] 75% [00m:18s] Processing chr. 22 [████████████████████████████████--------] 78% [00m:19s] Processing chr. 22 [████████████████████████████████--------] 79% [00m:20s] Processing chr. 22 [█████████████████████████████████-------] 80% [00m:21s] Processing chr. 22 [█████████████████████████████████-------] 81% [00m:22s] Processing chr. 22 [█████████████████████████████████-------] 82% [00m:23s] Processing chr. 22 [██████████████████████████████████------] 84% [00m:24s] Processing chr. 22 [███████████████████████████████████-----] 85% [00m:25s] Processing chr. 22 [███████████████████████████████████-----] 86% [00m:26s] Processing chr. 22 [███████████████████████████████████-----] 87% [00m:27s] Processing chr. 22 [████████████████████████████████████----] 89% [00m:28s] Processing chr. 22 [█████████████████████████████████████---] 90% [00m:29s] Processing chr. 22 [█████████████████████████████████████---] 91% [00m:30s] Processing chr. 22 [██████████████████████████████████████--] 93% [00m:31s] Processing chr. 22 [██████████████████████████████████████--] 94% [00m:32s] Processing chr. 22 [███████████████████████████████████████-] 95% [00m:33s] Processing chr. 22 [███████████████████████████████████████-] 97% [00m:34s] Processing chr. 22 [████████████████████████████████████████] 98% [00m:35s] Processing chr. 22 [████████████████████████████████████████] 99% [00m:36s] Processing chr. 22 [████████████████████████████████████████] 99% [00m:37s] Processing chr. 22 [████████████████████████████████████████] 99% [00m:38s] Processing chr. 22 [████████████████████████████████████████] 100% [00m:39s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:39s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:39s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:39s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:39s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:39s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:39s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:39s] Reads simulated

sam_files <- list.files("SAM_0.7/")

ex_file <- paste("SAM_0.7/", sam_files[1], sep = "")
for (line in readLines(ex_file, n = 10)) {
  cat(paste(line, "\n"))
}
#> @HD  VN:1.6  SO:unknown 
#> @SQ  SN:22   LN:51304566 AN:chr22, chromosome22,chromosome_22,chr_22 
#> @RG  ID:S_1_1    SM:S_1_1    PL:ILLUMINA 
#> @RG  ID:S_1_2    SM:S_1_2    PL:ILLUMINA 
#> @RG  ID:S_2_1    SM:S_2_1    PL:ILLUMINA 
#> @RG  ID:S_2_2    SM:S_2_2    PL:ILLUMINA 
#> @RG  ID:normal_sample    SM:normal_sample    PL:ILLUMINA 
#> r2200000000000   16  22  22309146    93  150M    *   0   0   CACATTCCTTAGCTCACCTGAGCCTCAGTTTCTTGATGAGAAAAAATCGGGACAATAGCAACCAACCTATCTCAGAGGGCTGCTGTGAGACCCTGAGCCACTGTTTCTCAAACTGCCCCTGAGATCACCTACAACAGAATGGCTGTAGGG  FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF  RG:Z:S_1_1  NM:i:0  CB:Z:0000025I 
#> r2200000000001   0   22  40552280    93  150M    *   0   0   ACGTTGGAGACATTGGGGAACTCACCAGTTGCTTTGGTGGCTGTTAATGCATTGCTTCTATTTATAGACCAACTAACTAGAAGGACAAGAAAGTTAGCGTTACAGTTTTTCACACTCGCCTAATCAAAATTTATGTTATGTTGTAGCAAA  FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF  RG:Z:S_1_1  NM:i:0  CB:Z:0000025I 
#> r2200000000003   16  22  38382851    93  150M    *   0   0   CAGCAGGCAAGGGTGAAGGACCCATAGTCATTCCTGAAGAGCAGGGAGCTTGGAGAGGGGAGGGATCCTGGCTGAAGACACCTGGGCCTCTGCCCCAGCAGGACAACAGGAGGGCCAGTGTCACCTTCTCCTCCTTTCCCTGCTGGGGGT  FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF  RG:Z:S_1_1  NM:i:0  CB:Z:0000025I

The default value of the parameter purity is 1, i.e., simulate_seq() assumes all the cells in the sample to be neoplastic by default.

The function simulate_normal_seq() simulates the sequencing of a sample whose cells have germline and tumoral pre-neoplastic mutations. The pre-neoplastic mutations can be avoided by setting the optional parameter with_preneoplastic.

Simulating Sequencing Errors

To simulate sequencing errors, users need to specify a sequencer to the function simulate_seq().

At the moment, there are only two classes implementing sequencers: the ErrorlessIlluminaSequencer class and the BasicIlluminaSequencer classes.

The ErrorlessIlluminaSequencer Class

This class models a perfect Illumina sequencer. No sequencing errors are produced, and all the bases have the maximum quality.

The following code simulates the sequencing by using this kind of sequencer.

# build an error-free Illumina sequencer
no_error_seq <- ErrorlessIlluminaSequencer()

# let us simulate a 2.5x sequencing of the four sample
# on the error-free sequencer
seq_results <- simulate_seq(phylo_forest, sequencer = no_error_seq,
                            coverage = 2.5)

The BasicIlluminaSequencer Class

This class sets a sequencing error rate independent of the base in which the error occurs, from the position of the base in the read and from the position of the read on the genome.

# build a basic Illumina sequencer model in which errors occur
# at rate 4e-3 per base
basic_seq <- BasicIlluminaSequencer(4e-3)

# let us simulate a 2.5x sequencing of the four sample
# on the error-free sequencer
seq_results <- simulate_seq(phylo_forest, sequencer = basic_seq,
                            coverage = 2.5, write_SAM = TRUE)

Chromosome Sequencing

The function simulate_seq() allows users to simulate the sequencing of a selection of the reference chromosomes by using the parameter chromosomes.

# let us simulate a 2.5x sequencing of the chromosomes 22 and
# X of the four sample
seq_results <- simulate_seq(phylo_forest, chromosomes = c("22", "X"),
                            coverage = 2.5, write_SAM = TRUE)

Updating the SAM Output Directory

Users may want to simulate sequencing in multiple steps, for instance, by splitting it by chromosome or reaching the aimed coverage with different simulations. However, ProCESS prevents successive writing in the same directory by default. The Boolean parameter update_SAM allows multiple writing in the same directory.

# the default SAM directory already exists
sam_files <- list.files("ProCESS_SAM/")

# since the default save directory already exists, any call to
# `simulate_seq` throws an error
tryCatch(
  {
    simulate_seq(phylo_forest, coverage = 2.5, write_SAM = TRUE)
  },
  error = function(e) {
    print(e)
  }
)
#> <std::domain_error in eval(expr, envir): "ProCESS_SAM" already exists>

# setting `update_SAM` to TRUE enables successive writing in
# the output directory
seq_results <- simulate_seq(phylo_forest, coverage = 2.5,
                            write_SAM = TRUE, update_SAM = TRUE)
#>  [█---------------------------------------] 0% [00m:00s]  [█---------------------------------------] 0% [00m:00s] Processing chr. 22 [█---------------------------------------] 0% [00m:03s] Processing chr. 22 [███-------------------------------------] 5% [00m:04s] Processing chr. 22 [█████-----------------------------------] 11% [00m:05s] Processing chr. 22 [███████---------------------------------] 16% [00m:06s] Processing chr. 22 [█████████-------------------------------] 22% [00m:07s] Processing chr. 22 [███████████-----------------------------] 27% [00m:08s] Processing chr. 22 [█████████████---------------------------] 32% [00m:09s] Processing chr. 22 [███████████████-------------------------] 37% [00m:10s] Processing chr. 22 [█████████████████-----------------------] 42% [00m:11s] Processing chr. 22 [███████████████████---------------------] 47% [00m:12s] Processing chr. 22 [█████████████████████-------------------] 51% [00m:13s] Processing chr. 22 [███████████████████████-----------------] 55% [00m:14s] Processing chr. 22 [████████████████████████----------------] 58% [00m:15s] Processing chr. 22 [█████████████████████████---------------] 62% [00m:16s] Processing chr. 22 [███████████████████████████-------------] 65% [00m:17s] Processing chr. 22 [████████████████████████████------------] 69% [00m:18s] Processing chr. 22 [█████████████████████████████-----------] 72% [00m:19s] Processing chr. 22 [███████████████████████████████---------] 76% [00m:20s] Processing chr. 22 [████████████████████████████████--------] 78% [00m:21s] Processing chr. 22 [█████████████████████████████████-------] 80% [00m:22s] Processing chr. 22 [█████████████████████████████████-------] 81% [00m:23s] Processing chr. 22 [█████████████████████████████████-------] 82% [00m:24s] Processing chr. 22 [██████████████████████████████████------] 83% [00m:25s] Processing chr. 22 [███████████████████████████████████-----] 85% [00m:26s] Processing chr. 22 [███████████████████████████████████-----] 86% [00m:27s] Processing chr. 22 [███████████████████████████████████-----] 87% [00m:28s] Processing chr. 22 [████████████████████████████████████----] 88% [00m:29s] Processing chr. 22 [█████████████████████████████████████---] 90% [00m:30s] Processing chr. 22 [█████████████████████████████████████---] 91% [00m:31s] Processing chr. 22 [█████████████████████████████████████---] 92% [00m:32s] Processing chr. 22 [██████████████████████████████████████--] 94% [00m:33s] Processing chr. 22 [███████████████████████████████████████-] 95% [00m:34s] Processing chr. 22 [███████████████████████████████████████-] 96% [00m:35s] Processing chr. 22 [███████████████████████████████████████-] 97% [00m:36s] Processing chr. 22 [████████████████████████████████████████] 99% [00m:37s] Processing chr. 22 [████████████████████████████████████████] 99% [00m:38s] Processing chr. 22 [████████████████████████████████████████] 99% [00m:39s] Processing chr. 22 [████████████████████████████████████████] 100% [00m:40s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:40s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:40s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:40s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:40s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:40s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:40s] Processing chr. 22
#>  [████████████████████████████████████████] 100% [00m:40s] Reads simulated

When the output directory already contains a SAM file associated with a chromosome, the alignments on that chromosome are saved in a file whose name is the first available, having the format "chr_{chromosome name}_{natural number}.sam".

list.files("ProCESS_SAM/")
#> [1] "chr_22_0.sam" "chr_22.sam"
Li, Heng, Bob Handsaker, Alec Wysoker, et al. 2009. The Sequence Alignment/Map format and SAMtools.” Bioinformatics 25 (16): 2078–79. https://doi.org/10.1093/bioinformatics/btp352.