This method simulates a wild-type sample sequencing in a
phylogenetic forest. Add the cells in the wild-type sample contains
the germline mutations. The forest pre-neoplastic mutations are also
added to the sample by default. However, they can be avoided by
using the parameter with_preneoplastic.
Arguments
- phylo_forest
A phylogenetic forest.
- sequencer
The sequencer that performs the sequencing simulation (default: an
ErrorlessIlluminaSequencer).- reference_genome
The reference genome (default: NULL to use the mutation engine reference genome).
- chromosomes
The chromosomes that must be considered (default:
NULL, i.e., all the reference chromosomes).- coverage
The sequencing coverage (default:
10).- read_size
The read size (default:
150).- insert_size_mean
The insert size mean. Use 0 for single read sequencing and any value greater than 0 for pair read sequencing (default:
0).- insert_size_stddev
The insert size standard deviation. (default:
10).- output_dir
The SAM output directory (default:
"rRACES_normal_SAM").- write_SAM
A Boolean flag to enable/disable SAM generation (default:
TRUE).- update_SAM
Update the output directory (default:
FALSE).- with_preneoplastic
Add the forest pre-neoplastic mutations to the sample cells. (default:
TRUE).- filename_prefix
The prefix of the output SAM file name (default:
"chr_").- template_name_prefix
The template name prefix (default:
"r").- include_non_sequenced_mutations
A Boolean flag to include in the resulting data frame also the mutations that are not covered by any of the simulated reads, but occur to one of the samples at least (default:
FALSE).- seed
The random seed for the internal random generator (optional).
Value
A named list of two elements: the sequencing output data
frame (name "mutations") and the calling parameters
(name "parameters").
The sequencing output data frame reports, for each of the
observed SNVs and indels, the chromosome and the position in
which it occurs (columns chr and chr_pos),
the SNV reference base, the alterate base, the causes,
and the classes of the SNV (columns ref_base, alt_base,
causes, and classes, respectively). Moreover, for each
of the sequencied samples normal_sample, the returned
data frame contains three columns: the number of reads in
which the corresponding SNV occurs (column
normal_sample.occurrences), the coverage of the SNV
locus (column normal_sample.coverage), and the
corresponding VAF (column normal_sample.VAF).
See also
BasicIlluminaSequencer and
ErrorlessIlluminaSequencer as sequencer types, and
vignette("sequencing") for usage examples