Simulate Single Nucleotide Variants for Single Cell Data

Source: R/snv_relevant_functions.R

Simulates nucleotide-level details for single nucleotide variants (SNVs) in single cell mutation data, handling both regular and recurrent mutations with appropriate nucleotide changes.

sim_snv_nt_sc(
  genome_sequence,
  seg_list,
  mutation_table,
  recurrent_mutation_tracker = NA,
  nt_transition_matrix
)

Arguments

genome_sequence

List of DNA sequences representing the reference genome

seg_list

Data frame or list containing genomic segment information with mapping between chromosome coordinates and segment identifiers

mutation_table

Data frame containing mutation information with columns: clone, cell_index, haplotype, chrom, pos, time

recurrent_mutation_tracker

List structure tracking recurrent mutations, organized by genomic location and mutation sets (default is NA for no recurrent mutations)

nt_transition_matrix

Matrix specifying nucleotide transition probabilities for different mutation types

Value

An extended mutation_table data frame with additional columns:

  • seg_id: Segment identifier for the mutation

  • ref_pos: Reference position within the segment

  • original_nt: Original nucleotide at the mutation site

  • alternative_nt: Mutated nucleotide

  • processed: Logical flag indicating whether the mutation has been processed

Details

This function simulates the nucleotide-level details of mutations by:

  1. Extending the mutation table with columns for segment ID, reference position, original nucleotide, alternative nucleotide, and processing status

  2. Processing recurrent mutations first, maintaining proper nucleotide changes across mutation sets (later mutations in a set build upon earlier ones)

  3. Processing regular (non-recurrent) mutations

For recurrent mutations, the function ensures that:

  • The first mutation in a set is processed normally

  • Subsequent mutations in the set use the alternative nucleotide from the previous mutation as their original nucleotide

The function relies on helper functions:

  • find_identical_row_index(): To locate specific mutations in the table

  • simulate_single_nt_change(): To determine nucleotide changes for individual mutations