Compute pairwise sequence distances — compute_sequence

Provides transparent base-R implementations of Levenshtein edit distance, longest-common-subsequence distance, optimal matching, and a first-order transition-profile distance.

Usage

compute_sequence_distance(
  data,
  sequence_id_col = "sequence_id",
  order_col = "sequence_order",
  state_col = "state",
  method = c("levenshtein", "lcs", "optimal_matching", "transition"),
  indel_cost = 1,
  substitution_cost = 1,
  substitution_matrix = NULL,
  transition_smoothing = 0,
  normalise = c("none", "max_length", "path_length")
)

Arguments

data: Long-format sequence data or a prepared gp3sequences result.
sequence_id_col, order_col, state_col: Sequence columns.
method: Distance method.
indel_cost: Non-negative insertion/deletion cost used by method = "optimal_matching".
substitution_cost: Non-negative default substitution cost used by method = "optimal_matching".
substitution_matrix: Optional named square substitution-cost matrix for method = "optimal_matching".
transition_smoothing: Non-negative smoothing for transition profiles.
normalise: One of "none", "max_length", or "path_length".

Value

A dist object with method and preprocessing metadata attached.

Examples

sequences <- data.frame(
  sequence_id = rep(c("s1", "s2", "s3", "s4"), each = 4L),
  sequence_order = rep(1:4, times = 4L),
  state = c("A", "B", "C", "D", "A", "B", "C", "C",
            "D", "C", "B", "A", "D", "C", "A", "A"),
  group = rep(c("g1", "g2"), each = 8L),
  stringsAsFactors = FALSE
)
compute_sequence_distance(sequences, method = "lcs")
#>    s1 s2 s3
#> s2  2      
#> s3  6  6   
#> s4  6  6  2