Landmark points for dCST dominance-lineages

Computes representative landmark points for the dominance-lineages of a "linf.csts" object at a chosen depth and view.

Landmark types are defined with respect to the leaf feature of the dCST path: the last feature ID in the lineage ID path. Lineages whose leaf token is rare.label are reported but skipped for landmark computation because they do not correspond to a unique target feature.

Usage

linf.landmarks(
  M,
  csts,
  depth = NULL,
  view = c("active", "pure", "absorb"),
  landmark.types = c("endpoint.max", "endpoint.min", "mean.rep", "median.rep"),
  tie.method = c("first", "random", "error"),
  backend = c("auto", "dense", "sparse")
)

Arguments

M

Numeric matrix (samples x features) used to build or refine the dCSTs.

csts

A "linf.csts" object.

depth

Integer. dCST depth to inspect. Defaults to the leaf depth csts$depth.

view

Character. One of "active", "pure", or "absorb".

landmark.types

Character vector containing any of "endpoint.max", "endpoint.min", "mean.rep", or "median.rep".

tie.method

Character. Tie handling for landmark selection: "first", "random", or "error".

backend

Character. Matrix backend to use: "auto", "dense", or "sparse". The default "auto" preserves sparse input and otherwise uses the dense path.

Value

A list of class "linf.landmarks" with components:

• depth, view, sep, rare.label

• feature.ids, feature.labels

• lineages: one row per dominance-lineage with computability metadata

• landmarks: one row per computed landmark point

Examples

M <- rbind(
  s1 = c(A = 1.0, B = 0.2),
  s2 = c(A = 0.9, B = 0.4),
  s3 = c(A = 0.3, B = 1.0),
  s4 = c(A = 0.1, B = 0.9)
)
fit <- linf.csts(M, n0 = 2, low.freq.policy = "absorb")
landmarks <- linf.landmarks(
  M,
  fit,
  landmark.types = c("endpoint.max", "mean.rep")
)
landmarks$landmarks
#>   lineage.id lineage.label landmark.type point.index point.name
#> 1          A             A  endpoint.max           1         s1
#> 2          A             A      mean.rep           2         s2
#> 3          B             B  endpoint.max           3         s3
#> 4          B             B      mean.rep           4         s4
#>   target.feature.id target.feature.label observed.value target.value
#> 1                 A                    A            1.0         1.00
#> 2                 A                    A            0.9         0.95
#> 3                 B                    B            1.0         1.00
#> 4                 B                    B            0.9         0.95
#>   abs.deviation
#> 1          0.00
#> 2          0.05
#> 3          0.00
#> 4          0.05