Score the genome under a trained gsynth model

Writes a misha fixed-bin dense track whose value at each output bin is the summed natural-log conditional probability of the reference sequence under the trained Markov model: $$T(a) = \sum_{i=a}^{a+r-1} \log P_M(\mathrm{seq}[i] \mid \mathrm{seq}[i-k..i-1], b(i))$$ where $b(i)$ is the model's stratum bin (constant within each model$iterator-bp window). The first $k$ bp of every chromosome are NA (no upstream context available); under default policies, any output bin containing an NA per-bp contribution is NA.

Usage

gsynth.score(
  model,
  track,
  description = NULL,
  intervals = NULL,
  mask = NULL,
  resolution = NULL,
  sparse_policy = c("NA", "uniform"),
  n_policy = c("NA", "uniform"),
  overwrite = FALSE
)

Arguments

model: A gsynth.model object (from gsynth.train).
track: Name of the misha track to create.
description: Optional track description.
intervals: Intervals to score. Defaults to gintervals.all(). Overlapping intervals are unified first, so each position is scored once. The stratum of a position comes from the iterator windows of its own interval, extended upstream by one model$iterator: a window cut by the interval end (or by the start of the extension) is evaluated on its part inside, so its bin may differ from the one training saw on the whole window. Intervals aligned to multiples of model$iterator avoid this.
mask: Optional intervals to NA-out in the output (e.g. repeats). Intersects with intervals per bp; every output bin containing a masked bp becomes NaN.
resolution: Output bin size in bp. Defaults to model$iterator. 1 produces a per-bp track; any positive integer is allowed.
sparse_policy: How to score positions whose stratum bin is sparse in the model: "NA" (default) propagates NA; "uniform" contributes log(1/4) per bp.
n_policy: How to score positions whose k-mer context contains an N: "NA" (default) or "uniform" (log(1/4) per bp). The predicted base itself is always NA when N — the model has no $\log P$ for non-ACGT bases.
overwrite: If TRUE, replace an existing track of the same name.

Value

Invisibly NULL. Side effect: creates the named misha track. Output bins are written as NaN where the sum is undefined (out of intervals, or any per-bp NA).

Details

Two models scored against the same reference give a windowed log Bayes factor as a track expression:


    gsynth.score(genome_model, "genome_score")
    gsynth.score(cre_model,    "cre_score")
    gextract("cre_score - genome_score", iterator=1000, ...)

Score the genome under a trained gsynth model

Usage

Arguments

Value

Details

See also