Aligned set scores, and the alternatives worth comparing against

The aligned projection is dmsa_score()'s, restated for a matrix input and with optional winsorisation. The other three are what competing methods implicitly use, returned so that the comparison can be run rather than argued.

Usage

dmsa_scores(
  M,
  alignment,
  method = c("expected", "fixed"),
  winsor = 3,
  flavours = c("aligned", "mean", "pc1", "blind"),
  weighting = c("combined", "reliability", "flat"),
  w_floor = 1.5
)

Arguments

M

Numeric matrix, samples x probes, on the analysis scale (M-values). Columns are standardised internally.

alignment

A dmsa_align() result for the same probes in the same order, or a numeric vector of multipliers.

method

"expected" uses 2*p_s_plus - 1; "fixed" uses s.

winsor

Winsorise each probe at this many MADs first; NULL disables. 3 is the default because it won the positive-control estimator grid - outliers otherwise dominate the composite.

flavours

Which projections to return. aligned is DMSA's;

weighting

Character. Probe weighting engine within a unit: "combined" (default) fuses the flat and reliability statistics on one shared permutation stream, "flat" weights every usable aligned probe equally, "reliability" weights each probe by its item-rest correlation with the rest of its unit. Weights are computed from methylation alone, so the permutation null is unaffected.

w_floor

Numeric. Lower bound applied to reliability weights before normalisation, so a single poorly behaved probe cannot be driven to zero influence. Ignored when weighting = "flat". mean is the unweighted mean z that "averaging the gene set" means; pc1 is the regional principal component (DMRPC-style), oriented by the alignment so its sign stays interpretable; blind is mean |z|, the sign-blind control.

Value

data.frame of standardised scores, one column per flavour.

See also

dmsa_score for the single aligned vector on a data.frame, dmsa_model for the upper-level model.

Examples

if (FALSE) { # \dontrun{
al <- dmsa_align(dir_table, genes = gene, level = "gene")
S  <- dmsa_scores(M[, probes], al)
dat$oxytocin <- S$aligned
} # }