Score REO-MetaKTSP within-sample pair-order votes — score_reo

Scores each row of X independently with the frozen oriented pairs learned by fit_reo_metaktsp. The model is trained meta-analytically across cohorts via meta_train[[cohort_col]] using avgTSP with equal cohort weight, but score-time inference is single-sample: each retained pair votes 1 only when feature_a < feature_b within that specimen. The graceful single-cohort degeneration, missing-cohort-row drop, and single-class-cohort skip are fit-time behaviors only. If no retained pair has both features present in X, scoring returns the neutral vote fraction 0.5 for every row.

Usage

score_reo_metaktsp(model, X, meta = NULL)

Arguments

model: A reo_metaktsp_model object returned by fit_reo_metaktsp.
X: Numeric matrix (samples \(\times\) features) of non-negative abundance values. Columns must be named feature ids.
meta: Optional per-sample metadata. Accepted for interface uniformity and ignored by this method.

Value

Numeric vector of one finite vote-fraction score per row of X. Scores use only each row's own values plus the frozen model, with no scored-batch centering, quantiles, or renormalization.

References

Kim S, Lin C-W, Tseng GC. (2016) MetaKTSP: a meta-analytically derived k-top-scoring-pair classifier for robust prediction of human cancer subtypes. Bioinformatics 32: 1966-1973. PMID: 27153719.

Geman D, d'Avignon C, Naiman DQ, Winslow RL. (2004) Classifying gene expression profiles from pairwise mRNA comparisons. Statistical Applications in Genetics and Molecular Biology 3: Article19.

Tan AC, Naiman DQ, Xu L, Winslow RL, Geman D. (2005) Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics 21: 3896-3904.

Examples

if (FALSE) { # \dontrun{
X <- matrix(stats::rgamma(60 * 12, shape = 2), nrow = 60,
            dimnames = list(NULL, paste0("miR-", seq_len(12))))
y <- rep(c(0, 1), times = 30)
meta <- data.frame(accession = rep(paste0("GSE", 1:3), each = 20))
X[y == 0, "miR-1"] <- X[y == 0, "miR-1"] + 5
X[y == 1, "miR-2"] <- X[y == 1, "miR-2"] + 5
model <- fit_reo_metaktsp(X, y, meta_train = meta, hp = list(k = 3L))
score_reo_metaktsp(model, X[1, , drop = FALSE])
} # }