Fit REO-kTSP within-sample pair-order discriminator

Learns a frozen k-Top-Scoring-Pairs (k-TSP) discriminator from training samples by calling os_ktsp_fit. The fitted primitive stores oriented feature pairs so that larger vote fractions indicate the case class: each retained pair votes 1 for a specimen when feature_a < feature_b within that specimen. No test data or test-time batch statistics are used.

Usage

fit_reo_ktsp(X_train, y_train, meta_train = NULL, hp = list())

Arguments

X_train

Numeric matrix (samples \(\times\) features) of non-negative abundance values. Columns must be uniquely named feature ids.

y_train

Integer/numeric 0/1 labels aligned to X_train; 1 is case/disease and 0 is control.

meta_train

Optional per-sample metadata. Accepted for interface uniformity and ignored by this method.

hp

List of frozen hyperparameters: k, the number of oriented pairs requested from os_ktsp_fit (default 11L). k is resolved once during fitting and is not tuned inside fit_reo_ktsp().

Value

A plain list of class reo_ktsp_model containing the fitted os_ktsp_model in ktsp, feature_universe, retained pair count k, and the resolved hp.

References

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(40 * 12, shape = 2), nrow = 40,
            dimnames = list(NULL, paste0("miR-", seq_len(12))))
y <- rep(c(0, 1), 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_ktsp(X, y, hp = list(k = 3L))
score <- score_reo_ktsp(model, X)
} # }