Score REO-singscore within-sample rank signature — score_reo

Scores each row of X independently with the frozen signature learned by fit_reo_singscore. For one sample, features in the frozen training universe that are present in X are ranked in increasing abundance using rank(x, ties.method = "average") / length(x). The up term is the mean fractional rank of present up features; the down term is the mean fractional rank of present down features. If a signature direction has no present features, it contributes the neutral expected fractional rank 0.5. With use_down = TRUE, the returned score is mean_rank(up) - mean_rank(down); otherwise it is mean_rank(up).

Usage

score_reo_singscore(model, X, meta = NULL)

Arguments

model: A reo_singscore_model object returned by fit_reo_singscore.
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 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. If no model-universe features are shared with X, scoring stops explicitly.

References

Foroutan M, Bhuva DD, Lyu R, Horan K, Cursons J, Davis MJ. (2018) Single sample scoring of molecular phenotypes. BMC Bioinformatics 19: 404. PMID 30400809.

Examples

if (FALSE) { # \dontrun{
set.seed(1)
X <- matrix(stats::rgamma(40 * 30, shape = 2), nrow = 40,
            dimnames = list(NULL, paste0("miR-", seq_len(30))))
y <- rep(c(0, 1), each = 20)
X[y == 1, 1:5] <- X[y == 1, 1:5] + 5
model <- fit_reo_singscore(X, y)
score_reo_singscore(model, X[1, , drop = FALSE])
} # }