Score specimens with a frozen graph-Fourier discriminator

Scores each row of X independently with a frozen gsp_gft_model. For each specimen, the scorer computes a per-sample rCLR vector over the model features that are present in X, aligns that vector to the frozen training feature universe with absent features set to 0, projects it onto the frozen low-frequency graph-Fourier basis, applies the frozen coordinate standardization, and evaluates the frozen linear head.

Usage

score_gsp_gft(model, X, meta = NULL)

Arguments

model: A gsp_gft_model object returned by fit_gsp_gft.
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

Plain finite numeric vector of length nrow(X). Larger values are more case-like. If fewer than model$hp$min_features model features are present, the documented neutral score 0 is returned for every row.

Details

The score of row $i$ depends only on X[i, ] and the frozen model. The scored batch contributes no centering, normalization, graph update, bandwidth, basis, or head statistic. The rCLR representation is exactly invariant to multiplying one specimen by a positive scalar, because $\log(c v_j) - mean_k \log(c v_k) = \log(v_j) - mean_k \log(v_k)$ over that specimen's own positive entries.

References

Shuman DI, Narang SK, Frossard P, Ortega A, Vandergheynst P. (2013) The emerging field of signal processing on graphs. IEEE Signal Processing Magazine 30: 83-98.

Chung FRK. (1997) Spectral Graph Theory. American Mathematical Society.

Examples

if (FALSE) { # \dontrun{
set.seed(1)
X <- matrix(stats::rgamma(60 * 30, shape = 3), nrow = 60,
            dimnames = list(NULL, paste0("miR-", seq_len(30))))
y <- rep(c(0, 1), each = 30)
X[y == 1, 1:6] <- X[y == 1, 1:6] + 20
model <- fit_gsp_gft(X, y)
score_gsp_gft(model, X[1, , drop = FALSE])
} # }