Gene scores from multinomial elastic-net cell-type classification

Fits a multinomial (or binomial) elastic net (glmnet::glmnet()) that predicts cell type from expression features. Inputs are purified expression profiles \(\boldsymbol{X}\in\mathcal{M}_{G\times J\times N}\) (genes \(\times\) cell types \(\times\) samples) and length-\(J\) cell-type labels. Variability across samples replaces synthetic isotropic noise. Gene scores are the sum over classes of absolute coefficients at a chosen \(\lambda\) (intercept excluded). For nested / CV selection of \(\lambda\), see the experimental compute_glmnet_gene_scores_cv() helper (not shipped in the package build).

Usage

compute_glmnet_gene_scores(
  expression_profiles,
  celltype_labels,
  alpha = 0.5,
  lambda = NULL,
  ...
)

Arguments

expression_profiles

Numeric array \(G\times J\times N\) of purified profiles.

celltype_labels

Character or factor labels of length \(J\) (one per cell-type slice).

alpha

Elastic-net mixing parameter in \([0,1]\) (default 0.5).

lambda

Optional penalty value at which coefficients are extracted. When NULL, uses the smallest \(\lambda\) on the fitted path.

...

Additional arguments forwarded to glmnet::glmnet().

Value

Named numeric vector of length \(G\) (gene scores; larger means stronger multinomial signal).

See also

compute_average_jeffreys(), compute_average_overlap()

Examples

set.seed(1)
G <- 4L
J <- 3L
N <- 12L
profiles <- array(0, dim = c(G, J, N))
for (j in seq_len(J)) {
  profiles[j, j, ] <- 5 + stats::rnorm(N, sd = 0.2)
}
labels <- paste0("ct", seq_len(J))
scores <- compute_glmnet_gene_scores(profiles, labels)
names(scores)[which.max(scores)]
#> [1] "gene_3"