Compute summary metrics for estimated proportions

When a ground truth \(\boldsymbol{p}^{\star}\) is supplied, scores compare \(\hat{\boldsymbol{p}}\) to \(\boldsymbol{p}^{\star}\). Otherwise scores compare the reconstituted bulk \(\hat{\boldsymbol{y}}=\boldsymbol{\mu}\hat{\boldsymbol{p}}\) to the observed \(\boldsymbol{y}\).

Usage

compute_benchmark_metrics(
  y,
  mean_signature_matrix,
  estimated_p,
  true_ratios = NULL
)

Arguments

y

Bulk expression vector \(\boldsymbol{y}\in\mathbb{R}^{G}\) (one heterogeneous sample).

mean_signature_matrix

Mean signature \(\boldsymbol{\mu}\in\mathcal{M}_{G\times J}\) (columns = cell types; plug-in for latent profiles).

estimated_p

Estimated proportions \(\hat{\boldsymbol{p}}\in\mathbb{R}^{J}\).

true_ratios

Optional ground-truth proportions \(\boldsymbol{p}^{\star}\in\mathbb{R}^{J}\). When supplied, metrics compare \(\hat{\boldsymbol{p}}\) to \(\boldsymbol{p}^{\star}\); otherwise they compare \(\hat{\boldsymbol{y}}=\boldsymbol{\mu}\hat{\boldsymbol{p}}\) to \(\boldsymbol{y}\).

Value

A tibble with mse/rmse/mae, optionally \(R^{2}\) / adjusted \(R^{2}\), and Pearson correlation.

Examples

mu <- matrix(c(20, 22, 22, 20), nrow = 2,
             dimnames = list(paste0("g", 1:2), paste0("ct", 1:2)))
y <- drop(mu %*% c(0.4, 0.6))
compute_benchmark_metrics(y, mu, estimated_p = c(0.45, 0.55),
                          true_ratios = c(0.4, 0.6))
#> # A tibble: 1 × 6
#>   model_mse model_rmse model_mae model_coef_determination model_coef_determina…¹
#>       <dbl>      <dbl>     <dbl>                    <dbl>                  <dbl>
#> 1   0.00250     0.0500    0.0500                     0.75                   0.75
#> # ℹ abbreviated name: ¹​model_coef_determination_adjusted
#> # ℹ 1 more variable: model_cor <dbl>