Parametric bootstrap for one single-cell bulk sample

Draws \(\boldsymbol{Y}^{(b)}\sim\mathcal{N}(\hat c\tilde{\boldsymbol{\mu}} \hat{\boldsymbol{p}},\hat\phi\boldsymbol{S}_W(\hat{\boldsymbol{p}}))\) and refits the profile model. Percentile intervals are returned for the composition and for the two scales. The reference moments stay fixed.

Usage

bootstrap_single_cell(
  y,
  mean_signature_matrix,
  Sigma,
  n_boot = 200L,
  level = 0.95,
  epsilon = 1e-06,
  itmax = 40L,
  optimiser = c("newton", "lbfgsb")
)

Arguments

y

Bulk vector of length \(G\).

mean_signature_matrix

Reference mean, \(G\times J\).

Sigma

Cell-level covariances, \(G\times G\times J\).

n_boot

Number of replicates.

level

Confidence level.

epsilon

Relative function and step tolerances passed to stats::nlminb().

itmax

Maximum number of iterations.

optimiser

Passed to deconvolute_single_cell() for the fit and every refit, so the bootstrap matches the estimator being assessed.

Value

A list with estimates, interval, n_failed (refits that errored, left as NA columns), scales, scale_interval and fit. scales and scale_interval have rows mean_scale (\(c\)), variance_scale (\(\phi\)), platform_scale_a (\(a=\phi/c\)) and cell_count_n (\(n=c^{2}/\phi\)).

See also

confint_single_cell(), bootstrap_decovart()

Other decovart_inference: bootstrap_decovart(), boundary_diagnostics(), chi_bar_square_pvalue(), confint_decovart(), confint_ilr_backtransform(), confint_profile_decovart(), confint_profile_single_cell(), confint_single_cell(), equivariance_check_decovart(), fit_firth_decovart(), fit_firth_single_cell(), lrt_decovart(), lrt_single_cell(), multistart_decovart(), reference_bootstrap_decovart(), reference_bootstrap_single_cell(), restricted_mle_decovart(), restricted_mle_single_cell()

Examples

mu <- matrix(c(2, 0.2, 0.2, 2, 0.2, 3, 0.2, 3), nrow = 4)
colnames(mu) <- c("ct1", "ct2")
sigma <- array(rep(diag(0.05, 4), 2), c(4, 4, 2))
y <- 20 * drop(mu %*% c(0.7, 0.3)) + c(0.3, -0.2, 0.1, -0.1)
set.seed(1)
bootstrap_single_cell(y, mu, sigma, n_boot = 5)$interval
#>          2.5%     97.5%
#> ct1 0.7045195 0.7066303
#> ct2 0.2933697 0.2954805