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Gene-wise posterior + test statistic + p-value without storing posterior matrices

compute_test_per_gene.Rd

This function reproduces the behavior of compute_posterior(), compute_test_statistic(), and compute_pvalue(), but it computes everything one gene at a time without ever allocating an n x p posterior mean/variance matrix.

Usage

compute_test_per_gene(
  input_obj,
  alpha_max = 1000,
  bool_adjust_covariates = FALSE,
  bool_covariates_as_library = TRUE,
  bool_stabilize_underdispersion = TRUE,
  library_min = 0.01,
  nuisance_lower_quantile = 0.01,
  pseudocount = 0,
  verbose = 0
)

Arguments

input_obj

eSVD object after nuisance estimation.

alpha_max

Maximum value of the prior mean (same role as in compute_posterior.eSVD); default 1e3.

bool_adjust_covariates

Boolean; if TRUE, adjust the prior by confounding covariates (same as in compute_posterior.default).

bool_covariates_as_library

Boolean; if TRUE, include non–case-control covariates in the covariate-adjusted library size.

bool_stabilize_underdispersion

Boolean; if TRUE, mean-center log10(nuisance_vec) when it suggests under-dispersion.

library_min

Minimum value for the covariate-adjusted library size.

nuisance_lower_quantile

Lower quantile at which to floor nuisance_vec.

pseudocount

Numeric; additional count added to each entry in the count matrix when forming the posterior.

verbose

Integer; controls printed messages.

Value

The input eSVD object with:

  • teststat_vec — Welch t-statistics (length p).

  • case_mean — case Gaussian means per gene.

  • control_mean — control Gaussian means per gene.

  • pvalue_list — list with df_vec, fdr_vec, gaussian_teststat, log10pvalue, null_mean, null_sd.