Package index • eSVD2

All functions

compute_posterior(): Compute posterior according to Gamma-Poisson model

compute_posterior(<default>): Compute posterior according to Gamma-Poisson model for matrices and sparse matrices.

compute_posterior(<eSVD>): Compute posterior according to Gamma-Poisson model for eSVD object

compute_pvalue(): Compute p-values

compute_test_statistic(): Compute test statistics

compute_test_statistic(<default>): Compute test statistics for matrices

compute_test_statistic(<eSVD>): Compute test statistics for eSVD object

data_loader(): Internal data loader function

.compute_df(): Compute the degree of freedom

.reparameterize(): Function to reparameterize two matrices

estimate_nuisance(): Estimate nuisance values

estimate_nuisance(<default>): Estimate nuisance values for matrix or sparse matrices.

estimate_nuisance(<eSVD>): Estimate nuisance values for eSVD objects (i.e., over-dispersion)

esvd_family(): Internal Constructor for distribution family

fisher_test(): Fisher's exact test

format_covariates(): Format covariates

gandal_df: Gandal et al. results

generate_data(): Generate data

generate_null(): Generate null data

housekeeping_df: Hounkpe et al. housekeeping genes

initialize_esvd(): Initialize eSVD

multtest(): Perform multiple-testing adjustment using Efron's empirical null

opt_esvd(): Optimize eSVD

opt_esvd(<default>): Optimize eSVD for matrices or sparse matrices.

opt_esvd(<eSVD>): Optimize eSVD for eSVD objects

opt_x(): Optimize X given C, Y and Z

opt_yz(): Optimize Y and Z given X and C

print(<esvd_data_loader>): Internal data loader function

reparameterization_esvd_covariates(): Reparameterize eSVD object

sfari_df: SFARI genes

velmeshev_gene_df: Velmeshev et al. DEGs