Skip to contents

Compute posterior according to Gamma-Poisson model for eSVD object

compute_posterior.eSVD.Rd

The posterior is computed based on whatever input_obj$latest_Fit is set to.

Usage

# S3 method for class 'eSVD'
compute_posterior(
  input_obj,
  alpha_max = 1000,
  bool_adjust_covariates = F,
  bool_covariates_as_library = T,
  bool_return_components = F,
  bool_stabilize_underdispersion = T,
  library_min = 0.1,
  nuisance_lower_quantile = 0.01,
  pseudocount = 0,
  ...
)

Arguments

input_obj

eSVD object outputed from opt_esvd.eSVD.

alpha_max

Maximum value of numerator when computing posterior, default is 1e3.

bool_adjust_covariates

Boolean to adjust the numerator in the posterior by the donor covariates, default is FALSE. This parameter is experimental, and we have not yet encountered a scenario where it is useful to be set to be TRUE.

bool_covariates_as_library

Boolean to include the donor covariates effects in the adjusted library size, default is TRUE

bool_return_components

Boolean to return the numerator and denominator of the posterior terms as well (which will themselves by matrices that are cell-by-gene matrices), default is FALSE

bool_stabilize_underdispersion

Boolean to stabilize the over-dispersion parameter, specifically to rescale all the over-dispersions the global mean over-disperion is less than 1, default is TRUE

library_min

All covariate-adjusted library size smaller than this value are set to this value, default is 0.1.

nuisance_lower_quantile

All the nuisance values that are smaller than this quantile are set to this quantile, default is 0.01

pseudocount

The additional count that is added to the count matrix, default is 0.

...

Additional parameters.

Value

eSVD object with posterior_mean_mat and posterior_var_mat appended to the list in input_obj[[input_obj[["latest_Fit"]]]].