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Extract the parameters of each cell's distribution

Source: R/extract_para.R
extract_para.Rd

extract_para generates parameter matrices which determine each cell's distribution

Usage

extract_para(
  sce,
  assay_use = "counts",
  marginal_list,
  n_cores,
  family_use,
  new_covariate,
  parallelization = "mcmapply",
  BPPARAM = NULL,
  data
)

Arguments

sce

A SingleCellExperiment object.

assay_use

A string which indicates the assay you will use in the sce. Default is 'counts'.

marginal_list

A list of fitted regression models from fit_marginal for each gene in sce.

n_cores

An integer. The number of cores to use.

family_use

A string of the marginal distribution. Must be one of 'poisson', 'nb', 'zip', 'zinb' or 'gaussian', which represent 'poisson distribution', 'negative binomial distribution', 'zero-inflated poisson distribution', 'zero-inflated negative binomial distribution', and 'gaussian distribution' respectively.

new_covariate

A data.frame which contains covariates of targeted simulated data from construct_data and the correlation group assignment for each cell in the column 'corr_group'.

parallelization

A string indicating the specific parallelization function to use. Must be one of 'mcmapply', 'bpmapply', or 'pbmcmapply', which corresponds to the parallelization function in the package parallel,BiocParallel, and pbmcapply respectively. The default value is 'mcmapply'.

BPPARAM

A MulticoreParam object or NULL. When the parameter parallelization = 'mcmapply' or 'pbmcmapply', this parameter must be NULL. When the parameter parallelization = 'bpmapply', this parameter must be one of the MulticoreParam object offered by the package 'BiocParallel. The default value is NULL.

data

A dataframe which is used when fitting the gamlss model

Value

A list with the components:

mean_mat

A cell by feature matrix of the mean parameter.

sigma_mat

A cell by feature matrix of the sigma parameter (for Gaussian, the variance; for NB, the dispersion.).

zero_mat

A cell by feature matrix of the zero-inflation parameter (only non-zero for ZIP and ZINB).

Details

The function takes the new covariate (if use) from construct_data and marginal models from fit_marginal.

Examples

  data(example_sce)
  my_data <- construct_data(
  sce = example_sce,
  assay_use = "counts",
  celltype = "cell_type",
  pseudotime = "pseudotime",
  spatial = NULL,
  other_covariates = NULL,
  corr_by = "1"
  )
  my_marginal <- fit_marginal(
  data = my_data,
  mu_formula = "s(pseudotime, bs = 'cr', k = 10)",
  sigma_formula = "1",
  family_use = "nb",
  n_cores = 1,
  usebam = FALSE
  )
  my_copula <- fit_copula(
  sce = example_sce,
  assay_use = "counts",
  marginal_list = my_marginal,
  family_use = c(rep("nb", 5), rep("zip", 5)),
  copula = "vine",
  n_cores = 1,
  input_data = my_data$dat
  )
#> Convert Residuals to Uniform
#> Converting End
#> Copula group 1 starts
#> Vine Copula Estimation Starts
#> Time difference of 0.1801219 secs
#> Vine Copula Estimation Ends
  my_para <- extract_para(
    sce = example_sce,
    marginal_list = my_marginal,
    n_cores = 1,
    family_use = c(rep("nb", 5), rep("zip", 5)),
    new_covariate = my_data$new_covariate,
    data = my_data$dat
  )