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Simulate new data

Source: R/simuNewPop.R
simuNewPop.Rd

simuNewPop generates new simulated data based on fitted marginal and copula models. This function is adapted from simu_new function in scDesign3 v0.99.7

Usage

simuNewPop(
  sce,
  assay_use = "counts",
  mean_mat,
  sigma_mat,
  zero_mat,
  quantile_mat = NULL,
  copula_list,
  n_cores = 2L,
  fastmvn = FALSE,
  family_use,
  nonnegative = TRUE,
  nonzerovar = FALSE,
  input_data,
  new_covariate,
  important_feature = "all",
  parallelization = c("pbmcapply", "future.apply", "parallel", "biocparallel"),
  BPPARAM = NULL,
  future.seed = FALSE,
  data_maxsize = 1,
  filtered_gene,
  mean_limit = 1e+15,
  debug = FALSE,
  ...
)

Arguments

sce

A SingleCellExperiment object.

assay_use

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

mean_mat

A cell by feature matrix of the mean parameter.

sigma_mat

A cell by feature matrix of the sigma parameter.

zero_mat

A cell by feature matrix of the zero-inflation parameter.

quantile_mat

A cell by feature matrix of the multivariate quantile.

copula_list

A list of copulas for generating the multivariate quantile matrix. If provided, the quantile_mat must be NULL.

n_cores

a positive integer value (greater or equal to 1) to specify the number of CPU cores used in parallelization. The default is 2.

fastmvn

An logical variable. If TRUE, the sampling of multivariate Gaussian is done by mvnfast, otherwise by mvtnorm. Default is FALSE.

family_use

A string of the marginal distribution. Must be one of 'poisson', 'nb', or 'gaussian'.

nonnegative

A logical variable. If TRUE, values < 0 in the synthetic data will be converted to 0. Default is TRUE (since the expression matrix is nonnegative).

nonzerovar

A logical variable. If TRUE, for any gene with zero variance, a cell will be replaced with 1. This is designed for avoiding potential errors, for example, PCA.

input_data

A input count matrix.

new_covariate

A data.frame which contains covariates of targeted simulated data from constructDataPop.

important_feature

important_feature A string or vector which indicates whether a gene will be used in correlation estimation or not. If this is a string, then this string must be either "all" (using all genes) or "auto", which indicates that the genes will be automatically selected based on the proportion of zero expression across cells for each gene. Gene with zero proportion greater than 0.8 will be excluded form gene-gene correlation estimation. If this is a vector, then this should be a logical vector with length equal to the number of genes in sce. TRUE in the logical vector means the corresponding gene will be included in gene-gene correlation estimation and FALSE in the logical vector means the corresponding gene will be excluded from the gene-gene correlation estimation. The default value for is "all".

parallelization

a string scalar specifying the parallelization backend used when simulating data. Must be one of "parallel", "future.apply", "biocparallel", or "pbmcapply". The default value is "parallel". See details.

BPPARAM

a BiocParallelParam class object (from BiocParallel R package) that must be specified when using parallelization = "biocparallel". Either BiocParallel::SnowParam() or BiocParallel::MulticoreParam() can be used to initialize, depending on the operating system. BPPARAM is not used in other parallelization options. The default is NULL.

future.seed

a logical or an integer (of length one or seven), or a list of length(X) with pre-generated random seeds that can be specified when using parallelization = "future.apply". See future.apply::future_eapply documentation for more details on its usage. future.seed is not used in other parallelization options. The default is FALSE.

data_maxsize

a positive numeric value used to set max marginal_list size in GiB increments. Used only when parallelization = "future.apply". The default is 1.

filtered_gene

A vector or NULL which contains genes that are excluded in the marginal and copula fitting

mean_limit

A numeric scalar to filter genes which has cells that exceed the limit in the mean_mat. The default value is 1e15. This is to avoid features that have extremely high and unreasonable means.

debug

A logical scalar for whether to return a list of variables in addition to simulated count matrix. The default is FALSE.

...

additional arguments passed to internal functions.

Value

A feature by cell matrix of the new simulated count (expression) matrix or sparse matrix.

Details

The function takes the new covariate (if use) from constructDataPop, parameter matrices from extractParaPop and multivariate Unifs from fitCopulaPop.

Parallelization options

If "parallel" is used then mcmapply is called from the parallel package; if "biocparallel" is used, then bpmapply is called from the BiocParallel package; if "future.apply" is used, then future_mapply is called from the future.apply package; if "pbmcapply" is used, then pbmcmapply is called from the pbmcapply package.

Examples

NULL
#> NULL