Perform a cell-type-specific power analysis on eQTL effects

Usage

powerAnalysis(
  marginal_list,
  marginal_model = NULL,
  refit_formula = NULL,
  geneid = NULL,
  snpid = NULL,
  celltype_colname = "cell_type",
  celltype_vector = NULL,
  celltype_specific_ES_vector = NULL,
  indiv_colname = "indiv",
  method = c("nb", "poisson", "gaussian", "pseudoBulkLinear"),
  nindivs = NULL,
  ncells = NULL,
  nPool = NULL,
  nIndivPerPool = NULL,
  nCellPerPool = NULL,
  alpha = 0.05,
  nsims = 100,
  ncores = 2L,
  parallelization = c("pbmcapply", "future.apply", "parallel", "biocparallel"),
  BPPARAM = NULL,
  future.seed = FALSE,
  data_maxsize = 1
)

Arguments

marginal_list: the output of function fitMarginalPop().
marginal_model: a character showing the model types of the full marginal model.
refit_formula: the formula used to refit the marginal full model if user wants to. Default is null.
geneid: a character object contains geneid.
snpid: a character object contains snpid.
celltype_colname: a string scalar specifying the cell state variable in marginal_list[[geneid]]$frame. The default is "cell_type".
celltype_vector: a vector object specifies the cell type that will be tested
celltype_specific_ES_vector: a vector object specifies the genotype effect size (ES) for each cell type
indiv_colname: a string scalar of the sample ID variable in cell covariate of marginal_list[[geneid]]$frame. The default is "indiv".
method: a character object specifying the method that will be analyzed for power. (Options: nb,poisson,gaussian,pseudoBulkLinear).
nindivs: a vector of numeric values showing the numbers of individuals that user wants to simulate.
ncells: a vector of numeric values showing the numbers of cells per each individual that user wants to simulate.
nPool: a vector of numeric values showing how many pools of sequencing has been performed.
nIndivPerPool: a numerical value showing how many individuals are sequenced in one pool.
nCellPerPool: a vector of numeric values showing how many cells are sequenced in one pool.
alpha: the p value threshold for rejecting the H0 hypothesis.
nsims: number of simulations for calculating the power. This parameter will affect the resolution of the power value.
ncores: a positive integer value (greater or equal to 1) to specify the number of CPU cores used in parallelization. The default is 2.
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 "pbmcapply". 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.

Value

a list of named features, each containing a list with the following items:

intercept: the intercept for the genotype effect in the specified type/level.
slope: the slope for the genotype effect in the specified type/level.
power: a data frame contains power values in different parameter settings.
data: a data frame contains both the H1 and H0 genotype effect estimates in different parameter settings and simulation times.

Examples

NULL
#> NULL