BB_Fun: Estimating size for each gene by either 1D or 2D maximization...
In bayNorm: Single-cell RNA sequencing data normalization

Description Usage Arguments Value Examples

Estimating parameters of the prior distribution for each gene by maximizing marginal distribution: 1D (optimize with respect to size using MME estimate of mu, 2D (optimize with respect to both mu and size)

BB_Fun(
  Data,
  BETA_vec,
  INITIAL_MU_vec,
  INITIAL_SIZE_vec,
  MU_lower = 0.01,
  MU_upper = 500,
  SIZE_lower = 0.01,
  SIZE_upper = 30,
  parallel = FALSE,
  NCores = 5,
  FIX_MU = TRUE,
  GR = FALSE
)

`Data`	A matrix of single-cell expression where rows are genes and columns are samples (cells). `Data` can be of class `SummarizedExperiment` (the assays slot contains the expression matrix, is named "Counts"), just `matrix` or sparse matrix.
`BETA_vec`	A vector of capture efficiencies (probabilities) of cells.
`INITIAL_MU_vec`	Mean expression of genes, can be estimated from `EstPrior`.
`INITIAL_SIZE_vec`	size of genes (size is a parameter in NB distribution), can come from EstPrior.
`MU_lower`	The lower bound for the mu.(Only need it when you want to do 2D optimization). Default is 0.01.
`MU_upper`	The upper bound for the mu.(Only need it when you want to do 2D optimization). Default is 500.
`SIZE_lower`	The lower bound for the size. Default is 0.01.
`SIZE_upper`	The upper bound for the size. Default is 30.
`parallel`	If TRUE, `NCores` cores will be used for parallelization. Default is TRUE.
`NCores`	number of cores to use, default is 5. This will be used to set up a parallel environment using either MulticoreParam (Linux, Mac) or SnowParam (Windows) with NCores using the package BiocParallel.
`FIX_MU`	If TRUE, then 1D optimization, otherwise 2D optimization (slow).
`GR`	If TRUE, the gradient function will be used in optimization. However since the gradient function itself is very complicated, it does not help too much in speeding up. Default is FALSE.

BB estimated size (1D optimization) or size and mu (2D optimization).

data('EXAMPLE_DATA_list')
BB_RESULT<-BB_Fun(Data=EXAMPLE_DATA_list$inputdata[,seq(1,30)],
BETA_vec = EXAMPLE_DATA_list$inputbeta[seq(1,30)],
INITIAL_MU_vec=EXAMPLE_DATA_list$mu,
INITIAL_SIZE_vec=EXAMPLE_DATA_list$size,
MU_lower=0.01,MU_upper=500,SIZE_lower=0.01,
SIZE_upper=30,parallel=FALSE,NCores=5,FIX_MU=TRUE,GR=FALSE)