BB_Fun {bayNorm} | R Documentation

## Estimating size for each gene by either 1D or 2D maximization of marginal distribution

### Description

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)

### Usage

    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
    )

### Arguments

`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.

### Value

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

### Examples

    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)

[Package bayNorm version 1.6.0 Index]