BB_Fun: Estimating size for each gene by either 1D or 2D maximization...

In WT215/bayNorm: Single-cell RNA sequencing data normalization

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)

WT215/bayNorm documentation built on Sept. 2, 2022, 1:46 a.m.