Single-cell RNA sequencing data normalization

Documentation for package ‘bayNorm’ version 1.8.0

AdjustSIZE_fun	Adjust MME size estimate
asMatrix	Rcpp version: as.matrix
as_matrix	Rcpp version's as.matrix
bayNorm	A wrapper function of prior estimation and bayNorm function
bayNorm_sup	bayNorm with estimated parameters as input
BB_Fun	Estimating size for each gene by either 1D or 2D maximization of marginal distribution
BetaFun	Estimate capture efficiency for cells
Check_input	Check input
DownSampling	Binomial downsampling
EstPrior	Estimate size and mu for Negative Binomial distribution for each gene using MME method
EstPrior_rcpp	Estimate size and mu for Negative Binomial distribution for each gene using MME method (Rcpp version)
EstPrior_sprcpp	Estimate size and mu for Negative Binomial distribution for each gene using MME method (Rcpp version, sp_mat)
EXAMPLE_DATA_list	A subset of Grun et al (2014) data: 2i samples
NOISY_FUN	Noisy gene detection
noisy_gene_detection	A wrapper function for noisy gene detection from raw data. his produces synthetic control, performs bayNorm on both real cell data and synthetic controls and does noisy gene detection.
Prior_fun	A wrapper function of 'EstPrior' and 'AdjustSIZE_fun'
SyntheticControl	Generate synthetic control
t_sp	Transpose of sparse matrix