bayNorm_sup: bayNorm with estimated parameters as input

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

bayNorm with estimated parameters as input

Description

This is a supplementary wrapper function for bayNorm. It is useful if one has already estimated prior parameters and wants to simulate 2D or 3D normalized output using the same prior estimates.

Usage

bayNorm_sup(
  Data,
  PRIORS = NULL,
  input_params = NULL,
  mode_version = FALSE,
  mean_version = FALSE,
  S = 20,
  parallel = TRUE,
  NCores = 5,
  BB_SIZE = TRUE,
  verbose = TRUE,
  out.sparse = 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.
PRIORS	A list of estimated prior parameters obtained from bayNorm.
input_params	A list of input parameters which have been used: BETA_vec, Conditions, UMI_sffl, Prior_type, FIX_MU, BB_SIZE and GR.
mode_version	If TRUE, bayNorm return mode version normalized data which is of 2D matrix instead of 3D array. Default is FALSE.
mean_version	If TRUE, bayNorm return mean version normalized data which is of 2D matrix instead of 3D array. Default is FALSE.
S	The number of samples you would like to generate from estimated posterior distribution (The third dimension of 3D array). Default is 20. S needs to be specified if mode_version=FALSE.
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.
BB_SIZE	If TRUE (default), use adjusted size for normalization. The adjusted size is obtained by adjusting MME estimated size by a factor. The factor is calculated based on both MME estimated size and BB estimated size. If FALSE, use MME estimated SIZE.
verbose	print out status messages. Default is TRUE.
out.sparse	Only valid for mean version: Whether the output is of type dgCMatrix or not. Default is FALSE.

Details

If you have run bayNorm before and obtained a list of estimated prior parameters, then you may not want to run parameter estimation again. You can just use previous estimated parameters for obtaining 3D or 2D normalized data.

Value

List containing 3D arrays of normalized expression (if mode_version=FALSE) or 2D matrix of normalized expression (if mode_version=TRUE or mean_version=TRUE), a list contains estimated priors and a list contains input parameters used: BETA_vec, Conditions (if specified), UMI_sffl (if specified), Prior_type, FIX_MU, BB_SIZE and GR.

References

Wenhao Tang, Francois Bertaux, Philipp Thomas, Claire Stefanelli, Malika Saint, Samuel Blaise Marguerat, Vahid Shahrezaei bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data Bioinformatics, btz726; doi: 10.1093/bioinformatics/btz726

Examples

data('EXAMPLE_DATA_list')
#Return 3D array normalzied data:
bayNorm_3D<-bayNorm(
Data=EXAMPLE_DATA_list$inputdata[,seq(1,30)],
BETA_vec = EXAMPLE_DATA_list$inputbeta[seq(1,30)]
,mode_version=FALSE,parallel =FALSE)

#Now if you want to generate 2D matrix using the same prior
#estimates as generated before:
bayNorm_2D<-bayNorm_sup(
Data=EXAMPLE_DATA_list$inputdata[,seq(1,30)]
,PRIORS=bayNorm_3D$PRIORS,
input_params = bayNorm_3D$input_params
,mode_version=TRUE)

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