noisy_gene_detection: A wrapper function for noisy gene detection from raw data....

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

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.

Description

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.

Usage

noisy_gene_detection(
  Data,
  BETA_vec = NULL,
  mode_version = FALSE,
  mean_version = FALSE,
  S = 20,
  parallel = TRUE,
  NCores = 5,
  FIX_MU = TRUE,
  GR = FALSE,
  BB_SIZE = TRUE,
  verbose = TRUE,
  plot.out = FALSE,
  PRIORS = NULL,
  input_params = NULL
)

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 of cells.
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.
FIX_MU	Whether fix mu when estimating parameters by maximizing marginal distribution. 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_SIZE	If TRUE, estimate BB size, and then use it for adjusting MME SIZE. Use the adjusted MME size for bayNorm. Default is TRUE.
verbose	Print out status messages. Default is TRUE.
plot.out	If TRUE, show CV^2 vs Mean expression plot. Default is FALSE.
PRIORS	(Need to be specified for efficiency if bayNorm has already been applied) A list of estimated prior parameters obtained from bayNorm. Default is NULL.
input_params	(Need to be specified for efficiency if bayNorm has already been applied) A list of input parameters which have been used: BETA_vec, Conditions, UMI_sffl, Prior_type, FIX_MU, BB_SIZE and GR.

Details

A wrapper function for noisy gene detection from raw scRNA-seq data.

Value

A list of objects.

Examples

data("EXAMPLE_DATA_list")
noisy_out<-noisy_gene_detection(Data=
EXAMPLE_DATA_list$inputdata[,seq(1,30)],BETA_vec
=EXAMPLE_DATA_list$inputbeta[seq(1,30)], mode_version = FALSE,
mean_version=FALSE,
S = 20,parallel = FALSE, NCores = 5,
FIX_MU = TRUE, GR = FALSE,
PRIORS=NULL,
BB_SIZE = TRUE,
verbose = TRUE, plot.out = TRUE)

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