noisy_gene_detection {bayNorm} | R Documentation

## 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)

[Package bayNorm version 1.6.0 Index]