bayNorm_sup {bayNorm} | R Documentation

## 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)

[Package bayNorm version 1.8.0 Index]