ImputeNetwork {ADImpute} | R Documentation

## Network-based imputation

### Description

Network-based imputation

### Usage

    ImputeNetwork(data, net.coef = NULL,
    cores = BiocParallel::bpworkers(BPPARAM),
    BPPARAM = BiocParallel::SnowParam(type = "SOCK"),
    type = 'iteration', write = FALSE, ...)

### Arguments

`data` | matrix with entries equal to zero to be imputed, normalized and log2-transformed (genes as rows and samples as columns)
`net.coef` | matrix; network coefficients.
`cores` | integer; number of cores to use
`BPPARAM` | parallel back-end to be used during parallel computation. See `BiocParallelParam-class`.
`type` | character; either 'iteration', for an iterative solution, or 'pseudoinv', to use Moore-Penrose pseudo-inversion as a solution.
`write` | logical; should a file with the imputation results be written?
`...` | additional arguments to `ImputeNetParallel`

### Details

Imputes dropouts using a gene regulatory network trained on external data, as provided in `net.coef`. Dropout expression values are estimated from the expression of their predictor genes and the network coefficients.

### Value

matrix; imputation results incorporating network information

### See Also

`ImputeNetParallel`

[Package ADImpute version 1.0.0 Index]