smoothRWR: Imputing dropout values of scRNA-seq data.
In BMILAB/scNPF: Pre-processing of single-cell RNA-seq data

Description Usage Arguments Value

View source: R/scNPF_pro.R

scNPF-propagation for imputing dropouts and correcting expression expression measurements.scNPF-propagation involves a network propagation process based on random walk with restart (RWR) on a given gene-gene interaction network to obtain a distribution for each node (gene), which captures its relevance to all other genes in the network.

1	smoothRWR(x, network = "context", gamma = 0.5, nThreads = 1)

`x`	A expression count matrix. The rows correspond to genes and the columns correspond to cells.
`network`	A adjacency matrix contation gene-gene interaction network.
`nThreads`	The number of cores to use. Default is 1.
`gammma`	A number between 0 and 1 (default: 0.5). `gamma` is the trade-off between prior information and network diffusion, governing the distance that a signal is allowed to diffuse through the network during smoothing. The specific value of `gamma` has little effect on the results of network propagation over a sizable range.