SCRABBLE: Runs SCRABBLE

In SCRABBLE: Impute Single-Cell RNA-Seq Data Method

Description

This package imputes drop-out data by optimizing an objective function that consists of three terms. The first term ensures that imputed values for genes with nonzero expression remain as close to their original values as possible, thus minimizing unwanted bias towards expressed genes. The second term ensures the rank of the imputed data matrix to be as small as possible. The rationale is that we only expect a limited number of distinct cell types in the samples. The third term operates on the bulk RNA-Seq data. It ensures consistency between the average gene expression of the aggregated imputed data and the average gene expression of the bulk RNA-Seq data. We developed a convex optimization algorithm to minimize the objective function.

Usage

scrabble(data, parameter, nIter = 20, error_out_threshold = 1e-04,
  nIter_inner = 20, error_inner_threshold = 1e-04)

Arguments

data	the input data list. The input data is a list of two datasets, scRNAseq and bulk RNAseq.
parameter	the vector of parameters. The first parameter is the value of alpha in the mathematical model , the second one is the value of beta in the mathematical model.
nIter	the maximum iterations, the default is 20.
error_out_threshold	the threshold of the error between the current imputed matrix and the previous one. Default is 1e-4.
nIter_inner	the maximum interations of calculating the sub-optimization problem. Default is 20.
error_inner_threshold	the threshold of the error between the current updated matrix and the previous one. Default is 1e-4.

Value

A data matrix with the same size of the input scRNAseq data

Examples

# Set up the parameter used in SCRABBLE
parameter <- c(1, 1e-6, 1e-4)

# Run SCRABLE
result <- scrabble(demo_data,parameter = parameter)

SCRABBLE documentation built on May 2, 2019, 2:43 a.m.