BetaFun: Estimate capture efficiency for cells

In WT215/bayNorm: Single-cell RNA sequencing data normalization

Estimate capture efficiency for cells

Description

This function estimates cell specific capture efficiencies (BETA_vec) using mean raw counts of a subset of genes that is an input for bayNorm. A specific method is used to exclude genes with high expression or high drop-out are excluded.

Usage

BetaFun(Data, MeanBETA)

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.
MeanBETA	Mean capture efficiency of the scRNAseq data. This can be estimated via spike-ins or other methods.

Value

List containing: BETA: a vector of capture efficiencies, which is of length number of cells; Selected_genes: a subset of genes that are used for estimating BETA.

Examples

data('EXAMPLE_DATA_list')
BETA_out<-BetaFun(Data=EXAMPLE_DATA_list$inputdata,
MeanBETA=0.06)

WT215/bayNorm documentation built on Sept. 2, 2022, 1:46 a.m.