estDropoutNum: estDropoutNum: Estimate dropout gene number in a cell

In miaozhun/ImputeSingle: scRecover for imputation of single-cell RNA-seq data

estDropoutNum: Estimate dropout gene number in a cell

Description

This function is used to estimate dropout gene number in a cell for single-cell RNA-seq (scRNA-seq) data. It takes a non-negative vector of scRNA-seq raw read counts of a cell as input.

Usage

estDropoutNum(sample = NULL, depth = 20, histCounts = NULL,
  return = "dropoutNum")

Arguments

sample	A cell's raw read counts for each gene, could be a vector or a SingleCellExperiment object.
depth	Relative sequencing depth to be predicted compared with initial sample depth, should between 0-100, default is 20.
histCounts	Optional. Only needed when sample is blank or sample = NULL. A histogram table of raw read counts for the cell.
return	A character for choosing the return value type of the function. "dropoutNum" (default) for dropout gene number, "geneNumPredict" for all expressed gene number predicted, "transcriptNum" for all transcript number predicted.

Value

The dropout gene number (or all expressed gene number) predicted in a cell.

Author(s)

Zhun Miao.

See Also

scRecover, for imputation of single-cell RNA-seq data.

countsSampling, for downsampling the read counts in a cell.

normalization, for normalization of single-cell RNA-seq data.

scRecoverTest, a test dataset for scRecover.

Examples

# Load test data
data(scRecoverTest)

# Estimate dropout gene number in a cell
estDropoutNum(sample = counts[,1], return = "dropoutNum")

# Estimate all expressed gene number in a cell
estDropoutNum(sample = counts[,1], return = "geneNumPredict")

miaozhun/ImputeSingle documentation built on Jan. 12, 2023, 8:06 p.m.