scISR: scISR: Single-cell Imputation using Subspace Regression

In bangtran365/scISR: Single-cell Imputation using Subspace Regression

Description

Perform single-cell Imputation using Subspace Regression

Usage

scISR(data, ncores = 1, seed = 1)

Arguments

data	Input matrix or data frame. Rows represent genes while columns represent samples
ncores	Seed for reproducibility. Default value is 1.

Details

scISR performs imputation for single-cell sequencing data. scISR identifies the true dropout values in the scRNA-seq dataset using hyper-geomtric testing approach. Based on the result obtained from hyper-geometric testing, the original dataset is segregated into two subsets including training data and imputable data. Next, training data is used for constructing a generalize linear regression model that is used for imputation on the imputable data.

Value

scISR returns an imputed single-cell expression matrix where rows represent genes while columns represent samples.

References

1. Nguyen, T., Tagett, R., Diaz, D., & Draghici, S. (2017). A novel approach for data integration and disease subtyping. Genome research, 27(12), 2025-2039.

Examples

# Load the sample dataset scISRExample
data(scISRExample)

# Perform the imputation
imputed <- scISR(data = scISRExample$dropout)

# Plot the complete data
plotscISR(scISRExample$raw, label = scISRExample$celltype)

# Plot the dropout data
plotscISR(scISRExample$dropout, label = scISRExample$celltype)

# Plot the imputed data
plotscISR(imputed, label = scISRExample$celltype)

bangtran365/scISR documentation built on Jan. 20, 2021, 12:24 a.m.