plotscISR: plotscISR: Visualization of scRNA-seq data.

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

Description

Plotting scRNA-seq data to visualize the cell transciptome landscape.

Usage

plotscISR(data, label, perplexity = 30)

Arguments

data	Input matrix or data frame. Rows represent genes while columns represent samples.
label	A vector that contains the cell type labels.
perplexity	Number of closed neighbors for each data point.

Details

This function utilizes the dimension reduction feature of t-SNE package to viusualize high throughput data over two dimensional space.

Value

plotscISR Return the visualization of the cell populations in the scRNA-seq dataset.

References

1. Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(Nov), 2579-2605.

Examples

# Load the sample dataset scISRExample
data(scISRExample)

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

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

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

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

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