plotPCA: plotPCA

In IMB-Computational-Genomics-Lab/ascend: ascend - Analysis of Single Cell Expression, Normalisation, and Differential expression

Description

Plot two principal components (PCs) on a scatter plot. This plot corresponds more closely to the distance between points and therefore is good to use to review the effectiveness of clustering by the CORE algorithm.

Usage

plotPCA(object, PCX = 1, PCY = 2, group = NULL, density = FALSE)

Arguments

object	An EMSet object that has undergone PCA.
PCX	Principal component to plot on the x-axis.
PCY	Principal component to plot on the y-axis.
group	(Optional) Name of the column in colInfo that describe a set of conditions you would like to colour cells by.
density	Vary alpha by density (Default: FALSE)

Value

A ggplot glob that contains a scatter plot.

Examples

# Load EMSet
em_set <- ascend::analyzed_set

# Generate PCA plot
pca_plot <- plotPCA(em_set, PCX = 1, PCY = 2, 
group = "cluster", density = TRUE)

IMB-Computational-Genomics-Lab/ascend documentation built on Aug. 29, 2019, 4:10 a.m.