rna.plot_pca: Plot Results of Principal Component Analysis

In NicWir/VisomX: User-Friendly and Comprehensive Analysis and Visualization of Omics Data

Plot Results of Principal Component Analysis

Description

Performs 'regularized log' transformation followed by PCA on a given DESeqDataSet object and plots the results.

Usage

rna.plot_pca(
  dds,
  x = 1,
  y = 2,
  indicate = c("condition", "replicate"),
  title = NULL,
  label = FALSE,
  n = ncol(t(SummarizedExperiment::assay(dds))),
  point_size = 4,
  label_size = 3,
  hline = 0,
  hlineType = "longdash",
  hlineCol = "black",
  hlineWidth = 0.4,
  vline = 0,
  vlineType = "longdash",
  vlineCol = "black",
  vlineWidth = 0.4,
  basesize = 15,
  plot = TRUE,
  export = TRUE
)

Arguments

x	x-axis PC (default: 1)
y	y-axis PC (default: 2)
indicate	features to indicate in the plot ("condition" or "replicate")
title	title of the plot (default: "PCA plot - top n variable genes")
label	whether to label points in the plot (default: FALSE)
n	number of variables to take into account, sorted according to their variance in descending order. Only the n most variable genes are used to perform PCA. default: number of columns in dep)
point_size	size of points in the plot (default: 4)
label_size	size of labels in the plot (default: 3)
hline	position of horizontal line (default: 0)
hlineType	type of horizontal line (default: 'longdash')
hlineCol	color of horizontal line (default: 'black')
hlineWidth	width of horizontal line (default: 0.4)
vline	position of vertical line (default: 0)
vlineType	type of vertical line (default: 'longdash')
vlineCol	color of vertical line (default: 'black')
vlineWidth	width of vertical line (default: 0.4)
basesize	base size of the plot (default: 15)
plot	whether to return the PCA plot (default: TRUE)
export	whether to export the PCA plot to the Plots directory as PNG and PDF file (default: TRUE)
dep	DESeqDataSet object

Value

(invisibly) a data frame containing the PCA coordinates

NicWir/VisomX documentation built on Dec. 8, 2024, 1:27 a.m.