pcaplot: Sample PCA plot for transformed data

In pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach

Description

Plots the results of PCA on a 2-dimensional space

Usage

pcaplot(
  x,
  intgroup = "condition",
  ntop = 500,
  returnData = FALSE,
  title = NULL,
  pcX = 1,
  pcY = 2,
  text_labels = TRUE,
  point_size = 3,
  ellipse = TRUE,
  ellipse.prob = 0.95
)

Arguments

x	A DESeqTransform object, with data in assay(x), produced for example by either rlog or varianceStabilizingTransformation
intgroup	Interesting groups: a character vector of names in colData(x) to use for grouping
ntop	Number of top genes to use for principal components, selected by highest row variance
returnData	logical, if TRUE returns a data.frame for further use, containing the selected principal components and intgroup covariates for custom plotting
title	The plot title
pcX	The principal component to display on the x axis
pcY	The principal component to display on the y axis
text_labels	Logical, whether to display the labels with the sample identifiers
point_size	Integer, the size of the points for the samples
ellipse	Logical, whether to display the confidence ellipse for the selected groups
ellipse.prob	Numeric, a value in the interval [0;1)

Value

An object created by ggplot, which can be assigned and further customized.

Examples

dds <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3, betaSD_tissue = 1)
rlt <- DESeq2::rlogTransformation(dds)
pcaplot(rlt, ntop = 200)

pcaExplorer documentation built on Nov. 8, 2020, 5:29 p.m.