pcaplot: Sample PCA plot for transformed data

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

Sample PCA plot for transformed data

Description

Plots the results of PCA on a 2-dimensional space

Usage

pcaplot(
  x,
  intgroup = NULL,
  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()/vst()
intgroup	Interesting groups: a character vector of names in colData(x) to use for grouping. Defaults to NULL, which would then select the first column of the colData slot
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)

federicomarini/pcaExplorer documentation built on Sept. 29, 2024, 9:31 p.m.