pcaplot3d: 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 3-dimensional space, interactively

Usage

pcaplot3d(
  x,
  intgroup = "condition",
  ntop = 500,
  returnData = FALSE,
  title = NULL,
  pcX = 1,
  pcY = 2,
  pcZ = 3,
  text_labels = TRUE,
  point_size = 3
)

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
pcZ	The principal component to display on the z axis
text_labels	Logical, whether to display the labels with the sample identifiers
point_size	Integer, the size of the points for the samples

Value

A html-based visualization of the 3d PCA plot

Examples

dds <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3, betaSD_tissue = 1)
rlt <- DESeq2::rlogTransformation(dds)
pcaplot3d(rlt, ntop = 200)

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