pcaPlot: pcaPlot

In inDAGO: A GUI for Dual and Bulk RNA-Sequencing Analysis

pcaPlot

Description

Create a PCA scatter plot from log-expression data with sample labels.

Usage

pcaPlot(
  logcounts,
  Sample,
  Group,
  title,
  palette,
  maxOverlaps,
  sizeLabel,
  center,
  scale
)

Arguments

logcounts	Numeric matrix of log-CPM values (genes × samples), e.g., from edgeR::cpm.
Sample	Character vector of sample names corresponding to the columns of "logcounts".
Group	Factor or character vector denoting group/condition for each sample.
title	Character. Title for the PCA plot.
palette	Character. Name of a discrete color palette from the "paletteer" package.
maxOverlaps	Integer. Maximum number of overlapping labels allowed by "ggrepel".
sizeLabel	Numeric. Font size for sample labels.
center	Logical. If TRUE, center variables before PCA.
scale	Logical. If TRUE, scale variables to unit variance before PCA.

Details

This function performs Principal Component Analysis (PCA) on a log-count matrix and visualizes the first two principal components using ggplot2 and ggrepel. Each point represents a sample, colored by group, with hover labels.

1. Transposes the "logcounts" matrix so samples are rows.

2. Runs PCA via "stats::prcomp()" with centering and scaling options.

3. Calculates percent variance explained by PC1 and PC2.

4. Builds a scatter plot with black‐bordered points and non‐overlapping labels.

Value

A "ggplot" object displaying the PCA scatter plot of PC1 vs PC2.

inDAGO documentation built on Aug. 8, 2025, 7:47 p.m.