PCAtools

PCAtools provides functions for data exploration via Principal Component Analysis (PCA) and enables generation of publication-ready figures for high-dimensional biological data.

Key Features:

• PCA Data Exploration: Performs PCA via BiocSingular to extract fundamental data structure through dimensionality reduction into uncorrelated principal components.
• Optimal Component Identification: Identifies the optimal number of principal components via the elbow method and Horn's parallel analysis.
• Publication-Ready Visualization: Generates publication-ready figures for PCA results.

Scientific Applications:

• Single-Cell RNA Sequencing: Supports data reduction in single-cell RNA-seq (scRNA-seq) through optimal principal component selection.
• High-Dimensional Mass Cytometry: Applies dimensionality reduction for high dimensional mass cytometry data analysis.
• General Bioinformatics Data Analysis: Facilitates exploration of relationships and differences between logical entities in large biological datasets.

Methodology:

PCA is performed via BiocSingular. The optimal number of principal components is determined using the elbow method and Horn's parallel analysis.