pcaExplorer

pcaExplorer performs interactive principal component analysis and exploratory visualization of RNA sequencing (RNA-seq) datasets to identify gene expression patterns and support reproducible analyses.

Key Features:

• R/Bioconductor implementation: Provided as an R/Bioconductor package for analysis of RNA sequencing (RNA-seq) data.
• Principal Components Analysis (PCA): Applies PCA for dimensionality reduction and visualization of high-dimensional RNA-seq datasets.
• Interactive visualization: Enables dynamic exploration of PCA plots and gene expression patterns to examine structure and variability in the data.
• Reproducibility: Exposes underlying code and data to ensure transparency and reproducibility of analyses.

Scientific Applications:

• Exploratory data analysis: Identify expression patterns, sample relationships, and structure within high-dimensional RNA-seq datasets.
• Outlier detection and quality assessment: Detect sample outliers and assess variability across samples or conditions.
• Reproducible research: Facilitate sharing and validation of RNA-seq analyses through access to underlying code and data.

Methodology:

Principal Components Analysis (PCA) is used for dimensionality reduction and visualization of RNA-seq high-dimensional data.