V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) provides high-resolution gene expression data.
• Identifying and separating biological variation from technical artifacts (e.g., batch effects) is crucial for accurate scRNA-seq analysis.
• Surrogate variable analysis (SVA) methods are used for batch correction and identifying biological variation, but interpretation remains challenging.

Purpose of the Study:

• To develop an accessible tool for interpreting sources of variation in scRNA-seq data.
• To facilitate the distinction between biological and technical variation in scRNA-seq datasets.
• To enhance the annotation and understanding of surrogate variables derived from scRNA-seq data.

Main Methods:

• Development of an R Shiny application named Visual Surrogate Variable Analysis (V-SVA).
• Integration of tools for identifying genes associated with detected sources of variation.
• Incorporation of gene annotation using public databases and gene sets.
• Implementation of data visualization techniques, including dimension reduction.

Main Results:

• V-SVA provides an interactive web-based platform for exploring hidden variation in scRNA-seq data.
• The application aids in annotating and understanding the nature of identified surrogate variables.
• It supports the discovery of biologically relevant genes linked to specific variation patterns.

Conclusions:

• V-SVA offers a user-friendly interface to improve the interpretation of variation in scRNA-seq data.
• The tool assists researchers in distinguishing technical noise from biological signals.
• This facilitates more robust downstream analyses and biological discoveries from scRNA-seq experiments.