SCALA: A complete solution for multimodal analysis of single-cell Next Generation Sequencing data.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Analyzing single-cell transcriptional and chromatin accessibility data presents significant challenges.
• Numerous bioinformatics tools complicate data interpretation and hinder biological insight extraction.

Purpose of the Study:

• To introduce SCALA, a bioinformatics tool for single-cell RNA sequencing (scRNA-seq) and Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) data analysis.
• To enable independent or integrative analysis of scRNA-seq and scATAC-seq datasets.
• To assist biomedical researchers in data exploration, analysis, and visualization.

Main Methods:

• SCALA integrates multiple software packages for quality control, cell population identification, and cell state analysis.
• It offers functional enrichment, cellular trajectory inference, ligand-receptor analysis, and regulatory network reconstruction.
• The tool provides parameterizable modules and publication-ready visualizations.

Main Results:

• SCALA facilitates comprehensive analysis of scRNA-seq and scATAC-seq data.
• It successfully handles both modalities for integrated analysis.
• Demonstrated utility through use-cases involving TNF-driven arthritic mice.

Conclusions:

• SCALA empowers researchers to analyze complex single-cell data without extensive coding experience.
• The tool streamlines the process of deriving biological insights from multi-modal single-cell datasets.
• SCALA is available as a standalone R/Shiny application and an online service.