SCALA

SCALA performs multimodal analysis and visualization of single-cell next-generation sequencing (NGS) data, specifically scRNA-seq and scATAC-seq, to interpret transcriptional profiles and chromatin accessibility at single-cell resolution.

Key Features:

• Multimodal analysis: Supports both independent and integrative analysis of scRNA-seq and scATAC-seq data modalities.
• Quality control: Incorporates quality-control procedures for single-cell NGS datasets.
• Cell population identification: Identifies distinct cell populations from single-cell data.
• Cell state determination: Determines cell states across samples and conditions.
• Functional enrichment analysis: Performs functional enrichment on gene sets derived from single-cell analyses.
• Cellular trajectory inference: Infers cellular trajectories to analyze dynamic processes.
• Ligand–receptor interaction analysis: Analyzes ligand–receptor interactions between cell populations.
• Regulatory network reconstruction: Reconstructs regulatory networks from single-cell transcriptional and accessibility data.
• Software integration: Integrates multiple software packages to implement the described analyses.
• Configurable outputs: Provides parameterizable analysis functions with tabular results and publication-ready visualizations.

Scientific Applications:

• Multimodal single-cell profiling: Jointly analyze transcriptional and chromatin accessibility landscapes using scRNA-seq and scATAC-seq.
• Cell type and state discovery: Identify and characterize distinct cell types and states within heterogeneous tissues.
• Regulatory mechanism inference: Reconstruct regulatory networks linking chromatin accessibility and gene expression.
• Cell–cell communication mapping: Map ligand–receptor interactions to study intercellular signaling.
• Dynamic process analysis: Apply trajectory inference to study differentiation or response dynamics.
• Functional interpretation: Use enrichment analyses to interpret biological functions and pathways.
• Disease-model analysis: Analyze datasets from models such as TNF-driven arthritic mice to derive biological insights.

Methodology:

Computational steps explicitly include quality control, identification of distinct cell populations, determination of cell states, functional enrichment analysis, cellular trajectory inference, ligand–receptor interaction studies, and regulatory network reconstruction; implementation integrates multiple software packages and is developed using R, Shiny, and JavaScript.