scAnalyzeR

scAnalyzeR performs end-to-end analysis of single-cell RNA sequencing (scRNA-seq) data to identify cell subpopulations, compute differential gene expression, perform gene set enrichment and correlation analyses, and infer pseudotime cell trajectories.

Key Features:

• Compatibility and Flexibility: Accepts input from various technology platforms, supports multiple model organisms including human and mouse, and accommodates diverse file formats.
• Data Preprocessing: Implements preprocessing steps for scRNA-seq data prior to downstream analyses.
• Quality Control Measures: Provides quality control metrics and filtering for single-cell datasets.
• Basic Summary Statistics: Computes summary statistics for cells and genes.
• Dimension Reduction Techniques: Performs dimension reduction to facilitate visualization and clustering.
• Unsupervised Clustering Methods: Supports unsupervised clustering to identify cell populations.
• Differential Gene Expression Analysis: Conducts differential expression testing between cell groups or conditions.
• Gene Set Enrichment Analysis: Performs gene set enrichment to interpret gene expression signatures.
• Correlation Analysis: Computes gene–gene or cell–cell correlation analyses.
• Pseudotime Cell Trajectory Inference: Infers cell trajectories and pseudotime ordering.
• Visualization Capabilities: Generates various plots for interpretation and presentation of scRNA-seq results.
• Customization Options: Allows specification of custom analysis parameters.

Scientific Applications:

• Tissue Heterogeneity Analysis: Enables exploration of tissue heterogeneity and cellular complexity using scRNA-seq data.
• Liver Cancer Single-Cell Analysis: Applied to an in-house liver cancer scRNA-seq dataset to reveal distinct tumor cell subpopulations with unique gene expression signatures.
• Cancer Research and Molecular Insight: Facilitates identification of cellular and molecular signatures relevant to cancer research.

Methodology:

The pipeline integrates data preprocessing, quality control, summary statistics, dimension reduction, unsupervised clustering, differential gene expression, gene set enrichment, correlation analysis, and pseudotime cell trajectory inference.