SingleCAnalyzer

SingleCAnalyzer performs end-to-end computational analysis of single-cell RNA sequencing (scRNA-Seq) data, processing raw FASTQ files through demultiplexing, read trimming and alignment and providing quality control, empty droplet detection, feature selection, dimensional reduction, clustering, cell-type prediction, pseudotime/trajectory analysis, differential expression, gene set expression analysis, and functional enrichment.

Key Features:

• End-to-End Analysis Pipeline: Processes raw FASTQ files including demultiplexing, read trimming, and alignment to generate expression matrices for downstream analysis.
• Quality Control and Feature Selection: Performs sample quality control and feature selection to filter low-quality cells and select informative genes.
• Empty Droplet Detection: Identifies and removes empty droplets to distinguish true cell-derived signals from background noise.
• Dimensional Reduction and Clustering: Executes dimensional reduction and unsupervised clustering to identify and visualize cellular subpopulations.
• Cellular Type Prediction and Pseudotime Analysis: Provides cell-type prediction and pseudotime/trajectory analysis to investigate cell identity and developmental trajectories.
• Comparative and Functional Analyses: Performs expression comparisons between groups, differential expression analysis, functional enrichment, and gene set expression analysis.
• Interactive Visualization: Generates interactive graphs for exploratory and analytical inspection of single-cell results.

Scientific Applications:

• Cellular biology: Enables analysis of cell function and cellular heterogeneity at single-cell resolution.
• Developmental biology: Facilitates investigation of differentiation processes and developmental trajectories using pseudotime analysis.
• Immunology: Supports characterization of immune cell populations and state-specific gene expression.
• Disease pathology: Allows comparison of expression profiles and pathway enrichment between conditions to study disease mechanisms.

Methodology:

Computational steps explicitly include demultiplexing, read trimming, alignment, sample quality control, feature selection, empty droplet detection, dimensional reduction, unsupervised clustering, cell-type prediction, pseudotime/trajectory analysis, group expression comparisons, differential expression, functional enrichment, and gene set expression analysis.