Single-Cell Analysis Skills Index

What problem does it solve?

This index aggregates core single-cell RNA-seq workflows—quality control, cell type annotation, and trajectory inference—so researchers can load and bootstrap analyses quickly on common scRNA-seq tasks.

Core Features & Use Cases

• Quality Control: QC, filtering, normalization, and QC metric visualization for scRNA-seq data.
• Cell Type Annotation: Marker-based and reference-based approaches for labeling cell types in clusters.
• Trajectory Inference: Pseudotime and trajectory analysis to study differentiation and developmental processes.
• Use Case: Start with QC to ensure data quality, then annotate major cell types, and finally infer developmental trajectories across samples.

Quick Start

Load the Single-Cell Analysis Skills Index and begin with QC, annotation, and trajectory inference workflows.