SC Best Practices Skills Index

SC Best Practices Skills

Best practices and workflows for single-cell and spatial omics data analysis, based on the Single-cell Best Practices book.

When performing specific analysis tasks, load the relevant skill files to guide your approach.

Available Skills

Introduction & Fundamentals

Overview of single-cell RNA-seq technologies, raw data processing pipelines, analysis frameworks, and data format interoperability.

Skill file: introduction.md

When to use:

• Starting a new single-cell project and choosing technology/tools
• Need guidance on raw data processing (CellRanger, STARsolo, Kallisto)
• Converting between AnnData, SingleCellExperiment, and Seurat formats

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Preprocessing & Quality Control

Quality control, ambient RNA removal, doublet detection, normalization, feature selection, and dimensionality reduction.

Skill file: preprocessing.md

When to use:

• Starting analysis of a new single-cell dataset
• Filtering low-quality cells with MAD-based thresholds
• Choosing normalization and feature selection methods
• Running PCA, UMAP, or t-SNE

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Clustering & Cell Type Annotation

Graph-based clustering, resolution selection, manual and automated cell type annotation, and dataset integration.

Skill file: clustering_and_annotation.md

When to use:

• Clustering cells with Leiden algorithm
• Annotating cell types using markers or automated tools (CellTypist, scArches)
• Integrating multiple datasets (scVI, scANVI, BBKNN, Harmony)

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Trajectory Analysis

Pseudotime inference, RNA velocity, fate prediction, and lineage tracing.

Skill file: trajectory_analysis.md

When to use:

• Studying cell differentiation paths
• Running RNA velocity analysis (scVelo)
• Predicting cell fate with CellRank
• Analyzing lineage tracing data (Cassiopeia)

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Differential Expression & Condition Analysis