| id | sc_best_practices_index |
| name | SC Best Practices Skills Index |
| description | Skills derived from the Single-cell Best Practices book (sc-best-practices.org).
Comprehensive workflows and guidelines for single-cell and spatial omics analysis.
|
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
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
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)
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)
Differential Expression & Condition Analysis
Differential expression (pseudobulk methods), compositional analysis,
gene set enrichment, and perturbation modeling.
Skill file: differential_and_condition.md
When to use:
- Comparing gene expression between conditions
- Running pseudobulk DE analysis with edgeR/DESeq2
- Performing GSEA/pathway analysis with decoupler
- Analyzing compositional changes with scCODA
Gene Regulatory Networks & Cell-Cell Communication
GRN inference with pySCENIC and cell-cell communication analysis
with LIANA, NicheNet, and CellChat.
Skill file: regulatory_and_communication.md
When to use:
- Inferring gene regulatory networks from scRNA-seq
- Analyzing ligand-receptor interactions between cell types
- Running pySCENIC (GRNBoost2 + motif pruning + AUCell)
Bulk Deconvolution
Estimating cell-type proportions in bulk RNA-seq using single-cell references.
Skill file: bulk_deconvolution.md
When to use:
- Deconvolving bulk RNA-seq with single-cell reference
- Comparing methods (CIBERSORTx, MuSiC, DWLS, Scaden)
- Validating deconvolution with pseudobulk benchmarks
Chromatin Accessibility (scATAC-seq)
scATAC-seq preprocessing, QC, peak calling, motif analysis, and
GRN inference from chromatin data.
Skill file: chromatin_accessibility.md
When to use:
- Processing scATAC-seq data (SnapATAC2, ArchR, Signac)
- Assessing QC metrics (TSS enrichment, fragment size distribution)
- Running TF motif enrichment with chromVAR
- Integrating scATAC with scRNA-seq
Spatial Omics
Spatial transcriptomics analysis including neighborhood analysis,
spatial domains, spatially variable genes, deconvolution, and gene imputation.
Skill file: spatial_omics.md
When to use:
- Analyzing Visium, MERFISH, Xenium, or other spatial data
- Running spatial neighborhood analysis with Squidpy
- Identifying spatial domains (SpaGCN, STAGATE)
- Deconvolving spatial spots (Cell2location)
- Imputing unmeasured genes (Tangram)
Surface Protein (CITE-seq)
CITE-seq / ADT data processing, normalization, quality control,
and joint RNA-protein analysis.
Skill file: surface_protein.md
When to use:
- Processing CITE-seq / ADT data
- Normalizing protein data (CLR, DSB)
- Joint RNA-protein analysis (totalVI, WNN)
- ADT-based cell type annotation
Immune Repertoire (TCR/BCR)
TCR and BCR profiling, clonotype analysis, clonal expansion,
repertoire diversity, and integration with gene expression.
Skill file: immune_repertoire.md
When to use:
- Analyzing single-cell TCR/BCR sequencing data
- Clonotype definition and expansion analysis with scirpy
- Measuring repertoire diversity
- Integrating immune receptor data with transcriptomics
Multimodal Integration
Strategies for integrating multi-modal single-cell data including
paired (MOFA+, WNN, MultiVI) and unpaired (GLUE, bridge) approaches.
Skill file: multimodal_integration.md
When to use:
- Integrating RNA + ATAC (10x Multiome)
- Integrating RNA + Protein (CITE-seq)
- Working with unpaired multi-modal data
- Choosing between integration strategies
Reproducibility
Environment management, containerization, workflow orchestration,
version control, and documentation standards.
Skill file: reproducibility.md
When to use:
- Setting up a reproducible analysis environment
- Creating Docker/Singularity containers
- Building Snakemake or Nextflow pipelines
- Managing random seeds for deterministic results
Using Skills
- Before analysis: Scan this index for relevant skills
- Load skill file: Read the full skill document for detailed guidance
- Follow best practices: Use the code snippets and workflows provided
- Adapt as needed: Skills are templates; adjust for your specific data
