sc-best-practices-skills-index

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

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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

---

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

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Using Skills

1. Before analysis: Scan this index for relevant skills
2. Load skill file: Read the full skill document for detailed guidance
3. Follow best practices: Use the code snippets and workflows provided
4. Adapt as needed: Skills are templates; adjust for your specific data