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sc-best-practices-skills-index Skills derived from the Single-cell Best Practices book (sc-best-practices.org).
Comprehensive workflows and guidelines for single-cell and spatial omics analysis.
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Download Zip Downloading... bulk_deconvolution.md 12.4 KB chromatin_accessibility.md 11.7 KB clustering_and_annotation.md 14.8 KB differential_and_condition.md 18.3 KB immune_repertoire.md 14.1 KB multimodal_integration.md 14.6 KB regulatory_and_communication.md 20.1 KB reproducibility.md 16.7 KB surface_protein.md 14.7 KB trajectory_analysis.md 18.6 KB More from this repository atrium-desktop-driving-apps Drive the user's Atrium desktop: open files in installed viewer apps
(Viv bioimages, Vitessce/Spatial 3D omics, Mol* structures, IGV/Gosling
genomics, Volume 3D, Cytoscape, MSA, PhyloTree, RDKit), read and steer
ANY window — including ones the user opened — and call app backends.
Skills for opening and driving agent-controllable visualization
components in the Pantheon UI sidebar — interactive viewers the agent
can open, control, and read back. Viewers: Vitessce (spatial / single-
cell omics), Viv (bioimage / microscopy), volume3d (3D image volumes —
MIP/ISO), spatial3d (3D spatial transcriptomics), Mol*, IGV, Gosling,
Cytoscape, MSA, RDKit, phylotree, plus agent-generated apps.
developmental-gene-panel-design-workflow Panel design for DEVELOPING / dynamic systems (embryonic organs, differentiation, regeneration).
The target experiment is usually a LATE / terminal stage, but the biology is a trajectory: terminal
cell types are end-products of earlier lineage programs. A panel built from the target stage alone
resolves terminal STATES but systematically misses the developmental REGULATORS that produced them.
This workflow therefore uses TWO references — the target-stage dataset (cell-state resolution) and an
independent EARLIER-stage reference (developmental origin) — combines them under an explicit budget
split, and benchmarks on the target stage.
Related occupations SOC
Based on SOC occupation classification
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 skillsLoad skill file : Read the full skill document for detailed guidanceFollow best practices : Use the code snippets and workflows providedAdapt as needed : Skills are templates; adjust for your specific data 