Preface
I How sclet Thinks: State, Provenance, and Navigation
1 State-Aware Analysis Contract
2 Automation & Provenance Logs
II From Data to Discovery: Analysis Mainlines
3 Data Ingress and Object Interoperability
- 3.1 Seurat and SCE Conversion
- 3.2 AnnData (h5ad)
4 Recommended usage
- 4.1 For end users
- 4.2 For pipeline authors
5 Developer design notes
6 Core Single-Cell Workflow
7 Integration and Batch Correction
8 Cell Identity and Reference Mapping
9 Cell Fate and Dynamic Processes
10 Program, Regulon, and Mechanistic Interpretation
11 Differential Analysis and Functional Interpretation
12 Cell-Cell Communication and Microenvironment Interaction
13 An explicit id: the auto-generated visualization collides with the
14 Enhanced Visualization chapter.
- 14.0.1 Legacy CellChat Visualization
15 State Priority and Perturbation Sensitivity
16 Spatial Context and Niche Analysis
17 Multimodal Expansion
III Specialized Techniques and Reference
18 Enhanced Visualization
19 An explicit id: the auto-generated reference-mapping collides with the
20 Cell Identity and Reference Mapping chapter.
21 Metacells with SuperCell
- 21.1 RunSuperCell
- 21.2 Estimate SuperCell Purity
22 Milo: Differential Abundance
23 Phenotype Association
- 23.1 A practical pattern
- 23.2 What gets recorded
24 Interactive Data Exploration
- 24.1 Launch Explorer
- 24.2 Customization
25 SVP: Gene Set Activity in Spatial and Single-Cell Data
IV AI-Assisted Analysis
26 AI in One Call
27 AI Foundations: State, Actions, and the Ledger
28 Driving sclet from an External Agent
29 The In-Package Assistant
30 Evidence Governance: Claims, Results, and Reports
References

sclet: A State-Aware Single-Cell and Spatial Transcriptomics Toolkit

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