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Single-cell best practices

Introduction

1. Prior art
2. Single-cell RNA sequencing
3. Raw data processing
4. Analysis frameworks and tools
5. Interoperability

Preprocessing and visualization

6. Quality Control
7. Normalization
8. Feature selection
9. Dimensionality Reduction

Identifying cellular structure

10. Clustering
11. Annotation
12. Data integration

Inferring trajectories

13. Pseudotemporal ordering
14. RNA velocity
15. Lineage tracing

Dealing with conditions

16. Differential gene expression analysis
17. Compositional analysis
18. Gene set enrichment and pathway analysis
19. Perturbation modeling

Modeling mechanisms

20. Gene regulatory networks
21. Cell-cell communication

Deconvolution

22. Bulk deconvolution

Chromatin Accessibility

23. Single-cell ATAC sequencing
24. Quality Control
25. Gene regulatory networks using chromatin accessibility

Spatial omics

26. Single-cell data resolved in space
27. Neighborhood analysis
28. Spatial domains
29. Spatially variable genes
30. Spatial deconvolution
31. Imputation

Surface protein

35. Quality control
36. Normalization
37. Doublet detection
38. Dimensionality Reduction
39. Batch correction
40. Annotation

Adaptive immune receptor repertoire

41. Immune Receptor Profiling
42. Clonotype analysis
46. Specificity analysis
47. Integrating AIR and transcriptomics

Multimodal integration

48. Paired integration
49. Advanced integration

Reproducibility

50. Reproducibility

Outlook

51. Outlook

Acknowledgements

52. Acknowledgements

Glossary

53. Glossary

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Interoperability

5. Interoperability#

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4. Analysis frameworks and tools

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6. Quality Control

By Lukas Heumos, Anna Schaar, single-cell best practices consortium
© Copyright 2022.

Brought to you by Theislab, with many thanks to the single-cell community as a whole!