Suggestions for successful scRNA-seq analysis

A brief introduction to how single-cell technologies work, how to plan a successful experiment (from an analyst's point of view), the steps in a standard scRNA-seq analysis and touching on some more advanced topics. Presented at the 9th German Pharm-Tox Summit.

Transcript

1. Suggestions for successful scRNA-seq analysis Luke Zappia
2. Postdoctoral researcher scRNA-seq methods, software, benchmarking, analysis Luke Zappia Apply ML to biological data Integration, perturbations, transitions, multimodal Theis lab
3. 1. What is scRNA-seq? 2. Designing an scRNA-seq experiment 3. Standard scRNA-seq analysis 4. Advanced analysis topics
4. 1. What is scRNA-seq?
5. single-cell RNA sequencing
6. Why single-cell?
7. Single-cell capture Droplet-based Plate/well-based More cells Easier UMI Fewer cells Custom setup Full length, higher depth More flexible
8. UMI vs full-length Unique Molecular Identifiers 5’ AAAA (PCR){BARCODE}[UMI]TTTT Full-length Better quantification Less sequencing No gene-length bias Full coverage More sequencing Affected by gene length
9. Extensions Protein expression (CITE-seq, feature barcoding) Chromatin accessibility (scATAC-seq, 10x Multiome) Spatial location (10 Visium, MERFISH) Immune receptors (TCR/BCR profiling) Methylation, CRISPR screens, electrophysiology,... Pre-sorting (FACS to enrich target cells) Multiplexing (Cell hashing)
10. Comparison to bulk Gives insight into cellular variability Avoids the composition problem Much more complex analysis Much noisier Much sparser - But UMI data isn’t zero inflated!
11. 2. Experimental design
12. Who should be involved? Experimentalists 󰟾󰞲 Bioinformaticians 󰟲󰞅 PIs 󰟞󰞝 Collaborators 󰟦󰝹
13. What is the question? What do you want to answer with this experiment? - Not necessarily an hypothesis - Come from experimentalists but refined with analysts - Discuss everything that is relevant - Everyone needs to be on board
14. Things to consider Cells are not replicates! - You need multiple samples from each condition Avoid confounding batches and conditions - How will the samples be prepared? What are your controls? How rare are the cells you are interested in? Are you using the right assay?
15. Example designs Exploratory Case/control Multiple conditions Time series Cohort study Many others…
16. How long will it take? Experiments take time, so does analysis - Often getting results takes longer than generating data Simpler experiments with clearer questions are quicker and easier to analyse You will be likely be competing with other projects, good relationships are key!
17. Make a plan What is the question? What is the design (replicates!)? Who is involved? What is everyone’s role (authorship)? What if somebody leaves? What is the timeline? How is it funded? Write it down!
18. Tips for good collaborations Involve everyone in the process Good, clear communication Share all the (relevant) data Keep good records - Complete, consistent, machine-readable metadata
19. 3. Standard analysis
20. De-multiplexing Alignment Quantification @SEQ_ID GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT + !''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65 Gene Cell 1 Cell 2 Cell 3 Cell 4 A 12 10 9 0 B 0 0 1 4 C 9 6 0 0 D 7 0 4 0
21. Quality control Normalisation De-multiplexing Alignment Quantification Cell selection Cell filtering
22. Integration Remove technical effects between batches
23. Single-cell Integration Benchmarking Lücken et al., Nature Methods 2022 scvi-tools.org Gayoso et al., Nature Biotechnology 2022 De Donno et al., Nature Methods 2023 scPoli Hrovatin et al., bioRxiv sysVI Lotfollahi et al., Nature Biotechnology 2022 scArches
24. Integration Clustering Marker genes Annotation
25. Integration Clustering Marker genes Annotation
26. Integration Clustering Marker genes Annotation Label2 Label1 Label3 Label4 Label5
27. Quality control Normalisation De-multiplexing Alignment Quantification Integration Clustering Marker genes Annotation Downstream analysis…
28. Visualisation Quality control Normalisation De-multiplexing Alignment Quantification Integration Clustering Marker genes Annotation Downstream analysis…
29. 2D embedding Most common visualisation - t-SNE, UMAP etc. Can be useful BUT: - Easy to overinterpret - Hides lots of complexity - Potentially misleading
30. Over 1700 scRNA-seq tools www.scRNA-tools.org
31. Ecosystems scverse
32. 4. Advanced analysis
33. Downstream analyses vs Differential expression Condition 1 Condition 2 vs Differential abundance Pseudotime RNA velocity Fine variation
34. Using references Classification Reference mapping “Foundation models” Label Label Embedding Condition Niche…
35. Multimodal analysis RNA Protein Accessibility Result
36. Resources “Current best practices in single-cell RNA-seq analysis: a tutorial” Lücken, Theis, Molecular Systems Biology 2019 “Orchestrating Single-Cell Analysis with Bioconductor” bioconductor.org/books/release/OSCA Seurat satijalab.org/seurat Scanpy scanpy.readthedocs.io scverse scverse.org scRNA-tools scRNA-tools.org Open Problems in Single-Cell Analysis openproblems.bio sc-best-practices.org Huemos, Schaar et al., Nature Reviews Genetics 2023
37. Acknowledgements Theis lab 󰟾󰞲 Community 🫂 Everyone who has written documentation, tutorials etc. 📄 Everyone has developed tools and made their code available 🛠 󰞅
38. Suggestions for Successful scRNA-seq analysis 󰢦󰢡 ❓ Luke Zappia @_lazappi_ @lazappi lazappi.id.au @lazappi