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scRNA-Seq Lecture - Stem Cell Network RNA-Seq Workshop 2017
- 1. Gene Expression One Cell at a Time Experimental design and analysis of single-cell RNA-Seq data David Cook Vanderhyden Lab, uOttawa DavidPCook dpcook dcook082@uottawa.ca
- 2. Conclusions from bulk analysis can be representative of nothing Bulk analysis InterpretationSample
- 3. Conclusions from bulk analysis can be representative of nothing Impossible to conclude if differences are due to composition or cancer cells themselves TCGA, Nature, 2011
- 4. Bulk summaries hide underlying structure X Mean: Y Mean: X SD: Y SD: Corr: 54.26 47.83 16.76 26.93 -0.06 Matejka and Fitzmaurice (Autodesk Research, Toronto)
- 5. Single-cell exposes this heterogeneity scRNA-Seq Fur Venom Sample Interpretation
- 8. Fluidigm C1 Pros Allows visual inspection of captured cells Customizability Cons Only two inlets for cell samples Throughput can’t keep up with field Relatively long prep time Live Cell Dead Cell Multiple Live Cells Calcein AM Ethidium homodimer-1
- 9. Droplet-based methods Pros Very high throughput Up to 8 unique samples per run System cost relatively low Cons Limited customizability Zheng et al., Nature Comm, 2017
- 10. Plate methods
- 11. Common Chemistry: RT and 3’ Enrichment Only 3’ end of transcript is PCR amplified
- 12. Why 3’ enrichment? 5’ 3’1kb cDNA Ten 100bp reads needed for 1x coverage 100bp reads 5’ 3’ 200bp 3’ fragment Two 100bp reads needed for 1x coverage 100bp reads Consequence: Lose nearly all information about isoform usage (sorry, Matt)
- 13. Single-Cell Platforms 10x Genomics BioRad ddSeq Fluidigm C1 Plate methods Cost per cell Cells per run Flexibility/Customizable + ~1000-46000 + ++ ~300-10000 + ++++ 96 or 800 +++ Protocol Dependent 10 - >10k +++++
- 14. Cost 10x Genomics Reagent Kit (20 samples): $20,000 One sample = ~600-6000 cells Microfluidics Chips (Six 8-sample chips): $1,440 Fluidigm C1 (HT assays) Reagent Kit (5 runs): $5,000 One run = ~800 cells Integrated Fluidics Circuit (1 run): $2000 Sequencing NextSeq500 High Output 1 run ($3700) enough for ~2-3k cells HiSeq4000 1 lane (~$2700) enough for ~2-3k cells (Often need to purchase entire flow cell)
- 16. How many cells? Depends on what you’re looking at More cells = better detection of rare populations Mocosko et al,. Cell, 2015 Pollen et al,. Nature Biotech, 2014 More heterogeneity? More cells
- 17. Sequencing: How deep do you need to go? Depends on what you want Svensson et al., Nature Methods, 2017 Rough Guideline Aim for 100,000 reads per cell 50,000 per cell is probably fine Zheng et al., Nature Comm, 2017 16k reads/cell (>60k PBMCs) Zheng et al., Nature Comm, 2017
- 18. Sample numbers and batch effects Hicks et al., BioRxiv, 2016 Mix biological variables in individual runs!
- 19. Sample numbers and batch effects Tung et al., Scientific Reports, 2017
- 21. Project Background Control Estrogen Areas of columnar OSE Control Estradiol 0 5 10 15 %ovariansurfacethat hascolumnarcells * Areas of hyperplastic OSE Control Estradiol 5 10 15 %ovariansurface thatishyperplastic * Placebo E2 E2 Hormone replacement therapy increases risk of ovarian cancer Exogenous estrogen enhances the cancer progression in mouse models Prolonged estrogen exposure causes ovarian epithelial dysplasia in normal mice
- 22. General Analysis Workflow Sequencing Processing QC & Filtering Normalization (and imputation?) Clustering Differential expression, trajectory analysis, network analysis, etc
- 23. Alignment, transcript quantification, and import into R Kallisto – Pseudoalignment to the transcriptome Bray et al., Nature Biotech, 2016 tximport package to dump gene-level expression matrix into R Soneson et al., F1000, 2016
- 24. General Workflow Sequencing Processing QC & Filtering Normalization (and imputation?) Clustering Differential expression, trajectory analysis, network analysis, etc
- 25. Filtering scRNA-Seq Data Dead Cell Multiple Live Cells Ethidium homodimer-1 (Fluidigm specific) Before Filtering After Filtering 800 cells 30735 genes 636 cells 14300 genes Filter genes that are not detected in at least 10 cells
- 26. General Workflow Sequencing Processing QC & Filtering Normalization (and imputation?) Clustering Differential expression, trajectory analysis, network analysis, etc
- 27. Finding and controlling for technical variables Data exploration is critical Exprs. matrices Raw Counts Log-transformed Z-scores Normalized Cells Genes Cell metadataphenoData Gene metadata featureData SCEset:
- 28. Finding and controlling for technical variables 1. Library Size Scaling each library by a size factor • Counts per million (CPM) • DESeq • TMM • Pooled-based size factors (Lun et al., Genome Biology, 2016)
- 29. Finding and controlling for technical variables 2. Cell Cycle (or other confounding biological processes we aren’t interested in) Stegle et al., Nature Rev. Genetics, 2015 Cell cycle classification using “scran” package Cell cycle not driving large amounts of variation at this point
- 30. Finding and controlling for technical variables 3. Other technical variables Finding variables that drive variation Coloured by IFC Column
- 31. Finding and controlling for technical variables 3. Other technical variables removeBatchEffect() – limma package Yi = β0 + β1(TotalFeatures)i + β2(IFC.Row)i + β3(Condition)i + εi Removes the effect of the technical covariates on a per-gene basis Note: IFC.Column tackled same way, but split by condition beforehand Post-normalization Odd IFC Column
- 32. Finding and controlling for technical variables 3. Other technical variables O_o?
- 33. Data Imputation
- 34. Data Imputation Van Dijk et al., BioRxiv, 2017
- 35. Data Imputation Before Imputation After Imputation Before Imputation After Imputation
- 36. General Workflow Sequencing Processing QC & Filtering Normalization (and imputation?) Clustering Differential expression, trajectory analysis, network analysis, etc
- 38. General Workflow Sequencing Processing QC & Filtering Normalization (and imputation?) Clustering Differential expression, trajectory analysis, network analysis, etc
- 39. Differential Expression Currently no “standard”—variety of methods perform pretty well: • DESeq • edgeR • Monocle • SCDE (single cell differential expression)
- 40. Differential Expression PC2 Values PC1 Values PC3 Values Proteolysis Actin cytoskeleton organization Cell adhesion Innate immune response(?) Oxidation-reduction process Oxidative stress Positive regulation of apoptosis Metabolic pathways MAPK signaling PI3K-Akt signaling Negative regulation of apoptosis Cell differentiation Oxidation-reduction process Oxidative stress Proton transport Mitophagy Response to wound healing Response to hypoxia Apoptosis Negative regulation of cell cycle Oxidation-reduction process Cell adhesion Rho protein signaling TCA cycle
- 41. Trajectory Analysis Leveraging asynchrony to reconstruct cellular response trajectories Wagner et al., Nature Biotech Reviews, 2016 A couple methods: • Monocle • Diffusion pseudotime • PHATE • Wishbone • Waterfall • Wanderlust • SLICER • TSCAN • And more
- 42. Trajectory Analysis Reverse Graph Embedding (monocle) Qiu et al., BioRxiv, 2017
- 43. Trajectory Analysis Can we model the phenotype divergence where estrogen-treated cells progress to form foci? Does this model foci formation?
- 45. Trajectory Analysis Discovering new transcriptional dynamics Yi = β0 + β1(Pseudotime)i + β2(Branch)i + β3(Pseudotime)i(Branch)i + εiFull Model: Yi = β0 + β1(Pseudotime)i + β2(Branch)i + εiReduced Model: Likelihood Ratio Test to find sig. genes
- 46. Trajectory Analysis Still working on it! But here’s the type of stuff you pull out Qiu et al., BioRxiv, 2017
- 47. Where is the field going?
- 48. Trajectory Analysis • Larger data sets • Combining the technology with perturbations • Collecting multiple –omics datasets from individual cells Dixit et al., Cell, 2016 BioRxiv, 2017
- 49. Disclaimer: Much of this will be obsolete in a matter of months
- 50. Staying on the ball with scRNA-Seq Nature Methods, Jan 23rd, 2017 Science, March 3rd, 2017 Nature Methods, March 27th, 2017 Nature Methods, March 6th, 2017 Nature Methods, April 17, 2017 Nature Biotechnology, May 1st, 2017
- 51. Resources Sean Davis’s “Awesome Single Cell” list https://github.com/seandavi/awesome-single-cell 10x Genomics Public Datasets https://support.10xgenomics.com/single-cell/datasets 1.3 Million brain cells from E18 mice 68k PBMCs Fun Tutorials Seurat: http://satijalab.org/seurat/get_started.html Monocle (find on Bioconductor)
- 52. Thank You!!!
Hinweis der Redaktion
- CPM not robust to high amounts of differential expression DESeq and TMM don’t hold up well to lots of zero counts