Simplifying simulation of single-cell RNA-seq

Single-cell RNA sequencing (scRNA-seq) is rapidly becoming a tool of choice for biologists who wish to investigate gene expression, particularly in areas such as development and differentiation. In contrast to traditional bulk RNA-seq experiments, which measure expression averaged across millions of cells, single-cell experiments can be used to observe how genes are expressed in individual cells. Along with the dramatic increase in resolution provided by scRNA-seq comes an array of bioinformatics challenges. Single-cell data is relatively sparse (for both biological and technical reasons), quality control is difficult and it is unclear how to replicate measurements. The focus of analysis is also different, with more emphasis on clustering cells to identify cell types or ordering of cells to understand dynamic processes than traditional tasks such as differential expression testing. Any new bioinformatics method for scRNA-seq analysis should demonstrate two things: 1) it can do what it claims and 2) it helps to produce biological insight. The first is hard to prove on real data where there is often no known truth. Because of this, bioinformaticians turn to simulations. Unfortunately current scRNA-seq simulations are frequently poorly documented, not reproducible and do not demonstrate similarity to real data or experimental designs. Here we discuss some of the problems with simulating scRNA-seq data and provide a simulation framework that addresses these concerns.

Transcript

1. Simplifying simulation of single-cell RNA-seq Luke Zappia @_lazappi_
2. What is single-cell? Matthew Daniels via The Cell Image Library http://www.cellimagelibrary.org/images/38912
3. ACTGACTCCA TCAGTACTGA CGTGTCATAG GATTGACCTA ACTGACTCCA TCAGTACTGA CGTGTCATAG GATTGACCTA ACTGACTCCA TCAGTACTGA CGTGTCATAG GATTGACCTA ACTGACTCCA TCAGTACTGA CGTGTCATAG GATTGACCTA ACTGACTCCA TCAGTACTGA CGTGTCATAG GATTGACCTA BULK SINGLE-CELL BIOLOGY
4. Gene Sample 1 A 43 B 3 C 17 D 24 BULK SINGLE-CELL 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
5. Analysis Focus on clustering, lineage tracing Currently > 75 available methods goo.gl/4wcVwn github.com/lazappi/single-cell-software
6. A new analysis method should... 1. Show that it can do what it claims 2. Show that it produces insight
7. http://www.cellimagelibrary.org/images/40483 M Uhlen et al. via The Cell Image Library Simulations
8. Gene Cell 1 Cell 2 Cell 3 Cell 4 A B C D
9. Simulations Provide a known truth Allow us to test… • Effectiveness • Assumptions • Relative performance
10. Current simulations Often poorly documented and explained Not easily reproducible or reusable Don’t demonstrate similarity to real data
12. Splatter R package Collection of simulation methods Consistent, easy to use, interface Functions for comparison github.com/Oshlack/splatter
13. The Splat simulation Negative binomial Expression outliers Defined library sizes Mean-variance trend Dropout
14. Other simulations Simple - Negative binomial Lun - NB with cell factors Lun ATL, Bach K, Marioni JC. Genome Biology (2016). DOI: 10.1186/s13059-016-0947-7. Lun 2 - Sampled NB with batch effects Lun ATL, Marioni JC. bioRxiv (2016). DOI: 10.1101/073973. scDD - NB with bimodality Korthauer KD, et al. bioRxiv (2015). DOI: 10.1101/035501.
15. Using Splatter params1 <- splatEstimate(real.data) sim1 <- splatSimulate(params1, ...) params2 <- simpleEstimate(real.data) sim2 <- simpleSimulate(params2, ...) datasets <- list(Real = real.data, Splat = sim1, Simple = sim2) comp <- compareSCESets(datasets)
16. The data http://www.cellimagelibrary.org/images/41467 Jan Schmoranzer via The Cell Image Library
17. Subset of data from study looking at design and batch effects by Tung et al. Tung P-Y, et al. bioRxiv (2016). DOI: 10.1101/062919. Single HapMap stem cell line - 3 batches - 221 cells, 13 058 genes Real data
18. Means
19. Variance
20. Mean-variance
21. Library size
22. Zeros
23. What else? http://www.cellimagelibrary.org/images/44701 Andres J Garcia and Ankur Singh via The Cell Image Library
24. Groups
25. Paths
26. Summary Single-cell RNA-seq is an exciting new technology - Lots of analysis methods Simulations can be used to evaluate methods - But often hard to reuse Splatter - R package for simulation and comparison Splat - Simulation method for groups or paths
27. Acknowledgements Alicia Oshlack Melissa Little Belinda Phipson MCRI Bioinformatics oshalacklab.com
28. github.com/Oshlack/splatter github.com/lazappi/single-cell-software @_lazappi_ oshalacklab.com
29. Solution? http://www.cellimagelibrary.org/images/38804 Wellcome Images via The Cell Image Library
30. Library size
31. Means
32. Negative binomial