Splatter, a package for simulating single-cell RNA sequencing data Luke Zappia @_lazappi_

Single-cell RNA-seq ACTGACTCCA TCAGTACTGA CGTGTCATAG GATTGACCTA ACTGACTCCA TCAGTACTGA CGTGTCATAG GATTGACCTA ACTGACTCCA TCAGTACTGA CGTGTCATAG GATTGACCTA ACTGACTCCA TCAGTACTGA CGTGTCATAG GATTGACCTA Gene Cell 1 Cell 2 Cell 3 Cell 4 A 12 10 9 0 B 0 0 0 1 C 9 6 0 0 D 7 0 4 0

Sevensson et al. arXiv 1704.01379, 2017

Gene Cell 1 Cell 2 Cell 3 Cell 4 A 12 0 10 9 0 B 0 0 0 1 C 9 6 0 0 D 7 0 4 0 Bad cell? Low expression? Cell type specific? Cell cycle? Dropout?

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www. .org

Provide a truth to test against BUT - Often poorly documented and explained - Not easily reproducible or reusable - Don’t demonstrate similarity to real data Simulations

“Splatter: simulation of single-cell RNA sequencing data.” Genome Biology (2017) DOI: 10.1186/s13059-017-1305-0

The idea... Multiple simulations, same interface Real data Parameters Dataset Estimation Simulation

Building a package Bioconductor Devtools Roxygen testthat Checkmate GitHub Codecov (covr) Travis/Appveyor?

Checkmate fact <- function(n, method = "stirling") { if (length(n) != 1) stop("Argument 'n' must have length 1") if (!is.numeric(n)) stop("Argument 'n' must be numeric") if (is.na(n)) stop("Argument 'n' may not be NA") if (is.double(n)) { if (is.nan(n)) stop("Argument 'n' may not be NaN") if (is.infinite(n)) stop("Argument 'n' must be finite") if (abs(n - round(n, 0)) > sqrt(.Machine$double.eps)) stop("Argument 'n' must be an integerish value") n <- as.integer(n) } fact <- function(n, method = "stirling") { assertCount(n) assertChoice(method, c("stirling", "factorial")) if (method == "factorial") factorial(n) else sqrt(2 * pi * n) * (n / exp(1))^n }

Counts Parameters Estimation

Storing parameters Custom object Same structure, names - One object? Different objects

Params SimpleParams SplatParams LunParams SCDDParams (Virtual)

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Parameters Dataset Simulation

Storing simulations Simulated counts Intermediate values Parameters?

SCESet assayData featureData phenoData AnnotatedDataFrame AnnotatedDataFrame Matrix ExpressionSet

SingleCellExperiment assays rowData colData DataFrame DataFrame Matrix SummarizedExperiment metadata List

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Science!

Splat Negative binomial Expression outliers Defined library sizes Mean-variance trend Dropout

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. Biostatistics (2017). DOI: 10.1093/biostatistics/kxw055. scDD - NB with bimodality Korthauer KD, et al. Genome Biology (2016). DOI: 10.1186/s13059-016-1077-y. BASiCS - NB with spike-ins Vallejos CA, Marioni JC, Richardson S. PLoS Comp. Bio. (2015). DOI: 10.1371/journal.pcbi.1004333. Simulations

1. Estimate 2. Simulate 3. Compare params1 <- splatEstimate(real.data) params2 <- simpleEstimate(real.data) sim1 <- splatSimulate(params1, ...) sim2 <- simpleSimulate(params2, ...) datasets <- list(Real = real.data, Splat = sim1, Simple = sim2) comp <- compareSCESets(datasets) diff <- diffSCESets(datasets, ref = “Real”) Using Splatter

Means Difference in Means

Zeros per cell Difference in zeros

Mean-zeros Difference

Rank 1 8

Rank 1 8 Full-length Full-length

Complex simulations Groups Batches Paths

SingleCellExperiment Batch effects Simulations - BASiCS - mfa - PhenoPath - ZINB-WaVE New in Splatter 1.2.0 Bioconductor 3.6

Many tools for scRNA-seq analysis Catalogued in the scRNA-tools database Can be tested using synthetic datasets Splatter is our package for simulating scRNA-seq data Making a package is not as hard as you think Summary

@_lazappi_ oshlacklab.com Supervisors Alicia Oshlack Melissa Little MCRI Bioinformatics Belinda Phipson Breon Schmidt Everyone that makes tools and data available www.scRNA-tools.org @scRNAtools “Splatter: simulation of single-cell RNA sequencing data.” Genome Biology (2017) DOI: 10.1186/s13059-017-1305-0 “Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database” bioRxiv (2017) DOI: 10.1101/206573 bioconductor.org/packages/ splatter

Negative binomial

Real data 3 HapMap individuals 3 plates each 200 random cells Tung P-Y et al. Sci. Rep. (2017) DOI:10.1038/srep39921 A1 A2 A3 A B1 B2 B3 B C1 C2 C3 C Tung et al. iPSCs, C1 capture