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scRNA-seq Differential expression analyses Olga Dethlefsen olga.dethlefsen@nbis.se NBIS, National Bioinformatics Infrastructure Sweden May 2018 Olga (NBIS) scRNA-seq DE May 2018 1 / 43 Outline Outline Olga (NBIS) scRNA-seq DE May 2018

  1. scRNA-seq Differential expression analyses Olga Dethlefsen olga.dethlefsen@nbis.se NBIS, National Bioinformatics Infrastructure Sweden May 2018 Olga (NBIS) scRNA-seq DE May 2018 1 / 43

  2. Outline Outline Olga (NBIS) scRNA-seq DE May 2018 2 / 43

  3. Outline Outline Introduction: what is so special about scRNA-seq DE? Olga (NBIS) scRNA-seq DE May 2018 2 / 43

  4. Outline Outline Introduction: what is so special about scRNA-seq DE? Common methods: what is out there? Olga (NBIS) scRNA-seq DE May 2018 2 / 43

  5. Outline Outline Introduction: what is so special about scRNA-seq DE? Common methods: what is out there? Performance: how do we know what is best? Olga (NBIS) scRNA-seq DE May 2018 2 / 43

  6. Outline Outline Introduction: what is so special about scRNA-seq DE? Common methods: what is out there? Performance: how do we know what is best? Practicalities: what to do in real life? Olga (NBIS) scRNA-seq DE May 2018 2 / 43

  7. Outline Outline Introduction: what is so special about scRNA-seq DE? Common methods: what is out there? Performance: how do we know what is best? Practicalities: what to do in real life? Summary: what to remember from this hour? Olga (NBIS) scRNA-seq DE May 2018 2 / 43

  8. Outline Let’s get to know each other https://www.menti.com Olga (NBIS) scRNA-seq DE May 2018 3 / 43

  9. Introduction Introduction Olga (NBIS) scRNA-seq DE May 2018 4 / 43

  10. Introduction Figure: Simplified scRNA-seq workflow [adapted from Wikipedia] Olga (NBIS) scRNA-seq DE May 2018 5 / 43

  11. Introduction Figure: Simplified scRNA-seq workflow [adapted from Wikipedia] Olga (NBIS) scRNA-seq DE May 2018 6 / 43

  12. Introduction Differential expression means taking read count data & performing statistical analysis to discover quantitative changes in expression levels between experimental groups i.e. to decide whether, for a given gene, an observed difference in read counts is significant (greater than what would be expected just due to natural random variation) adapted from Wu et al. 2017 Olga (NBIS) scRNA-seq DE May 2018 7 / 43

  13. Introduction Differential expression means taking read count data & performing statistical analysis to discover quantitative changes in expression levels between experimental groups i.e. to decide whether, for a given gene, an observed difference in read counts is significant (greater than what would be expected just due to natural random variation) Differential expression is an old "problem" known from bulk RNA-seq and microarray studies in fact building on one of the most common statistical problems, i.e comparing groups for statistical differences adapted from Wu et al. 2017 Olga (NBIS) scRNA-seq DE May 2018 7 / 43

  14. Introduction Differential expression is an old problem. So what is all the commotion about? https://www.menti.com & 70 52 87 Olga (NBIS) scRNA-seq DE May 2018 8 / 43

  15. Introduction Differential expression is an old problem. So what is all the commotion about? https://www.menti.com & 70 52 87 scRNA-seq: special characteristics high noise levels (technical and biological factors) low library sizes low amount of available mRNAs results in amplification biases and "dropout events" 3’ bias, partial coverage and uneven depth (technical) stochastic nature of transcription (biological) multimodality in gene expression; presence of multiple possible cell states within a cell population (biological) Olga (NBIS) scRNA-seq DE May 2018 8 / 43

  16. Introduction 1300018J18Rik Arid2 Bend3 Ccdc104 Ccnt1 0.3 4 0.3 0.3 0.3 3 0.2 0.2 0.2 0.2 2 0.1 0.1 0.1 0.1 1 0 0.0 0.0 0.0 0.0 Crispld2 Fbxw13 Hbxip Katna1 Lcorl 0.25 1.5 2.0 3 0.3 0.20 1.5 1.0 0.15 2 0.2 1.0 0.10 0.5 1 0.1 0.5 0.05 0.0 0.0 0.0 0.00 0 density Mybpc1 Nars Ndufa3 Nono Pgam2 0.5 0.3 0.6 0.4 0.4 0.4 0.3 0.2 0.4 0.2 0.2 0.2 0.2 0.1 0.1 0.0 0.0 0.0 0.0 0.0 Rbm17 Rragc Slc1a3 Slc22a20 Smarcd1 1.5 0.5 0.4 0.3 0.15 0.4 0.3 1.0 0.3 0.2 0.10 0.2 0.2 0.5 0.05 0.1 0.1 0.1 0.0 0.00 0.0 0.0 0.0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 value Based on tutorial data Olga (NBIS) scRNA-seq DE May 2018 9 / 43

  17. Common methods Common methods Olga (NBIS) scRNA-seq DE May 2018 10 / 43

  18. Common methods Olga (NBIS) scRNA-seq DE May 2018 11 / 43

  19. Common methods Generic parametric tests, e.g. t-test non-parametric tests, e.g. Kruskal-Wallis RNA-seq based edgeR limma DEseq2 scRNA-seq specific MAST, SCDE, Monocle D 3 E, Pagoda Olga (NBIS) scRNA-seq DE May 2018 12 / 43

  20. Common methods Miao and Zhang 2016 Olga (NBIS) scRNA-seq DE May 2018 13 / 43

  21. Common methods Available Short name Method Software version Input Reference from BPSC BPSC BPSC 0.99.0/1 CPM GitHub [11] D3E D3E D3E 1.0 raw counts GitHub [12] DESeq2 DESeq2 DESeq2 1.14.1 raw counts Bioconductor [13] DESeq2betapFALSE DESeq2 without beta prior DESeq2 1.14.1 raw counts Bioconductor [13] DESeq2census DESeq2 DESeq2 1.14.1 Census counts Bioconductor [13] DESeq2 without the built-in in- DESeq2nofilt DESeq2 1.14.1 raw counts Bioconductor [13] dependent filtering DEsingle DEsingle DEsingle 0.1.0 raw counts GitHub [14] edgeRLRT edgeR/LRT edgeR 3.19.1 raw counts Bioconductor [15–17] edgeRLRTcensus edgeR/LRT edgeR 3.19.1 Census counts Bioconductor [15–17] edgeR/LRT with deconvolution edgeR 3.19.1, edgeRLRTdeconv raw counts Bioconductor [15, 17, 18] normalization scran 1.2.0 edgeR/LRT with robust disper- edgeRLRTrobust edgeR 3.19.1 raw counts Bioconductor [15–17, 19] sion estimation edgeRQLF edgeR/QLF edgeR 3.19.1 raw counts Bioconductor [15, 16, 20] edgeR/QLF with cellular detec- edgeRQLFDetRate edgeR 3.19.1 raw counts Bioconductor [15, 16, 20] tion rate as covariate limmatrend limma-trend limma 3.30.13 log 2 (CPM) Bioconductor [21, 22] MASTcpm MAST MAST 1.0.5 log 2 (CPM+1) Bioconductor [23] MAST with cellular detection MASTcpmDetRate MAST 1.0.5 log 2 (CPM+1) Bioconductor [23] rate as covariate MASTtpm MAST MAST 1.0.5 log 2 (TPM+1) Bioconductor [23] MAST with cellular detection MASTtpmDetRate MAST 1.0.5 log 2 (TPM+1) Bioconductor [23] rate as covariate metagenomeSeq metagenomeSeq metagenomeSeq raw counts Bioconductor [24] 1.16.0 monocle monocle (tobit) monocle 2.2.0 TPM Bioconductor [25] monoclecensus monocle (Negative Binomial) monocle 2.2.0 Census counts Bioconductor [25, 26] monoclecount monocle (Negative Binomial) monocle 2.2.0 raw counts Bioconductor [25] Author- NODES NODES NODES raw counts provided [27] 0.0.0.9010 link ROTScpm ROTS ROTS 1.2.0 CPM Bioconductor [28, 29] ROTStpm ROTS ROTS 1.2.0 TPM Bioconductor [28, 29] voom-transformed ROTSvoom ROTS ROTS 1.2.0 Bioconductor [28, 29] raw counts SAMseq SAMseq samr 2.0 raw counts CRAN [30] scDD scDD scDD 1.0.0 raw counts Bioconductor [31] SCDE SCDE scde 2.2.0 raw counts Bioconductor [32] SeuratBimod Seurat (bimod test) Seurat 1.4.0.7 raw counts GitHub [33, 34] Seurat (bimod test) without the SeuratBimodnofilt Seurat 1.4.0.7 raw counts GitHub [33, 34] internal filtering Seurat (bimod test) with internal SeuratBimodIsExpr2 Seurat 1.4.0.7 raw counts GitHub [33, 34] expression threshold set to 2 SeuratTobit Seurat (tobit test) Seurat 1.4.0.7 TPM GitHub [25, 33] TMM-normalized ttest t-test stats (R v 3.3) CRAN [16, 35] TPM voomlimma voom-limma limma 3.30.13 raw counts Bioconductor [21, 22] TMM-normalized Wilcoxon Wilcoxon test stats (R v 3.3) CRAN [16, 36] TPM Soneson and Robinson 2018 Olga (NBIS) scRNA-seq DE May 2018 14 / 43

  22. Common methods More detailed examples More detailed examples Olga (NBIS) scRNA-seq DE May 2018 15 / 43

  23. Common methods More detailed examples MAST uses generalized linear hurdle model designed to account for stochastic dropouts and bimodal expression distribution in which expression is either strongly non-zero or non-detectable The rate of expression Z , and the level of expression Y , are modeled for each gene g , indicating whether gene g is expressed in cell i (i.e., Z ig = 0 if y ig = 0 and z ig = 1 if y ig > 0) A logistic regression model for the discrete variable Z and a Gaussian linear model for the continuous variable (Y|Z=1): logit ( P r ( Z ig = 1 )) = X i β D g P r ( Y ig = Y | Z ig = 1 ) = N ( X i β C g , σ 2 g ) , where X i is a design matrix Model parameters are fitted using an empirical Bayesian framework Allows for a joint estimate of nuisance and treatment effects DE is determined using the likelihood ratio test Olga (NBIS) scRNA-seq DE May 2018 16 / 43

  24. Common methods More detailed examples SCDE models the read counts for each gene using a mixture of a NB, negative binomial, and a Poisson distribution NB distribution models the transcripts that are amplified and detected Poisson distribution models the unobserved or background-level signal of transcripts that are not amplified (e.g. dropout events) subset of robust genes is used to fit, via EM algorithm, the parameters to the mixture of models For DE, the posterior probability that the gene shows a fold expression difference between two conditions is computed using a Bayesian approach Olga (NBIS) scRNA-seq DE May 2018 17 / 43

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