1. Differential Expression from RNA-seq
X. Shirley Liu
STAT115/215, BIO/BST282

2. Sequencing Read Distribution
The number of patients arriving in an emergency room between 10 and 11 pm
# Reads mapped to a gene of 1KB long
Poisson dist
λ average events per interval
K # events in an interval
Var = mean = λ

3. Sequencing Read Distribution
In reality, sequencing data is over-dispersed
(Mean<Variance)
Negative binomial
NB(r, p)
# of success before the
first r failure, if Pb(succ)
is p

4. Modeling Read Over Dispersion
Variance estimated by borrowing information from all the genes – hierarchical models
Test whether gene i expression follows same NB() between 2 conditions
FDR?

5. Fold Change with Var Shrinkage
shrinkage is not equal.
strong moderation for low
information genes: low counts
almost no
shrinkage
noisy estimates due to low counts
large FDR from the statistical model,
but we shouldn't trust the estimate itself

6. Splicing Transcripts
Assign reads to splice isoforms (TopHat)

7. Reference-based assembly
Transcript Assembly
Reference-based assembly
Cufflinks
De novo assembly
Trinity

8. Isoform Inference If given known set of isoforms
Estimate x to maximize the likelihood of observing n

9. Known Isoform Abundance Inference

10. Identification of Differential Splicing Between RNA-seq Samples
Most differential splicing detection algorithms call differentially expressed exons, not whole transcripts, esp for novel splicing

11. Splicing Isoform Inference
With known isoform set, sometimes the gene-level expression level inference is great, although isoform abundances might have uncertainty (e.g. known set incomplete)
De novo method are usually better at detecting differential exon splicing, but not whole transcripts
De novo isoform inference is a non-identifiable problem if RNA-seq reads are short and gene is long with too many exons
Experimental validation of quantitative differential splicing is still quite hard

12. Active Field HISAT2 for fast alignment Kallisto and Sleuth
Hierarchical index
Kallisto and Sleuth
Kallisto TPM, Sleuth differential expression
Known genes and transcripts

13. Summary Break RNA-seq design considerations Read mapping: BWA, STAR
Quality control: RSeQC
Expression index: R/FPKM and TPM
Differential expression: LIMMA-VOOM and DESeq
Transcriptome assembly: Cufflinks, Trinity
Alternative splicing: r/MATs
New developments: HISAT2, Kallisto and Sleuth
Break

14. Single Cell RNA-seq

15. Why Single-Cell RNA-seq?
Heterogeneous cell populations
Kolodziejczyk et al, Mol Cell 2015

16. Why Single-Cell RNA-seq?

17. Two General Approaches
From Ziegenhain et al. 2017

18. Drop-Seq From Macosko et al. 2015
Drop-seq overview. Cells mix with reagents in a droplet. RNA attaches to particle with specific barcode, etc, etc.
From Macosko et al. 2015

19. Variations cDNA conversion rate: 2-25% Droplet size
Reagent concentration
Cell ct & dilution
PCR efficiency
UMI controls over amplification of one transcript

20. Sequencing Results
PE seq $$$, one read has cell barcode, UMI and polyA
Compress all transcripts with the same barcode and same UMI into 1
From Macosko et al. 2015

21. SMART-based vs Droplet-based
Fresh cells
One-cell at a time
Small cell population
Lower dropout
Cell barcode
Full length
Transcripts / cell higher
Per cell transcription more accurate
$$$
Droplet-based
Fresh cells
All droplets together
Higher dropout
Cell barcode
UMI for PCR bias correction
3’ bias
Transcripts / cell lower
Per cluster transcription more accurate
$$$

22. Potential Applications
Understand stem cell differentiation or state transition
Map heterogeneity in complex tissue type (tumor / brain / blood, etc)
Identify new cell types with new functions
Stochastic and dynamic responses to perturbation …
Break

23. Quality Control

24. Dropouts
Kharchenko, et al, Nat Meth 2014; Zheng et al, Nat Comm 2017

25. From Kolodziejczyk et al. 2015
In each single cell, we observe variations in the gene counts. A good proportion of the variation doesn’t help us discover biology.
UMIs discussed more on other slide; transcription kinetics are largely unknown. Multiple methods have been proposed for cell cycle adjustment but have had limited success
From Kolodziejczyk et al. 2015

26. Visualizing scRNA-seq data
t-distributed stochastic neighbor embedding (tSNE)
New dimension reduction method
Preserve pair-wise distance, but focus on points close by
Distant between far-away clusters don’t matter
Colors are manually labeled
Density should be labeled
Non-deterministic

29. Reconstruction of Retinal Cell Types
PCA ~14K high quality cells from 44K sequenced cells
T-SNE on 32 statistically significant PCA
Density based clustering

30. Checking Batch Effect
Single cells from different days

31. Seurat is an R package designed for QC, analysis, and exploration of single cell RNA-seq data
Satija et al., Nat. Biotech. 2015

32. Summary SMART-based vs Droplet-based single-cell sequencing
Barcode and UMI
Dropout modeling
tSNE for visualization

33. Acknowledgement Wei Li Michael Love Alisha Holloway Simon Andrews
Radhika Khetani
Chengzhong Zhang
Etai Jacob
Caleb Lareau
Luca Pinello
Assieh Saadatpour