Single Cell RNA SequencingAnalysis and Applications

Jacob M. Maronge

Department of StatisticsUniversity of Wisconsin–Madison

https://jmmaronge.github.io/

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Why RNA sequencing?Central dogma of molecular biology

DNA → mRNA → Proteins (→ Traits)

I Hard to measure proteinsI Measure mRNA as an attempt to get at traitsI 2 ways to measure mRNA gene expression

abundance: Bulk and Single Cell

RNA sequencingDifference between bulk and single cell

From: http://web.stanford.edu/class/cs262/presentations/lecture12.pdf

Single cell RNA-seqData structure

cell1,1 cell2,1 ... cellj1,1 cell1,2 cell2,2 ... cellj2,2

gene1 n1,1,1 n1,2,1 . . . n1,j1,1 n1,1,2 n1,2,2 . . . n1,j2,2

gene2 n2,1,1 n2,2,1 . . . n2,j1,1 n2,1,2 n2,2,2 . . . n2,j2,2...

......

......

......

......

genei ni,1,1 ni,2,1 . . . ni,j1,1 ni,1,2 ni,2,2 . . . ni,j2,2

I ni,j,k refers to the count of abundance of gene

expression in gene i , cell j , and condition k .

Single cell RNA-seqThis semester

I Learned about and found a problem in astatistical analysis technique for single cellRNA sequencing (scDD)

I Learned about the problem of normalization insingle cell RNA sequencing.

I Performed a literature review for the problem ofidentifying cell subpopulations.

Questions for Statistical AnalysisDifferential Expression

I Scientific collaborator comes in - wants to know ifthere is a difference in gene expression for theirfavorite gene across conditions.

AnalysisChallenges for Single Cell Data

I Single cell data present more challenges (lots ofzeros, multiple peaks, etc.)

Single Cell Differential Distribution (scDD)

From Korthauer et al. (2016)

I scDD uses 2 stage approach to sort genes into categories: 1.)run permutation test to see if gene is different acrossconditions; 2.) use Dirichlet Process Mixture Model to sortinto one of these 4 categories.

Potential Problem?Classified as DM – is it really?

I As a result, a min.size parameter was implementedinto the scDD R package to put a threshold on therequired number of obs. to be called a cluster.

Further analysisDigging deeper

I What if we look at the same genes in groups of cellsand try to classify subpopulations of these cells?

I Ex. pluripotent stem cells: These cells have the abilityto turn into blood cells, lung tissue etc.

I Goal: Figure out which pluripotent cells are morelikely to become blood, lung, etc.

I Turns out there’s many methods implemented forsolving similar problems.

Possible Further Direction?ATAC-Seq

I Short for: Assay for Transposase-AccessibleChromatin with high throughput sequencing(Buenrostro et al. (2016)).

I Chromatin is found in cells: consists of DNA, protein,and RNA.

I ATAC-Seq looks at where chromatin is accessible tobe transcribed.

I Related to epigenetics - basic idea: actions affectwhich genes are expressed.

Thank you!Special Thanks

Kendziorski LabI Rhonda BacherI Jeea ChoiI Prof. Christina KendziorskiI Ziyue Wang

References

I Buenrostro J, Wu B, Chang H, Greenleaf W. ATAC-seq: AMethod for Assaying Chromatin Accessibility Genome-Wide.Current protocols in molecular biology / edited by Frederick MAusubel . [et al]. 2015;109:21.29.1-21.29.9.doi:10.1002/0471142727.mb2129s109.

I Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R,Kendziorski C. A statistical approach for identifying differentialdistributions in single-cell RNA-seq experiments. GenomeBiology. 2016 Oct 25;17(1):222.