festival of genomics 2016 - brain talk
TRANSCRIPT
![Page 1: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/1.jpg)
1Jean Fan / Festival of Genomics / June 2016
Jean Fan NSF GRFP | Bioinformatics and Integrative Genomics PhD Candidate Kharchenko Lab | Department of Biomedical Informatics | Harvard University
Applying single cell transcriptomics: unraveling the complexity of the brain
![Page 2: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/2.jpg)
2Jean Fan / Festival of Genomics / June 2016
![Page 3: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/3.jpg)
3
Motivation: Characterize heterogeneity and identify cell subpopulations with scRNA-seq
Jean Fan / Festival of Genomics / June 2016
Valent P, Bonnet D, De maria R, et al. Cancer stem cell definitions and terminology: the devil is in the details. Nat Rev Cancer. 2012;12(11):767-75.
Cancer
Kaech SM, Cui W. Transcriptional control of effector and memory CD8+ T cell differentiation. Nat Rev Immunol. 2012;12(11):749-61.
T Cells
![Page 4: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/4.jpg)
4
Motivation: Characterize heterogeneity and identify cell subpopulations with scRNA-seq
Jean Fan / Festival of Genomics / June 2016
Greig LC, Woodworth MB, Galazo MJ, Padmanabhan H, Macklis JD. Molecular logic of neocortical projection neuron specification, development and diversity. Nat Rev Neurosci. 2013;14(11):755-69.
NPCs
![Page 5: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/5.jpg)
5
Motivation: Characterize heterogeneity and identify cell subpopulations with scRNA-seq
Jean Fan / Festival of Genomics / June 2016
Greig LC, Woodworth MB, Galazo MJ, Padmanabhan H, Macklis JD. Molecular logic of neocortical projection neuron specification, development and diversity. Nat Rev Neurosci. 2013;14(11):755-69.
NPCs
Single cellRNA-seq
![Page 6: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/6.jpg)
6
Food For Thought◦ How can we identify transcriptional subpopulations in a way that is robust
and takes into consideration technical artefacts from single cell RNA-seq?◦ What are the different ways to group and classify cells in the brain?◦ In additional to expression heterogeneity, how can we make the most out
of single-cell RNA-seq data?
Jean Fan / Festival of Genomics / June 2016
![Page 7: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/7.jpg)
7
Food For Thought◦ How can we identify transcriptional subpopulations in a way that is
robust and takes into consideration technical artefacts from single cell RNA-seq?
◦ What are the different ways to group and classify cells in the brain?◦ In additional to expression heterogeneity, how can we make the most out
of single-cell RNA-seq data?
Jean Fan / Festival of Genomics / June 2016
![Page 8: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/8.jpg)
8
Challenges: scRNA-seq data is highly variable and noisy◦ Expect high correlation between replicates
Jean Fan / Festival of Genomics / June 2016
expression in bulk replicate 1
expr
essio
n in
bul
k re
plic
ate
2
Bulk
![Page 9: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/9.jpg)
9
Challenges: scRNA-seq data is highly variable and noisy◦ Expect high correlation between replicates◦ Many differences between individual cells
(even of the same type)◦ Biological vs. technical differences◦ Focus on the biological variability◦ Control for the technical variability
◦ ex. measurement failures (drop-outs)
Jean Fan / Festival of Genomics / June 2016
Single Cell
![Page 10: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/10.jpg)
10
Previous work: SCDE - use error models to get a better handle on technical noise
Jean Fan / Festival of Genomics / June 2016
![Page 11: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/11.jpg)
11
Previous work: SCDE - use error models to get a better handle on technical noise◦ Estimate true
biological variability of a gene
◦ Account for possible drop-out events
Jean Fan / Festival of Genomics / June 2016
Cross-fits
Cell 1
Cell
2
![Page 12: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/12.jpg)
12
Previous work: SCDE - use error models to get a better handle on technical noise◦ Estimate true
biological variability of a gene
◦ Account for possible drop-out events
Jean Fan / Festival of Genomics / June 2016
Cross-fits Error Models
Cell 1
Cell
2
![Page 13: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/13.jpg)
13
Previous work: SCDE - use error models to get a better handle on technical noise◦ Estimate true
biological variability of a gene
◦ Account for possible drop-out events
◦ Assess variability of expressing taking into consideration expression magnitude dependencies
Jean Fan / Festival of Genomics / June 2016
Variance Normalization
![Page 14: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/14.jpg)
14Jean Fan / Festival of Genomics / June 2016
Error models and normalization helps us understand the data on a probabilistic level:
What is the chance this 0 expression in this cell is due to drop-out or true non-expression?
What is the chance that this gene is really this variable given the expected variability for genes at this average expression magnitude?
![Page 15: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/15.jpg)
PAGODA (Pathway And Geneset OverDispersion Analysis) applies error models and variance normalization to characterize heterogeneity and identify subpopulations
pklab.med.harvard.edu/scde
![Page 16: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/16.jpg)
PAGODA intuition: Improve statistical sensitivity by taking advantage of pathways and gene sets◦ Rather than relying on a few genes, look for broader patterns of variability◦ Coordinated patterns of variability of genes linked to function/phenotype
== stronger signal -> increases statistical power
![Page 17: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/17.jpg)
PAGODA intuition: Improve statistical sensitivity by taking advantage of pathways and gene sets◦ Rather than relying on a few genes, look for broader patterns of variability◦ Coordinated patterns of variability of genes linked to function/phenotype
== stronger signal -> increases statistical power
![Page 18: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/18.jpg)
PAGODA intuition: Improve statistical sensitivity by taking advantage of pathways and gene sets◦ Rather than relying on a few genes, look for broader patterns of variability◦ Coordinated patterns of variability of genes linked to function/phenotype
== stronger signal -> increases statistical power
![Page 19: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/19.jpg)
PAGODA overview: assess expression within annotated pathways and de novo gene sets
![Page 20: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/20.jpg)
PAGODA overview: assess expression within annotated pathways and de novo gene sets
![Page 21: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/21.jpg)
PAGODA overview: Identify pathways and gene sets exhibiting coordinated over dispersion
![Page 22: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/22.jpg)
PAGODA overview: Remove redundancy pathways and gene sets, and visualize
![Page 23: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/23.jpg)
23Jean Fan / Festival of Genomics / June 2016
Pathway based approach integrates prior knowledge to increase statistical power and provide interpretability of identified subpopulations
(example next)
![Page 24: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/24.jpg)
24
Food For Thought◦ How can we identify transcriptional subpopulations in a way that is robust
and takes into consideration technical artefacts from single cell RNA-seq?◦ What are the different ways to group and classify cells in the brain?◦ In additional to expression heterogeneity, how can we make the most out
of single-cell RNA-seq data?
Jean Fan / Festival of Genomics / June 2016
![Page 25: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/25.jpg)
PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
cells
pathway clusters
Kun Zhang
Jerold Chun
![Page 26: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/26.jpg)
PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
![Page 27: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/27.jpg)
PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
![Page 28: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/28.jpg)
PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
![Page 29: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/29.jpg)
PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
![Page 30: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/30.jpg)
PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
![Page 31: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/31.jpg)
PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
![Page 32: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/32.jpg)
32
PAGODA integrated with FISH data spatially placed subpopulations
github.com/hms-dbmi/brainmapr
![Page 33: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/33.jpg)
PAGODA integrated with FISH data spatially placed subpopulations
Allen Brain Atlas; https://github.com/hms-dbmi/brainmapr
![Page 34: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/34.jpg)
PAGODA identifies multiple, potentially overlapping aspects of transcriptional heterogeneity
![Page 35: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/35.jpg)
PAGODA identifies multiple, potentially overlapping aspects of transcriptional heterogeneity
![Page 36: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/36.jpg)
PAGODA identifies multiple, potentially overlapping aspects of transcriptional heterogeneity
Allen Brain Atlas; https://github.com/hms-dbmi/brainmapr
![Page 37: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/37.jpg)
37
Food For Thought◦ How can we identify transcriptional subpopulations in a way that is robust
and takes into consideration technical artefacts from single cell RNA-seq?◦ What are the different ways to group and classify cells in the brain?◦ In additional to expression heterogeneity, how can we make the most
out of single-cell RNA-seq data?
Jean Fan / Festival of Genomics / June 2016
![Page 38: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/38.jpg)
38
Food For Thought◦ How can we identify transcriptional subpopulations in a way that is robust
and takes into consideration technical artefacts from single cell RNA-seq?◦ What are the different ways to group and classify cells in the brain?◦ In additional to expression heterogeneity, how can we make the most
out of single-cell RNA-seq data? ◦ Alternative splicing
Jean Fan / Festival of Genomics / June 2016
![Page 39: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/39.jpg)
39
PAGODA applied to human cortical cells identifies and characterizes subpopulations
Jean Fan / Festival of Genomics / June 2016
Xiaochang Zhang
Chris Walsh
![Page 40: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/40.jpg)
40Jean Fan / Festival of Genomics / June 2016
Marker genes confirm subpopulation identified by PAGODA
![Page 41: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/41.jpg)
41
PAGODA integrated with MISO identifies alternative splicing in pure pooled single cells
Jean Fan / Festival of Genomics / June 2016
![Page 42: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/42.jpg)
42
PAGODA integrated with MISO identifies alternative splicing in pure pooled single cells
Jean Fan / Festival of Genomics / June 2016
Needs bulk
![Page 43: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/43.jpg)
43
PAGODA integrated with MISO identifies alternative splicing in pure pooled single cells
Jean Fan / Festival of Genomics / June 2016
Needs bulk -> pool single cells
![Page 44: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/44.jpg)
44
Pure pooled RGs vs neurons lend credence to potential purity concerns with bulk CP vs. VZ
Jean Fan / Festival of Genomics / June 2016
![Page 45: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/45.jpg)
45
Food For Thought◦ How can we identify transcriptional subpopulations in a way that is robust
and takes into consideration technical artefacts from single cell RNA-seq?◦ What are the different ways to group and classify cells in the brain?◦ In additional to expression heterogeneity, how can we make the most
out of single-cell RNA-seq data? ◦ Alternative splicing◦ Copy number alteration detection / integrative analysis
Jean Fan / Festival of Genomics / June 2016
![Page 46: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/46.jpg)
46
BADGER quantitatively assess posterior probabilities of copy number alterations
Jean Fan / Festival of Genomics / June 2016
Bayesian Approach to CNV Detection from single cell RNA-seq (BADGER)
![Page 47: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/47.jpg)
47
BADGER quantitatively assess posterior probabilities of copy number alterations
Jean Fan / Festival of Genomics / June 2016
Bayesian Approach to CNV Detection from single cell RNA-seq (BADGER)
![Page 48: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/48.jpg)
48
BADGER quantitatively assess posterior probabilities of copy number alterations
Jean Fan / Festival of Genomics / June 2016
Bayesian Approach to CNV Detection from single cell RNA-seq (BADGER)
![Page 49: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/49.jpg)
49
BADGER applied to scRNA-seq identified subclonal expansion in progressive MM
Jean Fan / Festival of Genomics / June 2016
Soo Lee
Peter Park
Woong-Yang Park
Hae-Ock Lee
Initi
al
Bone
M
arro
wAs
cite
MM34
MM34A
![Page 50: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/50.jpg)
50
BADGER applied to scRNA-seq identified subclonal expansion in progressive MM
Jean Fan / Festival of Genomics / June 2016
![Page 51: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/51.jpg)
51
BADGER applied to scRNA-seq identified subclonal expansion in progressive MM
Jean Fan / Festival of Genomics / June 2016
![Page 52: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/52.jpg)
52
BADGER applied to scRNA-seq identified subclonal expansion in progressive MM
Jean Fan / Festival of Genomics / June 2016
![Page 53: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/53.jpg)
53
BADGER applied to scRNA-seq identified subclonal expansion in progressive MM
Jean Fan / Festival of Genomics / June 2016
![Page 54: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/54.jpg)
54
PAGODA integrated with BADGER connects genetic with transcriptional heterogeneity
Jean Fan / Festival of Genomics / June 2016
![Page 55: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/55.jpg)
55
PAGODA integrated with BADGER connects genetic with transcriptional heterogeneity
Jean Fan / Festival of Genomics / June 2016
![Page 56: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/56.jpg)
56Jean Fan / Festival of Genomics / June 2016
ScRNA-seq contains (noisy) expression as well as (noisy) splicing and some (noisy) genetic information.
Novel statistical and computational methods and techniques are still needed to harness the potential of scRNA-seq data!
![Page 57: Festival Of Genomics 2016 - Brain talk](https://reader035.vdocument.in/reader035/viewer/2022062820/589d70881a28abd91d8b6c15/html5/thumbnails/57.jpg)
57
Thanks! Kharchenko Lab
Peter Kharchenko
Joseph Herman
Jean Fan / Festival of Genomics / June 2016
Park Lab
Soo Lee
Semin Lee
SGI
Hae-Ock Lee
Walsh Lab
Xiaochang Zhang
Funding