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Jean Fan / Festival of Genomics / June 2016 1
Jean Fan
NSF GRFP | Bioinformatics and Integrative Genomics PhD Candidate
Kh...
Jean Fan / Festival of Genomics / June 2016 2
Motivation: Characterize heterogeneity and
identify cell subpopulations with scRNA-seq
Jean Fan / Festival of Genomics / J...
Motivation: Characterize heterogeneity and
identify cell subpopulations with scRNA-seq
Jean Fan / Festival of Genomics / J...
Motivation: Characterize heterogeneity and
identify cell subpopulations with scRNA-seq
Jean Fan / Festival of Genomics / J...
Food For Thought
◦ How can we identify transcriptional subpopulations in a way that is robust
and takes into consideration...
Food For Thought
◦ How can we identify transcriptional subpopulations in a way that is
robust and takes into consideration...
Challenges: scRNA-seq data is highly variable
and noisy
◦ Expect high correlation between replicates
Jean Fan / Festival o...
Challenges: scRNA-seq data is highly variable
and noisy
◦ Expect high correlation between replicates
◦ Many differences be...
Previous work: SCDE - use error models to get
a better handle on technical noise
Jean Fan / Festival of Genomics / June 20...
Previous work: SCDE - use error models to get
a better handle on technical noise
◦ Estimate true
biological
variability of...
Previous work: SCDE - use error models to get
a better handle on technical noise
◦ Estimate true
biological
variability of...
Previous work: SCDE - use error models to get
a better handle on technical noise
◦ Estimate true
biological
variability of...
Jean Fan / Festival of Genomics / June 2016 14
Error models and normalization helps us understand the data on a
probabilis...
PAGODA (Pathway And Geneset OverDispersion Analysis)
applies error models and variance normalization to
characterize heter...
PAGODA intuition: Improve statistical sensitivity
by taking advantage of pathways and gene sets
◦ Rather than relying on a...
PAGODA intuition: Improve statistical sensitivity
by taking advantage of pathways and gene sets
◦ Rather than relying on a...
PAGODA intuition: Improve statistical sensitivity
by taking advantage of pathways and gene sets
◦ Rather than relying on a...
PAGODA overview: assess expression within
annotated pathways and de novo gene sets
PAGODA overview: assess expression within
annotated pathways and de novo gene sets
PAGODA overview: Identify pathways and gene
sets exhibiting coordinated over dispersion
PAGODA overview: Remove redundancy
pathways and gene sets, and visualize
Jean Fan / Festival of Genomics / June 2016 23
Pathway based approach integrates prior knowledge to increase
statistical p...
Food For Thought
◦ How can we identify transcriptional subpopulations in a way that is robust
and takes into consideration...
PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations
cells
pathway clusters
Kun Zhang
Je...
PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations
PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations
PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations
PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations
PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations
PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations
PAGODA integrated with FISH data spatially
placed subpopulations
32
github.com/hms-dbmi/brainmapr
PAGODA integrated with FISH data spatially
placed subpopulations
Allen Brain Atlas; https://github.com/hms-dbmi/brainmapr
PAGODA identifies multiple, potentially overlapping
aspects of transcriptional heterogeneity
PAGODA identifies multiple, potentially overlapping
aspects of transcriptional heterogeneity
PAGODA identifies multiple, potentially overlapping
aspects of transcriptional heterogeneity
Allen Brain Atlas; https://gi...
Food For Thought
◦ How can we identify transcriptional subpopulations in a way that is robust
and takes into consideration...
Food For Thought
◦ How can we identify transcriptional subpopulations in a way that is robust
and takes into consideration...
PAGODA applied to human cortical cells
identifies and characterizes subpopulations
Jean Fan / Festival of Genomics / June ...
Jean Fan / Festival of Genomics / June 2016 40
Marker genes
confirm
subpopulation
identified by
PAGODA
PAGODA integrated with MISO identifies
alternative splicing in pure pooled single cells
Jean Fan / Festival of Genomics / ...
PAGODA integrated with MISO identifies
alternative splicing in pure pooled single cells
Jean Fan / Festival of Genomics / ...
PAGODA integrated with MISO identifies
alternative splicing in pure pooled single cells
Jean Fan / Festival of Genomics / ...
Pure pooled RGs vs neurons lend credence to
potential purity concerns with bulk CP vs. VZ
Jean Fan / Festival of Genomics ...
Food For Thought
◦ How can we identify transcriptional subpopulations in a way that is robust
and takes into consideration...
BADGER quantitatively assess posterior
probabilities of copy number alterations
Jean Fan / Festival of Genomics / June 201...
BADGER quantitatively assess posterior
probabilities of copy number alterations
Jean Fan / Festival of Genomics / June 201...
BADGER quantitatively assess posterior
probabilities of copy number alterations
Jean Fan / Festival of Genomics / June 201...
BADGER applied to scRNA-seq identified
subclonal expansion in progressive MM
Jean Fan / Festival of Genomics / June 2016 4...
BADGER applied to scRNA-seq identified
subclonal expansion in progressive MM
Jean Fan / Festival of Genomics / June 2016 50
BADGER applied to scRNA-seq identified
subclonal expansion in progressive MM
Jean Fan / Festival of Genomics / June 2016 51
BADGER applied to scRNA-seq identified
subclonal expansion in progressive MM
Jean Fan / Festival of Genomics / June 2016 52
BADGER applied to scRNA-seq identified
subclonal expansion in progressive MM
Jean Fan / Festival of Genomics / June 2016 53
PAGODA integrated with BADGER connects
genetic with transcriptional heterogeneity
Jean Fan / Festival of Genomics / June 2...
PAGODA integrated with BADGER connects
genetic with transcriptional heterogeneity
Jean Fan / Festival of Genomics / June 2...
Jean Fan / Festival of Genomics / June 2016 56
ScRNA-seq contains (noisy) expression as well as (noisy) splicing and
some ...
Thanks!
Kharchenko Lab
Peter Kharchenko
Joseph Herman
Jean Fan / Festival of Genomics / June 2016 57
Park Lab
Soo Lee
Semi...
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Festival Of Genomics 2016 - Brain talk

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Applying single cell transcriptomics: unraveling the complexity of the brain

http://www.festivalofgenomicsboston.com/speaker/jean-fan/

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Festival Of Genomics 2016 - Brain talk

  1. 1. Jean Fan / Festival of Genomics / June 2016 1 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
  2. 2. Jean Fan / Festival of Genomics / June 2016 2
  3. 3. Motivation: Characterize heterogeneity and identify cell subpopulations with scRNA-seq Jean Fan / Festival of Genomics / June 2016 3 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
  4. 4. Motivation: Characterize heterogeneity and identify cell subpopulations with scRNA-seq Jean Fan / Festival of Genomics / June 2016 4 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
  5. 5. Motivation: Characterize heterogeneity and identify cell subpopulations with scRNA-seq Jean Fan / Festival of Genomics / June 2016 5 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 cell RNA-seq
  6. 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 6
  7. 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 7
  8. 8. Challenges: scRNA-seq data is highly variable and noisy ◦ Expect high correlation between replicates Jean Fan / Festival of Genomics / June 2016 8 expression in bulk replicate 1 expressioninbulkreplicate2 Bulk
  9. 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 9 Single Cell
  10. 10. Previous work: SCDE - use error models to get a better handle on technical noise Jean Fan / Festival of Genomics / June 2016 10
  11. 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 11 Cross-fits Cell 1 Cell2
  12. 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 12 Cross-fits Error Models Cell 1 Cell2
  13. 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 13 Variance Normalization
  14. 14. Jean Fan / Festival of Genomics / June 2016 14 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?
  15. 15. PAGODA (Pathway And Geneset OverDispersion Analysis) applies error models and variance normalization to characterize heterogeneity and identify subpopulations pklab.med.harvard.edu/scde
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. PAGODA overview: assess expression within annotated pathways and de novo gene sets
  20. 20. PAGODA overview: assess expression within annotated pathways and de novo gene sets
  21. 21. PAGODA overview: Identify pathways and gene sets exhibiting coordinated over dispersion
  22. 22. PAGODA overview: Remove redundancy pathways and gene sets, and visualize
  23. 23. Jean Fan / Festival of Genomics / June 2016 23 Pathway based approach integrates prior knowledge to increase statistical power and provide interpretability of identified subpopulations (example next)
  24. 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 24
  25. 25. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations cells pathway clusters Kun Zhang Jerold Chun
  26. 26. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
  27. 27. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
  28. 28. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
  29. 29. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
  30. 30. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
  31. 31. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
  32. 32. PAGODA integrated with FISH data spatially placed subpopulations 32 github.com/hms-dbmi/brainmapr
  33. 33. PAGODA integrated with FISH data spatially placed subpopulations Allen Brain Atlas; https://github.com/hms-dbmi/brainmapr
  34. 34. PAGODA identifies multiple, potentially overlapping aspects of transcriptional heterogeneity
  35. 35. PAGODA identifies multiple, potentially overlapping aspects of transcriptional heterogeneity
  36. 36. PAGODA identifies multiple, potentially overlapping aspects of transcriptional heterogeneity Allen Brain Atlas; https://github.com/hms-dbmi/brainmapr
  37. 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 37
  38. 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 38
  39. 39. PAGODA applied to human cortical cells identifies and characterizes subpopulations Jean Fan / Festival of Genomics / June 2016 39 Xiaochang Zhang Chris Walsh
  40. 40. Jean Fan / Festival of Genomics / June 2016 40 Marker genes confirm subpopulation identified by PAGODA
  41. 41. PAGODA integrated with MISO identifies alternative splicing in pure pooled single cells Jean Fan / Festival of Genomics / June 2016 41
  42. 42. PAGODA integrated with MISO identifies alternative splicing in pure pooled single cells Jean Fan / Festival of Genomics / June 2016 42 Needs bulk
  43. 43. PAGODA integrated with MISO identifies alternative splicing in pure pooled single cells Jean Fan / Festival of Genomics / June 2016 43 Needs bulk -> pool single cells
  44. 44. Pure pooled RGs vs neurons lend credence to potential purity concerns with bulk CP vs. VZ Jean Fan / Festival of Genomics / June 2016 44
  45. 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 45
  46. 46. BADGER quantitatively assess posterior probabilities of copy number alterations Jean Fan / Festival of Genomics / June 2016 46 Bayesian Approach to CNV Detection from single cell RNA-seq (BADGER)
  47. 47. BADGER quantitatively assess posterior probabilities of copy number alterations Jean Fan / Festival of Genomics / June 2016 47 Bayesian Approach to CNV Detection from single cell RNA-seq (BADGER)
  48. 48. BADGER quantitatively assess posterior probabilities of copy number alterations Jean Fan / Festival of Genomics / June 2016 48 Bayesian Approach to CNV Detection from single cell RNA-seq (BADGER)
  49. 49. BADGER applied to scRNA-seq identified subclonal expansion in progressive MM Jean Fan / Festival of Genomics / June 2016 49 Soo Lee Peter Park Woong-Yang Park Hae-Ock Lee Initial Bone Marrow Ascite MM34 MM34A
  50. 50. BADGER applied to scRNA-seq identified subclonal expansion in progressive MM Jean Fan / Festival of Genomics / June 2016 50
  51. 51. BADGER applied to scRNA-seq identified subclonal expansion in progressive MM Jean Fan / Festival of Genomics / June 2016 51
  52. 52. BADGER applied to scRNA-seq identified subclonal expansion in progressive MM Jean Fan / Festival of Genomics / June 2016 52
  53. 53. BADGER applied to scRNA-seq identified subclonal expansion in progressive MM Jean Fan / Festival of Genomics / June 2016 53
  54. 54. PAGODA integrated with BADGER connects genetic with transcriptional heterogeneity Jean Fan / Festival of Genomics / June 2016 54
  55. 55. PAGODA integrated with BADGER connects genetic with transcriptional heterogeneity Jean Fan / Festival of Genomics / June 2016 55
  56. 56. Jean Fan / Festival of Genomics / June 2016 56 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!
  57. 57. Thanks! Kharchenko Lab Peter Kharchenko Joseph Herman Jean Fan / Festival of Genomics / June 2016 57 Park Lab Soo Lee Semin Lee SGI Hae-Ock Lee Walsh Lab Xiaochang Zhang Funding

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