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CSH SC 2015 - PAGODA talk

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Cold Spring Harbor. Single Cell Analyses Meeting. November 11 - 14, 2015. Slides for talk: PAGODA—Pathway and gene set overdispersion analysis characterizes single cell transcriptional heterogeneity.

Cold Spring Harbor. Single Cell Analyses Meeting. November 11 - 14, 2015. Slides for talk: PAGODA—Pathway and gene set overdispersion analysis characterizes single cell transcriptional heterogeneity.

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CSH SC 2015 - PAGODA talk

  1. 1. PAGODA Pathway and gene set overdispersion analysis characterizes single cell transcriptional heterogeneity Jean Fan Kharchenko Lab Department of Biomedical Informatics Harvard Medical School
  2. 2. Motivation: Characterize heterogeneity and identify cell subpopulations with single cell RNA-seq 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
  3. 3. Motivation: Characterize heterogeneity and identify cell subpopulations with single cell RNA-seq 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. Kaech SM, Cui W. Transcriptional control of effector and memory CD8+ T cell differentiation. Nat Rev Immunol. 2012;12(11):749-61. Cancer T Cells
  4. 4. Motivation: Characterize heterogeneity and identify cell subpopulations with single cell RNA-seq 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. 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. Kaech SM, Cui W. Transcriptional control of effector and memory CD8+ T cell differentiation. Nat Rev Immunol. 2012;12(11):749-61. Cancer T Cells NPCs
  5. 5. Challenges: Single-cell RNA-seq data is highly variable and noisy • 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)
  6. 6. Previous work: SCDE - use error models to get a better handle on technical noise
  7. 7. 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 • PAGODA uses these error models along with variance normalization to more accurately identify variables genes Error Models
  8. 8. 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 • PAGODA uses these error models along with variance normalization to more accurately identify variables genes Variance Normalization
  9. 9. 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 • Like GSEA • Coordinated patterns of variability of genes linked to function/phenotype == stronger signal • Increases statistical power
  10. 10. 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 • Like GSEA • Coordinated patterns of variability of genes linked to function/phenotype == stronger signal • Increases statistical power
  11. 11. PAGODA overview: assess expression within annotated pathways and de novo gene sets
  12. 12. PAGODA overview: assess expression within annotated pathways and de novo gene sets
  13. 13. PAGODA overview: Identify pathways and gene sets exhibiting coordinated over dispersion
  14. 14. PAGODA overview: Remove redundancy pathways and gene sets, and visualize
  15. 15. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
  16. 16. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
  17. 17. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
  18. 18. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
  19. 19. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
  20. 20. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations
  21. 21. PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations Allen Brain Atlas
  22. 22. PAGODA identifies multiple, potentially overlapping aspects of transcriptional heterogeneity
  23. 23. PAGODA identifies multiple, potentially overlapping aspects of transcriptional heterogeneity
  24. 24. PAGODA identifies multiple, potentially overlapping aspects of transcriptional heterogeneity Allen Brain Atlas
  25. 25. In summary: PAGODA characterizes single cell transcriptional heterogeneity • Uses error models and variance normalization to accurately quantify biological variability • Identifies significant aspects of coordinated variability within annotated pathways or de novo gene sets • Enables users to identify and characterize single cell subpopulations based on various (potentially overlapping) aspects of transcriptional heterogeneity PAGODA
  26. 26. pklab.med.harvard.edu/scde
  27. 27. Thanks to everyone involved! Thanks for listening! • Neeraj Salathia, Rui Liu, Gwen Kaeser, Yun Yung, Joseph L Herman, Fiona Kaper, Jian-Bing Fan, Kun Zhang, Jerold Chun, Peter Kharchenko
  28. 28. Thanks to everyone involved! Thanks for listening! • Neeraj Salathia, Rui Liu, Gwen Kaeser, Yun Yung, Joseph L Herman, Fiona Kaper, Jian-Bing Fan, Kun Zhang, Jerold Chun, Peter Kharchenko Looking for computational post-docs! pklab.med.harvard.edu pklab.med.harvard.edu/scde

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