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.

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. 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. 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. 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. 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. Previous work: SCDE - use error models to get a
better handle on technical noise

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. 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. 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. 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. PAGODA overview: assess expression within
annotated pathways and de novo gene sets

12. PAGODA overview: assess expression within
annotated pathways and de novo gene sets

13. PAGODA overview: Identify pathways and gene
sets exhibiting coordinated over dispersion

14. PAGODA overview: Remove redundancy pathways
and gene sets, and visualize

15. PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations

16. PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations

17. PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations

18. PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations

19. PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations

20. PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations

21. PAGODA applied to mouse neural progenitors
identifies and characterizes subpopulations
Allen Brain Atlas

22. PAGODA identifies multiple, potentially overlapping
aspects of transcriptional heterogeneity

23. PAGODA identifies multiple, potentially overlapping
aspects of transcriptional heterogeneity

24. PAGODA identifies multiple, potentially overlapping
aspects of transcriptional heterogeneity
Allen Brain Atlas

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

28. pklab.med.harvard.edu/scde

29. 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

30. 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