1.  Tools for visualizing high dimensional single cell data and inferring cellular hierarchy 
Sylvia K. Plevritis, PhD
Professor
Department of Radiology and
(by courtesy) Management Science and Engineering
Stanford University School of Medicine

2. Clustering versus Ordering
Most microarray analyses are focus on
what is the difference between A and B?
We want to know:
Is it possible that A becomes B (or visa versa)?
If so, what are the molecular drivers of this process? B A
All these existing methods are essentially asking the same question:
What is the difference between A and B.
In this talk, we are going to ask a different question.
How did A become B?
By that, I mean Does there exist any gradual/progressive changing pattern among individual samples?
Does there exist such a progression (green line) that describes: (1) the progression among individual samples within group A, (2) the progression within group B, and (3) the transition in-between.
If we can identify such a progressive pattern, this pattern tells us the trajectory of biological progression, and the important transition point. This can be applied to developmental biology, disease progression, drug response. B A

3. Inferring Order Given high throughput datasets, we want to identify:
(1) the ordering among individual samples
(2) which markers identify the ordering
Here is our goal,
Given a microarray data matrix, we want to identify two things:
(1)
(2)

4. Sample Progression Discovery (SPD)
Qiu et al., Discovering Biological Progression
Underlying Microarray Samples,
PLoS Computational Biology, 2011.

5. SPD on B-cell differentiation
7 HSC
7 CLP
7 proB
7 preB
7 IM
5 M
(Naïve B, CB,
CC, Memory B, CD19+)
Microarray expression data is obtained from the Weissman Lab.

6. SPD on B-cell differentiation

7. SPD on B-cell differentiation
Genes in selected modules were specific to B-cell differentiation and included CD19, CD20, CD79 as well as master transcription factors including PAX5 and SP140. There was also enrichment of genes in the BCR pathway.
Explain the genes that are responsible for this ordering.

8. SPD infers Individual Tumor Plasticity in TCGA Breast Cancer Data
Color coded by PAM50 Score
Enrichment of Mammary Gland Development, Mesenchymal Cells

9. SPD Graph Color-Coded by Genes Associated with EMT
Breast Cancer SPD graph with the samples colored with the expression of the module that contained genes involved in mesenchymal processes. 

10. Visualizing and Ordering High Dimensional Single Cell Data

11. Single cell mass cytometry
Garry Nolan, PhD
CyTOF = Cytometer
+ Elemental Mass Spectrometer
CyTOF Data
Sample: normal human bone marrow
31 Proteins measured on single cells
13 core surface markers 18 function markers

12. Qiu et al., Nature Biotechnology, 2011.
SPADE: Spanning-tree Progression Analysis of Density-normalized Events
Qiu et al., Nature Biotechnology, 2011.
Anchang et al, Nature Protocols, (accepted).

13. Viewing all the surface markers …

14. Manually identifying the populations …

15. Pooling samples …

16. Analyzing a non-branching process …

17. (a) Normal Bone Marrow (b) ALL
Comparison to other visualization algorithms …
(a) Normal
Bone Marrow
(b) ALL
viSNE : Amir et al., Nature Biotechnology 2013.
ACCENSE: Shekhar et al., PNAS 2014.

18. Integration of SPADE and t-SNE to create “SPADE FOREST”…

19. Multi-target drug combinations derived from single drug effects measured at the level of single cells

20. Overview
Intratumor heterogeneity is modeled at the level of the single cell Single drug response at the level of single cell is measured by changes in protein expression using CyToF Analysis of drug response are performed on clusters of similar cells Drug combinations are derived from single drug response each cell cluster individually then combined through a mixture model

21. Intratumoral Heterogeneity

22. Outline
Collect perturbation single data drug response data using CyTOF
Identify homogeneous cell types based on surface markers
Build a “nested effects” graphical drug model in each cell type based on intracellular change; estimate a “mixture nested effects”
Determine a scoring function of optimizing drug combinations
identify homogeneous cell types based on surface markers
build a “nested effects” graphical model of a drug response in each cell type based on intracellular change; estimate a “mixture nested effects”
determine a scoring function of optimizing drug combinations 1
Decision tree

23. Outline Collect single data perturbation data using CyTOF
Identify homogeneous cell types based on surface markers
Build a “nested effects” graphical drug model in each cell type based on intracellular change; estimate a “mixture nested effects”
Determine a scoring function of optimizing drug combinations
identify homogeneous cell types based on surface markers
build a “nested effects” graphical model of a drug response in each cell type based on intracellular change; estimate a “mixture nested effects”
determine a scoring function of optimizing drug combinations 1
Decision tree

24. Perturbation CyTOF Profiling of Functional & Surface Phenotypes in Healthy Bone Marrow and Pediatric AML
p4EBP1
pPLCgamma2
pAkt
pRb
pAMPK
pS6
pCbl
pSTAT1
pCREB
pSTAT3
pERK1/2
pSTAT5
p-p38 MAPK cleaved Casp3
pSyk
15 Functional Markers
CD3
CD41
CD7
CD44
CD11b
CD45
CD15
CD47
CD19
CD64
CD33
CD117
CD34
CD123
CD38
HLADR
16 Surface Markers
Staining panel (31 Abs)
No inhibitor
Basal (unstim.)
AICAR
Flt3 ligand G-CSF
GM-CSF
IFNα
IFNϒ
IL-3
IL-6
IL-10
IL-27
PMA + ionomycin
PVO4
SCF
TNFα
TPO
Perturbations (19)
PI3K/mTOR inhib.
Inhibitor alone
PMA/ionomycin
Healthy donor marrow (7)
Diagnosis AML marrow (18)
Matched relapse AML marrow (3)
Courtesy of Nolan Lab.

25. Outline Collect single data perturbation data using CyTOF
Identify homogeneous cell types based on surface markers
Build a “nested effects” graphical drug model in each cell type based on intracellular change; estimate a “mixture nested effects”
Determine a scoring function of optimizing drug combinations
identify homogeneous cell types based on surface markers
build a “nested effects” graphical model of a drug response in each cell type based on intracellular change; estimate a “mixture nested effects”
determine a scoring function of optimizing drug combinations 1
Decision tree

26. CCAST: Classification, Clustering and Sorting Tree
Anchang et al., PLoS Computational Biology, 2014.

27. CCAST identifies more homogeneous B-cell subpopulations
Anchang et al., PLoS Computational Biology, 2014.

28. Outline Collect single data drug response data using CyTOF
Identify homogeneous cell types based on surface markers
Build a “nested effects” graphical drug model in each cell type based on intracellular change; estimate a “mixture nested effects”
Determine a scoring function of optimizing drug combinations
identify homogeneous cell types based on surface markers
build a “nested effects” graphical model of a drug response in each cell type based on intracellular change; estimate a “mixture nested effects”
determine a scoring function of optimizing drug combinations 1
Decision tree

29. Graphical Models of Nested Drug Effects
An effect represents a change in protein marker following drug intervention
Different drug effect subsets can be observed
In a graphical model of the drugs D1 and D2, an edge from D1 to D2 indicaes that the effects of D2 are nested in the effects of D1
Node represent the drugs
An edge from D1 to D2 indicaes that the effects of D2 are nested in the effects of D1
An effect represents a change in signal from control drug intervention
Different drug effect subsets can be observed

30. Objective Function of DRUG NEM
Given a drug response single cell data from n drugs and m targets, identify an optimal drug regimen as minimum number of drugs that maximum number of markers effected. 1
Decision tree

31. Multi-drug experiment on HeLa cells under TRAlL stimulation
Study design
Stimulation: TRAIL (Base line treatment)
Inhibitors: JNK 1, GDC, GSK, SB
Cell states : Apoptotic-like and survivor-like
Intracellular markers: 1
Decision tree

32. MNEMs indicate pP38 MAPK (SB) and Mek (GSK) inhibitors are important candidates for combination therapy
We would SB and GSK to top the list of best 2 drug combinations 1
Decision tree

33. Summary
SPD infers hierarchical ordering of tumors based on gene experssion
SPADE infers hierarchical ordering among single cells based on CyTOF data
DRUGMNEM generates drug combination hypotheses using single drug information based on measurements of intratumor heterogeneity

34. Acknowledgements Benedict Anchang Peng Qiu Kara Davis Harris Feinberg
Sean Bendall
Robert Tibshirani
Garry Nolan
NCI Integrative Cancer Biology Program (ICBP)