1. Bio-IT
Pek Yee Lum, Ph.D.
VP Life Sciences
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2. Today’s Topics
What are the problems we face with big data today in the drug
discovery and development world?
What problems can Ayasdi solve for you?
Why topology?
Patient stratification using topology
Unveiling our NGS pipeline and genome browser
Summary
© 2012 Ayasdi inc.
2

3. Today’s Topics
What are the problems we face with big data today in the drug
discovery and development world?
What problems can Ayasdi solve for you?
Why topology?
Patient stratification using topology
Unveiling our NGS pipeline and genome browser
Summary
© 2012 Ayasdi inc.
3

4. Big data, bigger problems
for drug discovery and development
• Ever-growing complex and disparate datasets
• Scalability issues
• NGS raw data sometimes as large as 1TB per sample
• Accessing data no longer simple for the untrained users
• New IT infrastructure for every new problem
• Every new data type needs custom tools
• User experience does not exist today
• Bioinformatics tools are not integrated
• Analysis and visualization is disparate
• Accelerating the discovery process requires rethinking the
analysis workflow and streamlining its computational
infrastructure
Problems in drug discovery and development © 2012 Ayasdi inc.
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5. Data analysis landscape today
R
Cytoscape
Database
Spotfire
Matlab
Math
Writing code
Cloud
Problems in drug discovery and development © 2012 Ayasdi inc.
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6. Biological Complexity on top of
data problems
Diseases are often complex. Many components work in synergy
for disease manifestation.
The need to identify perhaps not a single drug target
but multiple targets that work as a network
Human population is heterogenous- drugs fail because of the
inability to stratify the patient population
The need to identify biomarkers that work for patient
stratification to decrease risk of adverse events and
lack of efficacy
Problems in drug discovery and development © 2012 Ayasdi inc.
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7. Today’s Topics
What are the problems we face with big data today in the drug
discovery and development world?
What problems can Ayasdi solve for you?
Why topology?
Patient stratification using topology
Unveiling our NGS pipeline and genome browser
Summary
© 2012 Ayasdi inc.
7

8. Ayasdi Iris increases probability of success (POS) and
shrinks time to market
Discovery of subtle patterns in a sea of noisy data
Handling of all data- large or small on the cloud
Fusing disparate data sets with ease
Access to critical public data on demand
Allows collaboration for all types of stakeholders on one
platform
The Ayasdi solution © 2012 Ayasdi inc.
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9. Today’s Topics
What are the problems we face with big data today in the drug
discovery and development world?
What problems can Ayasdi solve for you?
Why topology?
Patient stratification using topology
Unveiling our NGS pipeline and genome browser
Summary
© 2012 Ayasdi inc.
9

10. Solution Math+CS+UX platform
Data has Shape
and Shape has Meaning
Data has shape © 2012 Ayasdi inc.
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11. What is shape ?
If Age, Weight and Height were In reality, age, weight and height are
distributed randomly correlated and that data has a shape
Data has shape © 2012 Ayasdi inc.
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12. What is shape ?
Ayasdi Iris identifies the shape or pattern in data
Data has shape © 2012 Ayasdi inc.
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13. Why Topological Data Analysis
for drug discovery and development
1. Coordinate free representations are vital when studying data
collected using different technologies- lots of public data available,
many studies done at different times, different data types collected
2. Deformation invariance introduces robustness into the analysis,
which is important in the study of real world data- biological
heterogeneity is complex and needs an approach that is deformation
(variation) resistant
3. Compressed representations are obviously important when one is
dealing with very large data sets- with high dimensional omics data
and Next Gen sequencing getting more affordable, the amount of
data is increasing exponentially
Why topology ? © 2012 Ayasdi inc.
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14. Ayasdi Iris
Uses principles of geometry to find shape (pattern)
in data
Works across and for any type of data
Works with any amount of data
Generates and validates hypotheses
Quick, interactive results
Why topology ? © 2012 Ayasdi inc.
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15. Patient stratification - Results
gene expression profiling of breast tumors
Identified a sub-group of patients that are triple
negative with very good prognosis
Identified a sub-group of patients that are Luminal A
but with perfect survival (published PNAS 2011)
These groups were identified in independent datasets
These sub-groups were hard to find using conventional
methods
Patient stratification © 2012 Ayasdi inc.
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16. Today’s Topics
What are the problems we face with big data today in the drug
discovery and development world?
What problems can Ayasdi solve for you?
Why topology?
Patient stratification using topology
Unveiling our NGS pipeline and genome browser
Summary
© 2012 Ayasdi inc.
16

17. Each node contains
subsets of patients
These patients are
eccentric (away from the
center of the data)
These patients are close
to the center of the data
Color scheme
Topological Map of Patient-Patient Relationships according to the molecular
characteristics of their tumors (in this case, gene expression)
Patient stratification © 2012 Ayasdi inc.
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18. Zero event death
Low event death
High event death
High event death
Low event death
Mixed event death
These patients are
eccentric (away from the
center of the data)
Zero event death
Color scheme
Color scheme
Patient stratification Zero event death
© 2012 Ayasdi inc.
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19. patients did not survive 10 yrs
B
D
E
A
Triple Negative
HER2-, ESR-, PGR-
C
patients survived 10 yrs
Patient stratification © 2012 Ayasdi inc.
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20. NKI data
high ESR1
death
low ESR1
survived
high ESR1
low ESR1 Topological maps from
GSE2304 two independent cancer
data sets are very
relapsed
low ESR1
similar
high ESR1
relapse
no
high ESR1
low ESR1
© 2012 Ayasdi inc.
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21. Conventional Methods
subtypes difficult to identify
Clustering PCA
ER- did not survive
ER- survived
Patient stratification © 2012 Ayasdi inc.
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22. Next Generation Sequencing
unveiling our solution
DNA-Seq Pipeline
BAMs VCF IRIS
RNA-Seq Pipeline
BAMs cuffcomp/RPKM IRIS
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23. 1000 genomes DNA-seq data
population classification
Africans
East Asians
Caucasians
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24. Identification and visualization of significant
exon variants using PCA
Han Chinese and Japanese cannot be easily distinguished using
PCA
Han Chinese (grey)
Japanese (red)
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25. Identification and visualization of significant
variants
Han Chinese and Japanese can be easily distinguished and
visualized with TDA
Han Chinese
rs2294008
TT
chr8
Japanese
Poster #3: Navigating Next Generation Sequencing Data using Topological Data Analysis and Iris
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26. Today’s Agenda
What are the problems we face in drug discovery and
development?
What problems can Ayasdi solve for you?
Why topology?
Patient stratification using topology
Summary
© 2012 Ayasdi inc.
26

27. Ayasdi Iris platform
Ayasdi Iris Life Sciences Edition
All analyses performed on the cloud on secure servers
PubMed
GO
Bypass the need to invest in expensive hardware and
KEGG
PPI
database administration
GEO
Interactive Network
Visualization
Integrated Statistics
Algorithms
Integrated Public
Datasets
Flexibility to start your analysis immediately- just upload
your data
Ayasdi Iris Cloud Platform
Access to public data on demand
Analysis and Visualization
Topological Network Projections Network Histograms/ Dendro-
Mapping Analysis (e.g. PCA) Visualization Scatterplots grams
Scalable Distributed Datastore
Proprietary and Public data sources
Gene
mRNA SNP Clinical NGS PubMed
Expression
Ayasdi Iris platform © 2012 Ayasdi inc.
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28. Ayasdi Iris increases probability of success (POS) and
shrinks time to market
Discovery of subtle patterns in a sea of noisy data
Handling of all data- large or small on the cloud
Fusing disparate data sets with ease
Access to critical public data on demand
Allows collaboration for all types of stakeholders on one
platform
The Ayasdi solution © 2012 Ayasdi inc.
28

29. Contact us for more information
www.ayasdi.com
pek@ayasdi.com
info@ayasdi.com
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