Presentation given at Laboratory Data Management conference in Munich, Germany, June 2009.

1. Interactive Visual Data Analytics
Wolfgang G. Hoeck, Ph.D.
Senior Manager, Therapeutic Area Systems
Amgen Inc.
Laboratory Data Management, Munich, June16-17, 2009

2. Agenda
 A bit about Amgen
 Interactive visual data analytics explained
 Screening and target identification/validation
 Expectations from an interactive visual data analytics
platform
 Data formats ARE important
 Registration systems: uniquely identifying what you
are working with
 Bringing data together, the art of data mapping
 From tabular data to data networks
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 2

3. Amgen: A Biotechnology Pioneer
 Founded in 1980, Amgen was
one of the first biotechnology
companies to successfully
discover, develop and make
protein-based medicines
 Today, we’re leading the
industry in its next wave of
innovation by:
– Developing therapies in
multiple modalities
– Driving cutting-edge
research and development
– Continuing to advance the
science of biotechnological
manufacturing
3

4. Our Worldwide Presence
Cambridge, MA Norway Denmark
Toronto, ON Luxembourg Finland
West Greenwich, RI The Netherlands Sweden
Washington, DC Belgium
Estonia
Burnaby, BC Ireland
Latvia
Lithuania
Bothell, WA Russia
Seattle, WA England
Longmont, CO Czech Republic
France
Boulder, CO Poland
Switzerland
Slovakia
Hungary
Fremont, CA India
United Arab Emirates
South San Francisco, CA Hong Kong
Greece
Slovenia
Thousand Oaks, CA Austria
Germany
Mexico City, Mexico
Italy
Louisville, KY Spain Australia
Juncos, Puerto Rico Portugal New Zealand
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 4

5. Scientific Data are complex, and it’s not
going to get any better
 Target Identification & Validation
– Gene Expression of Cell Line Panels: 200 x 45000 x 3
• Understand differential expression of one or a handful of genes
• Understand expression profile in a particular cell line only
– Gene Expression of tumor samples: The Cancer Genome
Atlas
• Pilot phase: 3 tumor types - 500 GBM/Ovarian Cancer & 200 Lung Cancer
samples
• Next years: 25 more tumor types
 Compound/Target Profiling
– 400+ targets across 100’s of small molecule compounds
• Compare target properties with compound properties
 Cell Line Profiling
– 500 cell lines treated with 50 therapeutic molecules
– Each cell line has genetic abnormalities in many genes
(mutations, deletions, insertions, rearrangements, etc.)
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 5

6. Visualization of complex data must be
made available in interactive format
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 6

7. Interactive browsing of pre-analyzed data
– finding cell lines for in-vitro work
Step 1: Select gene of interest, e.g.: EGFR
Step 2: Select study of interest
Step 3: Review relative expression pattern
Step 4: Select cell line(s) for further work

8. Steps to share data in an interactive visual
format
 Determine the location of desired data (one or
multiple places and/or formats)
 Run a query against a database/data
warehouse
Power
User
 Capture a dataset(table) of rows and columns
 Decide on needed analytics & visualizations
 Determine visualization settings and state
 Share the results with other scientists
Decision
Maker
 Enable scientists to interact with data
 Enable scientists to download sets of data
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 8

9. We have many choices to visualize data …
 Table
 Bar Chart
 Box Plot
 Scatter Plot (X/Y-Plot)
 Line Chart
 Heatmap
 Parallel Coordinates Plot
(Profile Chart)
 Network
 Map
 TreeMap
 e-Northern
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 9

10. …and all choices should retain
interactivity
Filtering a set of cell line and gene alteration
data to view a particular set of cells and the
set of genes harboring deletions
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 10

11. The ideal interactive, visual data analytics
platform
Desktop Clients Zero-footprint Web Clients
 A desktop client
– Rich interactivity, visuals
– Rich analytics tools
 A server component
– Configurable security
– Configurable data access
Analysis Web
Server Server
 A web client
– Rich interactivity Stats, etc.
Server
– Easy access
 An API for extension
capabilities DB1 DB2 DB3
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 11

12. From Desktop Client to Analysis Server
 Data Access  Data Analysis
– From files – Clustering Methods
– From databases • Hierarchical
– From clipboard • K-Means
– From services • PCA
• SOM
 Data Manipulations – Profile Searching
– Data Mapping
– Data Merging  Documentation
– Calculations – Space to explain what was done
– Data Transformations
 Data Content
 Visualizations – Tabular Format
– Table – Multiple Tables
– X/Y-Plot – Relationships between Tables
– Bar Chart
 Data Security
– Parallel Coordinate Plot
– Group Level Security
– Box-Plot
– Function Level Security
– Networks
– Integration with Corporate LDAP
 Data Storage
 Action Logging
– One or many tabular datasets
– Who, When, What
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 12

13. About Data Formats
 Tall-Skinny
– aka non-pivoted data format
– Each row represents a single event
 Short-Wide
– aka pivoted data format
– Each rows represents a summary of
events in particular circumstances
– Typically results in “data loss”
 Subject-Verb-Object
– aka network data format
– aka nodes and edges
– Represent complex data relationships,
i.e.: everything has a potential many-to-
many relationship
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 13

14. We are dealing with a Complex Data Concept
Network – register your entities!
is critical in Disease
Project Target
has a
is critical in
BioProcess
is represented by
works in
Gene Pathway occurs in
is translated into
is functional in
has a has a
Protein
Protein
Gene is expressed in Status
Status Diff.Expressed
Postt.Modified
Wildtype
Mutated Cell Line is derived from
Diff.Expressed
Amplified/Deleted
Tissue
VIBEvents, Laboratory Data Management Conference, Munich, June 16/17th, 2009 14

15. Data Assembly and Integration
Contract
Human Gene Amgen
Screening Results KinomeTree
Nomenclature Project/Compound
on monthly Kinase Map
Database Association
spreadsheets
• POC/Kd values • Gene Symbol • Kinase • Compound
• Entrez Gene • Full Name Classification registered for
Symbol • Gene Synonyms • Manual mapping specific Amgen
• Compound of Gene Symbols Project
Concentration to Kinome
• Compound ID classes
Contract Screening
Data Assembly
Data get assembled in Spotfire based on
in Desktop Client matching data keys such as Gene Symbol
or CompoundID. Visualizations are prepared
Publication Step based on scientist’s input. Filters are
organized according to frequency of usage.
Adjustments can typically be made in a
couple of hours. The final file is published
Contract Screening into a web-library accessible via hyperlink.
Data Assembly in Announcements are made via e-mail and
Web Client embedded hyperlink.
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 15

16. Viewing and interacting with integrated
data
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009

17. Biology Visualizations – Pathway Example
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 17

18. Network Visualizations – The hairball
principle
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 18

19. Network Visualizations – The hairball
principle resolved
Tools to connect Nodes
Kidney
Tools to extend Nodes
Bladder
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 19

20. Combining tabular & network
visualizations
Step 1:
Select Disease, then select
Therapeutic Molecule
Step 2:
Study Therapeutic Molecule
network
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 20

21. Concluding Thoughts
 Interactive visualizations are a key to making complex
data shareable and understandable
 If interactivity is self-explanatory, adoption is very
rapid – nobody wants to read a manual
 Analytics can be accomplished in the hands of the
power user, it does not need to be available for
everyone
 Data complexity is not getting any simpler, however,
with more sophisticated tools even complex data can
be made accessible and understandable
Thank you for your time and interest
Wolfgang G. Hoeck, Ph.D., Laboratory Data Management Conference, Munich, June 16/17 th, 2009 21