1. eTRIKS Data Harmonization Service Platform
Putting Standards IntoAction
TranSMART Foundation Annual Meeting
San Diego 25 October 2016
Ibrahim Emam
Data Science Institute
Imperial College London

2. The eTRIKS Project
 > 50 safety and efficacy IMI projects
generating or aggregating pre-clinical and clinical data for analysis, across many different
disease areas.
 eTRIKS to provide a common infrastructure and service
to support pre-competitive (& competitive) cross-institutional Translational Research
 Service Project
~80% of project activities driven by demand from supported IMI projects
 Members:
10 Pharma, 3 Academic, 1 standards, 2 Commercial Suppliers

3. RNA Profiling microarray
Protein Profiling CyTOF Clinical Cohort B data
Metabolic Profiling
Animal model data
Clinical Cohort A data
DATA
SOURCES
ANALYSIS
ad-hoc INTEGRATION
RESULTS
Data integration and harmonization challenge

4. Losing the big picture
Focus on technology vs. biology
Silos of curation expertise
Obstacles To Proper Data Integration
Standards don‘t go beyond
recommendations
Focus on hypothesis-driven analysis
TranSMART?
Flexible for data analysis but rather rigid for data management

5. Towards FAIR Data Principles
Find-ability
Accessibility
Interoperability
Reusability
META DATA PRESERVATION IS KEY
STANDARDS TO THE RESCUE

6. THE STANDARDS DOGMA
Unrewarding
Overwhelming
Restrictive
Tedious
Controlled
Vocabulary?

7. Domain Aware Platform
Facilitate adoption of community standards
Bring curation know-how to the data owners
eTRIKS Harmonization Service Goals
Enlisting standards in eHS platform
Reward Data Generators
Data Exploratory
Losing the big picture
Focus on technology vs. biology
Silos of curation expertise
Standards don‘t go beyond
recommendations
Focus on hypothesis-driven
analysis
The Challenges

8. ANALYTICS
DATA MINING
REPORTING
Stage
Transform
Archive
tranSMART
EXPLORATORY
DISCOVERY
SEARCH
DATA REDUCTION
HYPOTHESIS
TESTING
ANALYSIS
Data Harmonization Service Data Analytics
eTRIKS
Harmonization
Modules
Biospeak
eTRIKS Ecosystem
Data Capture tools

9. eTRIKS Data Harmonization Service
 The TR Meta-data framework
 The Platform

10. eHS Platform Modules
Harmonized Data Repository (BioSpeak-db)
…for storing, exploring and integrating harmonized TR data
eTRIKS Curation Modules
…for servicing and managing metadata standards across projects
Data harmonization workflows
…for data standardization, curation and analytical platforms integration

11. Key Software Technologies
 Back-end
 ASP.NET core Web API
 Storage
 MySQL and MongoDB
 Front-end
 AngularJS
 Data visualization
 DC.js (Dimensional Charting)
 Based on D3.js and crossfilter.js

12. Core
Domain
Model
Data-access
Interfaces
Services
Infrastructure
DataAccess Persistence
WebAPI
Front end application
ViewsControllers ViewModel
Services
DependencyResolution
SQL
(App Domain)
MongoDB
(datasets &
documents)
.NETWebAPIBack-endApplication
AngularJS
Front-endApp

13. Standalone data
collection UI
Standard
CDISC clinical
datasets
Local
DB
On-site Data Registry
with data-exploratory
capabilities
MDS instance
Standalone data
collection UI
Standard
CDISC clinical
datasets
Local
DB
On-site Data Registry
with data-exploratory
capabilities
Neurologists
Participating
Centre 1
MDS instance
Standalone
data
collection UI
Standard
CDISC
clinical
datasets
Local
DB
Neurologist
Participating
Centre 2
Neurologist
Participating
Centre 3
Direct shared studies / sub-
studies anonymized data
between centres 1 & 2
Submitted anonymized data to the
central Harmonized Repository
Central
Harmonized Data
Repository
Platform Deployment Plan

14. eHS Metadata Framework
Approach To Data Harmonization
Multi-faceted integrated framework describing different resources at
different levels of granularity (a meta-model)
 Study Design
 Datasets
 Observations

15. Assays Samples Subjects Clinical Assessments
Arms Epochs ObjectivesVisits Factors
Design
Elements
Molecular data Study Design Demographics
Structural Metadata
DATASET MODEL
Contextual Metadata
TRANSLATIONAL
RESEARCH MODEL
Descriptive Metadata
OBSERVATION MODEL
Laboratory FindingsTarget annotation
ObservationObservation Descriptor
Dataset Templates
Domain
model
CDISC-SDTM
ISA-TAB
Observation Definition

16. eHS Approach to data harmonization
 Step 1
 Structural organization of data into standard dataset templates
 (Syntactic harmonization)
 Step 2
 Content standardization – controlled vocabularies
 (Semantic harmonization)
 Step 3
 Domain–aware Integration

17. Structured Datasets
Harmonization Modules
CDSIC SHARE API
Ontology Look-up service
Harmonization module
BiospeakAPI
Project Metadata
Clinical dataMolecular data
Assays
Samples
Study Context
Subjects
Clinical
Assessments
Observa ons Observa ons
Harmonized
Observations
Structuration Harmonization
Observation
Descriptors
Standard Dataset
Templates & controlled
vocabularies
Harmonized Observations
and TR elements
TR Project
unstructured
data files
Exploration
Integration
eHS Application
1 2 3
The eTRIKS Harmonization Service
Workflow & Architecture

18. Demo
http://ehs.biospeak.solutions/sandbox/app/

19. 1- Project Setup
Standard Templates
Standard
Observation
Descriptors
Configurations for project custom data requirements

20. 2- Data-staging

21. 3- Integrated UI for data exploration
Subject Data

22. 3- Integrated UI view for data
exploration
Clinical Data

23. 3- Integrated UI for data exploration
Molecular assay data

24. Dataset filters
4- Dataset export
Export criteria based on the
selected observation, filters
and groups explored in the
viewer
Create research-focused
datasets in preparation for
analysis
Save datasets and tag them
with meta-data
Maintaining the association
between study design
elements, relationship
between subjects and model
types, samples, and assays
Support dataset publications

25. Datatable
4- Dataset export

26. tranSMART integration : the eTRIKS transmart master tree
Focus the usage of
tranSMART as a platform
for focused research-
driven analytics and
hypothesis driven data
analysis
Reduces the curation
effort usually required to
build and map all of a
study’s data to a
tranSMART tree
One data source, many flexible tree representations
Users decide ‘what’ goes into the tree and ‘how’ they
want it structured to best fit with their analytical plans
5- EXSTENSIONS

27. Availability
 Currently in testing phase with different internal eTRIKS
projects
 Demo site
 http://ehs.biospeak.solutions/sandbox/app
 http://ehs.biospeak.solutions/blog/
 Docker container distribution scheduled for release in
November
 Public github repository will follow

28. Dilshan Silva, Imperial College
Chen Ze, Imperial College
Florian Guitton, Imperial College
Philippe Rocca-Serra, Oxford e-Research Centre, Oxford University
Francisco Bonachella Capdevilla, J & J
Dorina Bratfalean, CDISC
Paul Houston, CDISC
Yike Guo, Imperial College
Acknowledgments

29. THANKS FOR
LISTENING
i.emam@imperial.ac.uk