In the tranSMART 17.1 development project The Hyve is enriching tranSMART with support for time series, samples and cross-study concepts. The project is lead by the tranSMART Foundation and supported by Sanofi, Pfizer, Roche and AbbVie. Read more at http://thehyve.nl/transmart-17-1-time-series-samples-cross-study-concepts-and-more/. As presented on the tranSMART Foundation Annual Meeting 2016: https://youtu.be/k1eKMhXbqOA?t=5h6m57s

1. 27 Oct, 2016
Ward Weistra – The Hyve
ward@thehyve.nl / @wardweistra
TranSMART Pro 17.1 project
Functional overview

2. 2
Agenda
● Backstory
● Technical improvements (briefly)
● Functional improvements
○ New crucial functionality
○ Backwards compatibility & upgrade path
○ Documentation & tests
● Project structure

3. Backstory
What are we solving here?

4. 4
What are we solving here?
1. Missing crucial functionality
○ Time series, Samples, Cross-study concepts
○ Transcript-level RNA-Seq data
○ Large file storage
2. Code problems: ‘technical debt’
○ Monolithic architecture
○ Lack of automated tests
○ Old version Grails/Java, many code repositories
○ No documentation of database

5. 5
Backend only
● Stable, commercial grade core
○ Decoupling of the backend from the transmartApp User
Interface via the REST API
○ Towards an ecosystem of User interfaces on top of the
tranSMART backend
● Why only the backend?
○ transmartApp has issues: assumptions, old layered code
○ Enough work already
○ Current data will still work in transmartApp
○ 17.1 project will be part of the full 17.1 release

6. 6
It’s all open! http://bit.ly/171documents
DESIGN
REQUIREMENTS

7. Time series, samples and
cross study concepts
i2b2 database alignment and extension

8. 8
History
● tranSMART was developed on top of i2b2
to combine clinical with omics data
● i2b2 has cross-study concepts (with ontology codes) and
support for storing samples and time series data
● tranSMART lost this:
○ Concepts are study specific
○ User Interface assumes a patient-concept pair to have
one value
“Patient John has for concept heart rate the value 80 bpm”
“Concept age in study A is not the same as
concept age in study B”

9. 9
Time series
● Absolute time
○ Blood measurement with start (and end) date+time
○ Hospital visit per patient grouping multiple measurements
with start (and end) date+time
● Relative time
○ ‘Baseline’ (0 days) or ‘Week 1’ (7 days) observation
○ Shared between patients
● Ordinal time
○ First, second and third observation

10. 10
Samples
● Differentiated by ‘modifiers’
○ Tumor and normal measurement
○ Multiple doses
○ Multiple tissues
● Differentiated only by a number ‘instance_num’
○ Multiple replicas

11. 11
Cross-study concepts
● We want ‘Age’ in different studies to be the same
concept
○ Get subjects which match ‘Age > 50’ from ALL studies
○ Use ontology codes, eg. from an external ontology server
● Difference with i2b2: tranSMART is study based
○ Study based data loading
○ Study based data access
● We need to support both

12. The (relevant part of the) 17.1 data model
12

13. 13
Time series and samples - Example 1
A study with tumor and normal
samples
● Multiple observations for the same patient
differentiated by the modifier ‘tissue type’.
● The Start_Date (and End_Date) for the
observation will be empty.
● All observations will be linked to the same
trial_visit, which will link to the study.

14. Clinical trial with multiple timepoints
(Baseline, Week 1, Week 2)
● Multiple observations for the same patient
differentiated by their trial_visit.
● All observations will be linked to one of the
available trial_visits, which will link to the
study. Each trial_visit has a Label
(Baseline), a Unit (Days) and a Value (0, 7
and 14).
14
Time series and samples - Example 2

15. 15
Time series and samples - Example 3 (1/2)
An EHR dataset with observation and
visit timestamps and samples.
● Multiple observations for the same patient
differentiated by their observation
Start_Date, visit and Instance_Num.
● The Start_Date (and End_Date) for the
observation will be set to a timestamp.
● The Instance_Num will be set starting
from 1 for multiple samples on the same
observation Start_Date and visit.

16. 16
Time series and samples - Example 3 (2/2)
An EHR dataset with observation and
visit timestamps and samples.
● The observations from a patient will be
linked one visit per hospital visit.
● The Start_Date (and End_Date) for the
visit will be set to a timestamp including
time and date for the hospital visit.
● All observations will be linked to the same
trial_visit, which will link to the study.

17. ● Querying observations based on a combination of:
○ start time, end time
○ aggregated time series/samples values:
■ minimum, maximum, average
○ temporal constraints on sets of events:
■ define sets of events (e.g. A: all blood pressure readings for a
patient, B: the first use of drug X by the patient)
■ Specify constraints (e.g. All of A happen at least one week after
any of B).
17
Querying time series and samples

18. 18
● Querying patients based on observations:
○ Certain constraints are valid for any or for all observations
for the patient
○ Return patients where all observations of high blood
pressure occur after supply of drug X.
● Querying for aggregated values for numerical data:
○ minimum, maximum, average
Querying time series and samples

19. Transcript-level RNA-Seq

20. 20
TranSMART data types
● Metadata
○ Study, concept, patient metadata / Links to source data
● Clinical / NHTMP / Derived imaging data / Biobanking data
○ numerical and categorical
● Gene expression - RNA
○ Micro array
○ mRNAseq, miRNAseq - only linked to genes
○ qPCR miRNA
● Copy Number Variation data (Array CGH)
● Small Genomic Variants (SNP, indel – VCF format)
● Large genomic rearrangements
● Proteomics
○ Protein mass spectrometry – peptide or protein quantities
○ Immunoassay Rule-based medicine (RBM) – analyte concentrations
● Metabolomics
○ Metabolite quantities

21. 21
Transcript-level RNA-Seq data
● Adding a data type where measurements
(readcount, normalised readcount and z-score)
are linked to transcripts instead of genes
● Dictionary will link genes to transcript for searching
REF_ID GPL_ID CHROMOSOME START_BP END_BP TRANSCRIPT
ENST0001 RNASEQ_TRANSCRIPT_ANNOT X 1000 1100 TR1
2 RNASEQ_TRANSCRIPT_ANNOT Y 2000 2500
3 RNASEQ_TRANSCRIPT_ANNOT 10 3000 4000 TR2

22. Large file storage
Linking with Arvados

23. 23
Linking with Arvados: Scalable Genomics
● Linking files in Arvados to studies in tranSMART
for the storage of large files (eg BAM, VCF)
● If possible:
○ Align with linking files in MongoDB to studies
● Eventual UI goals:
○ See in tranSMART which Arvados files linked to study
○ Start from tranSMART a Arvados workflow on Arvados
files

24. Backwards compatibility
and the upgrade path

25. 25
Upgrade path / data migration
● If you have your data in 16.1 or 16.2
○ There will be a data migration path provided to 17.1

26. 26
Backwards compatibility
If you have your data in 16.1 or 16.2
● The current user interface (transmartApp) will still work on current
data
○ So only for data without time series, samples,
○ Plugins are not guaranteed to work (but might very well)
● The current REST clients will still work with the V1 version of the
REST API

27. Documentation and
automated testing

28. 28
Documentation
● Documentation will be provided for all available
REST and Core API calls
● Data model design will describe the complete data
model

29. 29
Automated testing
● The Core API will have
unit and integration tests
with a minimal test
coverage of 70%.
● The RESTful API will have
automated functional
tests for all API calls.

30. Project structure
GC, TSC and PMC

31. 31
Involved parties
● tranSMART Pro
○ Leadership: TranSMART Foundation
○ Sponsors:
■ Pfizer
■ Roche
■ AbbVie
■ Sanofi
○ Execution: The Hyve

32. ● GC: Business decisions
Sponsors + TSF
● TSC: Technical decisions
Sponsors + TSF
● EUTAB: Represent end users
Sponsors (+ selected experts)
● PMC: Direction for the release
TSC + Partners (ITTM, Curoverse, Clarivate)
32
Governance
17.1 Project
Management
Committee
tranSMART Pro
Technical Steering
Committee
tranSMART Pro
Governing Committee
The Hyve
tranSMART Pro
End User Technical
Advisory Board
17.1 project

33. 33
Technical Steering Committee
● Pfizer: Jay Bergeron
● Roche: Thomas Thies
● Sanofi: Heike Schürmann
● AbbVie: Samantha Lipsky
● Chair + customer representative:
Prof. Yi-Ke Guo (Imperial College,
tranSMART Foundation)

34. 34
The Hyve team
● Project manager: Erik van Eeuwijk
● Business analyst: Ward Weistra (me)
● Technical lead: Gijs Kant
● Development team:
○ Piotr Zakrzewski (present)
○ Ruslan Forostianov (present)
○ Jan Kanis
○ Ewelina Grudzien
○ Olaf Meuwese
○ Barteld Klasens (automated testing)

35. 35
Timeline
● Module A and B: End of 2016
○ Time series, samples, cross-study concepts
○ Transcript-level RNA-Seq
● Module C and project release: End of Q1 2017
○ Linking with Arvados
● TranSMART 17.1 version release: Q2 2017
○ Integration with all community developments