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27 Oct, 2016
Ward Weistra – The Hyve
ward@thehyve.nl / @wardweistra
TranSMART Pro 17.1 project
Functional overview
2
Agenda
● Backstory
● Technical improvements (briefly)
● Functional improvements
○ New crucial functionality
○ Backwards ...
Backstory
What are we solving here?
4
What are we solving here?
1. Missing crucial functionality
○ Time series, Samples, Cross-study concepts
○ Transcript-lev...
5
Backend only
● Stable, commercial grade core
○ Decoupling of the backend from the transmartApp User
Interface via the RE...
6
It’s all open! http://bit.ly/171documents
DESIGN
REQUIREMENTS
Time series, samples and
cross study concepts
i2b2 database alignment and extension
8
History
● tranSMART was developed on top of i2b2
to combine clinical with omics data
● i2b2 has cross-study concepts (wi...
9
Time series
● Absolute time
○ Blood measurement with start (and end) date+time
○ Hospital visit per patient grouping mul...
10
Samples
● Differentiated by ‘modifiers’
○ Tumor and normal measurement
○ Multiple doses
○ Multiple tissues
● Differenti...
11
Cross-study concepts
● We want ‘Age’ in different studies to be the same
concept
○ Get subjects which match ‘Age > 50’ ...
The (relevant part of the) 17.1 data model
12
13
Time series and samples - Example 1
A study with tumor and normal
samples
● Multiple observations for the same patient
...
Clinical trial with multiple timepoints
(Baseline, Week 1, Week 2)
● Multiple observations for the same patient
differenti...
15
Time series and samples - Example 3 (1/2)
An EHR dataset with observation and
visit timestamps and samples.
● Multiple ...
16
Time series and samples - Example 3 (2/2)
An EHR dataset with observation and
visit timestamps and samples.
● The obser...
● Querying observations based on a combination of:
○ start time, end time
○ aggregated time series/samples values:
■ minim...
18
● Querying patients based on observations:
○ Certain constraints are valid for any or for all observations
for the pati...
Transcript-level RNA-Seq
20
TranSMART data types
● Metadata
○ Study, concept, patient metadata / Links to source data
● Clinical / NHTMP / Derived ...
21
Transcript-level RNA-Seq data
● Adding a data type where measurements
(readcount, normalised readcount and z-score)
are...
Large file storage
Linking with Arvados
23
Linking with Arvados: Scalable Genomics
● Linking files in Arvados to studies in tranSMART
for the storage of large fil...
Backwards compatibility
and the upgrade path
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...
26
Backwards compatibility
If you have your data in 16.1 or 16.2
● The current user interface (transmartApp) will still wo...
Documentation and
automated testing
28
Documentation
● Documentation will be provided for all available
REST and Core API calls
● Data model design will descr...
29
Automated testing
● The Core API will have
unit and integration tests
with a minimal test
coverage of 70%.
● The RESTfu...
Project structure
GC, TSC and PMC
31
Involved parties
● tranSMART Pro
○ Leadership: TranSMART Foundation
○ Sponsors:
■ Pfizer
■ Roche
■ AbbVie
■ Sanofi
○ Ex...
● GC: Business decisions
Sponsors + TSF
● TSC: Technical decisions
Sponsors + TSF
● EUTAB: Represent end users
Sponsors (+...
33
Technical Steering Committee
● Pfizer: Jay Bergeron
● Roche: Thomas Thies
● Sanofi: Heike Schürmann
● AbbVie: Samantha ...
34
The Hyve team
● Project manager: Erik van Eeuwijk
● Business analyst: Ward Weistra (me)
● Technical lead: Gijs Kant
● D...
35
Timeline
● Module A and B: End of 2016
○ Time series, samples, cross-study concepts
○ Transcript-level RNA-Seq
● Module...
A functional overview of the tranSMART 17.1 development project
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A functional overview of the tranSMART 17.1 development project

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

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A functional overview of the tranSMART 17.1 development project

  1. 1. 27 Oct, 2016 Ward Weistra – The Hyve ward@thehyve.nl / @wardweistra TranSMART Pro 17.1 project Functional overview
  2. 2. 2 Agenda ● Backstory ● Technical improvements (briefly) ● Functional improvements ○ New crucial functionality ○ Backwards compatibility & upgrade path ○ Documentation & tests ● Project structure
  3. 3. Backstory What are we solving here?
  4. 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. 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. 6 It’s all open! http://bit.ly/171documents DESIGN REQUIREMENTS
  7. 7. Time series, samples and cross study concepts i2b2 database alignment and extension
  8. 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. 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. 10 Samples ● Differentiated by ‘modifiers’ ○ Tumor and normal measurement ○ Multiple doses ○ Multiple tissues ● Differentiated only by a number ‘instance_num’ ○ Multiple replicas
  11. 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. 12. The (relevant part of the) 17.1 data model 12
  13. 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. 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. 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. 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. 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. 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. 19. Transcript-level RNA-Seq
  20. 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. 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. 22. Large file storage Linking with Arvados
  23. 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. 24. Backwards compatibility and the upgrade path
  25. 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. 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. 27. Documentation and automated testing
  28. 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. 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. 30. Project structure GC, TSC and PMC
  31. 31. 31 Involved parties ● tranSMART Pro ○ Leadership: TranSMART Foundation ○ Sponsors: ■ Pfizer ■ Roche ■ AbbVie ■ Sanofi ○ Execution: The Hyve
  32. 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. 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. 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. 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

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