The INCF Digital Atlasing Infrastructure (INCF-DAI) in-depth discussion

1. The INCF Digital Atlasing Infrastructure (INCF-DAI)in-depth discussion Ilya Zaslavsky (UCSD) Pilsen, September 4, 2009

2. OUTLINE • What is INCF-DAI • A system of distributed atlas hubs and services Anatomy of a service Spatial transformations • INCF Central • The current status: introduction to the demo • How do you join the INCF atlasing network • Summary, current limitations, future work

3. Purpose of this INCF program: To enhance the interoperability, accessibility, and sharing of spatial data sets in neuroscience: INCF-sponsored standards

4. The Integration Problem: Whose standard?

5. What are we trying to do? • We have many individual fragmented efforts, each using separate semantic and spatial frameworks • Each is a research effort, i.e. constantly changing, imperfect, not necessarily designed for integration • Our focus is not on perfecting tools, but integrating them into an interoperable system, by creating an INCF-DAI architecture and standardizing data exchange • This is an iterative process: integration experiment -> finding “weakest links” -> prioritizing tool improvement -> new level of integration, with enhanced integration models…

6. What is INCF-DAI • A system of distributed atlas hubs and services Anatomy of a service Spatial transformations • INCF Central • The current status: introduction to the demo • How do you join the INCF atlasing network • Summary, current limitations, future work

7. Managing different data types in INCF-DAI

8. INCF Central, and INCF Services and Hubs WHS Service ABA Service INCF Central: Service registry, Registry of spaces, Metadata catalog Other… UCSDService Other… EMAGEService

9. Atlas Services (As of August 2009; planned services in italic) Service = some functionality that you can call over the web in a standard manner

10. Internals of a service(with ABAServiceController as an Example) • Service definition resides at the INCF server (as with all current services) • Some methods are implemented locally: core methods, all space translations (with mandatory bridge to WHS), also structure lookup by POI • Other methods wrap ABA API calls: GetCorrelationMap, Get2DImageatPOI, GetGenesAtPOI, GetGenesByStructure • The services return either HTML (currently), or XML/txt • GetCapabilitiesand DescribeProcess comply with OGC’s Web Processing Service (WPS) specification API: Application Programming Interface

11. Some examples of service signatures • Coordinate transformation: • http://132.239.131.188:8080/incf-services/service/ABAServiceController?request=SpaceTransformation&FromSRSCode=WHS&ToSRSCode=AGEA&x=X&y=Y&z=Z&output=txt • Structure name lookup: • http://132.239.131.188:8080/incf-services/service/ABAServiceController?request=GetStructureNameByPOI&SRScode=ABAVoxel&x=X&y=Y&z=Z&filter=structures:anatomic&output=txt • Find genes: • http://132.239.131.188:8080/incf-services/service/EmageServiceController?request=GetGenesByStructure&vocabulary=ABAvoxel&term=term&filter=“structures:anatomic” &output=html Services can be chained (i.e. output of one service becomes input of another) to implement different research workflows. We attempted to make the service signatures as common across different atlas hubs as possible

12. The Central Role of Waxholm Space

13. Transformation service issues (looking at WHS to Paxinos as example) WHS is a voxel space, containing continuous information of a particular mouse’s brain, obtained elegantly from an MRI imaging. Paxinos is derived from a mouse brain sample, physically sliced into cross-sections and observed visually: • Different animals, and different shape of the brain • There is no Paxinos information between two adjacent slices • Some Paxinos slides are “out of step” with the adjacent slides • Many Paxinos boundaries are estimated and not consistent with one another, particularly in the cerebellum. Method: • Taking cross-sections of the MRI image, projecting them over Paxinos slides. • The human eye and brain is still a very powerful data processor. By looking at a WHS section over a Paxinos slide, one can see what adjustments are needed: a nudge to the left, a twist clockwise, or a dilation in one direction. Not only can the shape of the brain itself be used, but the light-and-dark patches in the MRI match with Paxinos brain regions. Eventually, each Paxinos slide can match very closely with a WHS cross-section. • Once these cross-sections are found, a clear mathematical correlation in the data gives a formula from one space to another. Additional problems: • It is virtually impossible to make a perfect match, or ascertain accuracy • The anterior commisure region in WHS mouse happened to be located slightly higher than that in the Paxinos mouse. The discrepancy distance is about one diameter. A query in the WHS ac would be likely to miss the Paxinos ac. • The Olfactory Bulb is another difficult region; its cone shape is susceptible to deformation and has different relative dimensions not only in WHS and Paxinos, but in other spaces such as ABA as well. • In some slides, while the internal regions match up very well, the outside extent of the cerebral cortex fails to match.

14. What is INCF-DAI • A system of distributed atlas hubs and services Anatomy of a service Spatial transformations • INCF Central • The current status: introduction to the demo • How do you join the INCF atlasing network • Summary, current limitations, future work

15. INCF Central, and INCF Services and Hubs WHS Service ABA Service INCF Central: Service registry, Registry of spaces, Metadata catalog Other… UCSDService Other… EMAGEService

16. Atlasing Infrastructure User Interface Present information about atlasing hubs Query atlasing hubs webservices webservices webservices INCF Spatial Server INCF Concept Server INCF Registry INCF DAI Infrastructure Components webservices webservices webservices ABA: INCF Hub EMAGE: INCF Hub UCSD: INCF Hub Harvest metadata from atlasinghubs Atlasing Hubs All components built on standards WHS canonical space and dataset Standard terminologies Standard services and exchange schemas

17. INCF Central: Registry of Atlas Spaces • INCF role may play similar role to EPSG, the authority in coordinate systems • EPSG codes (e.g. EPSG:4326 for WGS84) are ubiquitous in GIS software. EPSG:900913 is the coordinate system used in Google Maps, Yahoo Maps, Virtual Earth. • INCF may maintainits own registry ofatlas coordinatesystems (we have aprototype)

18. Other INCF Central Registries • Service registry for atlas hubs (GetCapability requests) • Spatial transformations registry, from all atlas hubs • Ontology registry and repository (PONS) • Additional metadata: replicas of local metadata catalogs and spatial registries, segmentations, etc.

19. Internal organization of the server • Why: support development by a group (different developers for web services, transformations, lookup, central database, etc.) • Code management system

20. What is INCF-DAI • A system of distributed atlas hubs and services Anatomy of a service Spatial transformations • INCF Central • The current status: introduction to the demo • How do you join the INCF atlasing network • Summary, current limitations, future work

21. Demo Contributors Neuroscience scenarios: Maryann Martone, Stephen Larson (UCSD), Jyl Boline (Informed Minds), Lydia Ng and Mike Hawrylycz (Allen Institute), Al Johnson (Duke University) WHS dataset: Al Johnson and Jeff Brandenburg (Duke University), Jonathan Nissanov and Pablo Burstein (Drexel University) Other datasets and meshes: Maryann Martone and Stephen Larson (UCSD), Lydia Ng and Chris Lau (Allen Institute) INCF Central and atlas service wrappers: Asif Memon (UCSD) INCF hubs and local services: Lydia Ng (Allen Institute), Albert Burger (MRC, UK), Kenneth McLeod (Heriot-Watt Univ, UK), Asif Memon (UCSD) Spatial transformations and anatomic structure lookup: Lydia Ng (Allen Institute), Steven Lamont and Alexander Young (UCSD) Representation of coordinate spaces: Alexander Young (UCSD) Client development: Stephen Larson and UCSD team; Seth Ruffins and UCLA team Hardware and server setup: Larry Lui (UCSD) Architecture: Albert, Fons, Jyl, Mike, Janis, Ilya Thanks to Jyl and Janis for coordination!

22. Demo overview

23. Testing transformation chains: from Paxinos Reference Plates to ABA Reference Plates via WHS and AGEA/ABA Initial point: (1.0, 4.3, 1.78) Anterior to Bregma at 1.78 mm, Fig. 16 Structure= AC (anterior commissure)

24. Testing, Step 1: Paxinos to WHS Transform to WHS using Alexander’s conversion, wrapped in Asif’s service: http://incf-dev-mapserver.crbs.ucsd.edu:8080/incf-services/service/UCSDServiceController?request=SpaceTransformation&fromSRSCode=paxinos&toSRSCode=whs&x=1.0&y=4.3&z=1.78&output=html Result = 308,642,224 The original WHS coronal cut WHS coronal cut fitted with Paxinos plate

25. Testing, Step 2: WHS to AGEA Transform to AGEA using Steve’s lookup over Lydia’s conversion matrix, wrapped in Asif’s service: http://incf-dev-mapserver.crbs.ucsd.edu:8080/incf-services/service/ABAServiceController?request=SpaceTransformation&fromSRSCode=whs&toSRSCode=AGEA&x=308&y=642&z=224&output=html Result: 3825,5650,4650 http://tirebiter.ucsd.edu/cgi-bin/get_plane.cgi?atlas=whs&view=Z&x=308&y=642&z=224 The original Looking at the result in AGEA: http://mouse.brain-map.org/agea/all_coronal?correlation&seedPoint=3825,5650,4650 http://tirebiter.ucsd.edu/cgi-bin/get_plane.cgi?atlas=whs&view=Y&x=308&y=642&z=224 WHS views

26. Testing, Step 2: WHS to AGEA; Results in AGEA The original http://mouse.brain-map.org/agea/all_coronal?correlation&seedPoint=3825,5650,4650 Wrapped in Asif’s service: http://incf-dev-mapserver.crbs.ucsd.edu:8080/incf-services/service/ABAServiceController?request=GetCorrelationMap&SRSCode=whs&x=308&y=642&z=224&filter=maptype:coronal&output=html

27. Testing, Steps 3 and 4: AGEA to ABA volume to ABA reference plates Transform to ABA (i.e. divide by 25): http://incf-dev-mapserver.crbs.ucsd.edu:8080/incf-services/service/ABAServiceController?request=SpaceTransformation&fromSRSCode=agea&toSRSCode=abavoxel&x=3825&y=5650&z=4650&output=html Result: 153,226,186 Transform to ABA reference plate coordinates, using Lydia’s formula implemented in Asif’s service: http://incf-dev-mapserver.crbs.ucsd.edu:8080/incf-services/service/ABAServiceController?request=SpaceTransformation&fromSRSCode=abavoxel&toSRSCode=abareference&x=153&y=226&z=186&output=html Result: 1.194, 5.127,1.693 The original Check the result in ABA reference atlas at http://mouse.brain-map.org/atlas/ARA/Coronal/browser.html (see http://mouse.brain-map.org/viewImage.do?imageId=130973 ) – we are in Coronal level 38, as predicted!

28. The results, again The original Paxinos

29. Placing a probe in the 3D atlas viewer

30. Lateral view, with the probe in hippocampus

31. Calling ABA services

32. Calling EMAGE services

33. Calling WHS services

34. Calling UCSD (Smart Atlas) services

35. Response from UCSD Get2DImageAtPOI

36. Lateral view, rotated

37. Responses from various ABA requests

38. Responses from various ABA requests - 2 Fine Structure Name: DG Anatomic Structure Name: HIP WHC GetStructureNameByPOI: Hc

39. Response from EMAGE GetGenesByStructure

40. What is INCF-DAI • A system of distributed atlas hubs and services Anatomy of a service Spatial transformations • INCF Central • The current status: introduction to the demo • How do you join the INCF atlasing network • Summary, current limitations, future work

41. Client INCF-DAI: path to adoption + metadata Register to Atlas Query Visualize Analyze Servers & Databases Integration APIs Spatial Transformations Integrate Data Upload Semantic Integration Spatial Integration Database Integration Standards + metadata Image APIs Microarray APIs Imaging Spatial registry Microarray Information

42. Requirements • Flexibility • Individual atlases may use different representations and data types • Atlases may support different functions • Atlases may adhere to known (already registered) or unique (but defined) spatial and semantic frameworks • There may be different amount of funding available to bring the atlas in compliance with INCF-DAI expectations • A range of approaches: • Register a GetCapabilities service, then INCF Central will harvest the rest of the metadata (implying that local coordinate system(s) and semantics are formally described and available via services): we need to provide sample software stack. This would be an ideal scenario, though need a monitoring infrastructure • Work with INCF on a hybrid service, where some functions are hosted at the atlas hub, and other are created at INCF (e.g. space descriptions and WHS transformations) • Have INCF host entire atlas

43. Development of coordinate translation services – still fairly time consuming A few simple tools to help with this: • Image slice utility: (http://<server>/cgi-bin/get_plane.cgi?atlas=<atlas>&view={X,Y,Z}&x=<x>&y=<y>&z=<x>) • Coordinate lookuputility:(http://<server>/cgi-bin/atlas_lookup.cgi?atlas=<atlas>&x=<x>&y=<y>&z=<x>&direction={forward,inverse}) • Structure label lookuputility:http://<server>/cgi-bin/structure_lookup.cgi?atlas=<atlas>&x=<x>&y=<y>&z=<x> • WHS label lookup utility: http://<server>/cgi-bin/canon_lookup.cgi?x=<x>&y=<y>&z=<x>

44. What is INCF-DAI • A system of distributed atlas hubs and services Anatomy of a service Spatial transformations • INCF Central • The current status: introduction to the demo • How do you join the INCF atlasing network • Summary, current limitations, future work

45. Summary • We developed concepts of INCF-DAI, atlas hubs and services, INCF Central server, and communications between them • We built service-oriented architecture prototype for distributed digital atlases of mouse brain, that relies, where possible: • On standard service descriptions and exchange schemas • On standardized spaces, and a registry of spaces • On a collection of coordinate translation services, with WHS at the center • On standard terminology, with term cross-walks where needed • The translation services have performed well, and several tools were built for testing their performance • Data from several atlas hubs can be spatially integrated for the first time, via services and service chains • The integration has been demonstrated with a novel3D Atlas Integration application • The system is extensible

46. Limitations • There are many other ways to link data to tell a neuroscience story • E.g. get genes from ABA, and check where these genes are expressed in EMAGE ( but not the focus of the prototype) • Spatial integration methods: • From coordinate-based and anatomic feature-based, to integration by spatial placement rules • WHS meshes are “fitted” into ABA volume space, for now • Because otherwise need to adjust 2D images in the view, and perhaps have their warped copies • Spatial selection • Now by POI; in the future: by ROIs, transects, trajectories (e.g. along neuronal paths) • The client does not yet integrate data returned by spatial requests: • It is still Alpha; client integration is planned for the complete version (for 2D images, in particular) • XML schemas are not yet standardized for must queries

47. Future work • Additional atlasing hubs • Additional data types and associated standard services • XML schemas for the services • Further coordinate space standardization • Additional reference spaces and transformations (EMAP, in particular) • A registration and metadata harvesting system • A compelling neuroscience story • Working closely with the ontology and metadata TFs on APIs and central registries • Versioning of INCF-DAI

48. Global Spatial Data Infrastructure, as an example • GSDI = technology, policies, standards, human resources necessary to acquire, process, store, distribute, and improve utilization of geospatial data • Started in 1995/96; GSDI conferences • Global access to spatial data based on standards • Builds on National SDIs operated by member states, and on SDIs created by international bodies • Building a global picture without disturbing the NSDI process • Promote capacity building globally; GSDI implementation “cookbook” • Key features: common spatial framework based on international standards (ISO for spatial metadata etc.; Open Geospatial Consortium); registries of geographic feature names and gazetteers; spatial data catalogs; clearinghouses; terminology, etc. • Membership-based and funded; small grants mechanism • Also: infrastructure developed by international research projects: for ocean observations, meteorology, hydrology, geology, etc. (but seriously constrained by national funding)

49. Global Brain Data Infrastructure • Supporting national BDI efforts to be compatible with the global framework • Small grants, workshops, tutorials, code-sharing, demo projects, etc. • Infrastructure pillars • Common spatial and semantic framework, standards for data exchange, registries of shared atlas spaces and concepts • Community process for extending spatial and semantic frameworks, standards governance

50. DISCUSSION