Publishing expression data from the SMD

1. Publishing expression data from the SMD Catherine Ball Tuesday, May 30, 2006 array@genome.stanford.edu http://smd.stanford.edu/

2. User Help: Tutorials and Workshops • SMD Help & FAQ http://genome-www.stanford.edu/microarray/helpindex.html • SMD Tutorials – regularly scheduled (we hope) • Welcome to SMD • Data analysis, Normalization and Clustering • Publishing expression data • Power users and the data repository • Interested? Email array@genome.stanford.edu

3. What we won’t discuss: User Registration Loader Accounts Submitting Data Finding Your Data Displaying Your Data Data Retrieval and Analysis Submitting a Printlist Data Normalization Data Quality Assessment Data Analysis (clustering) External User Tools (XCluster, TreeView, etc.) What we will discuss: Publishing Publisher’s requirements Experimenter’s responsibilities Hybridization Annotation Categories, Subcategories Protocols Procedures and parameters Clinical Data Experiment Set Annotation Organizing Data Experiment Design Categories Experimental Factors Factor Values Making your data available SMD Web Supplements Public Data repositories Publishing expression data : a tutorial • Please fill out the sign-up sheet and survey form • Questions? email us at: array@genome.stanford.edu

4. Publishing expression data • Background • Publishing requirements and responsibilities • Pre-publication responsibilities • Hybridization Annotation • Experiment Set Annotation • Post-publication responsibilities • Making your data available

5. Background : Interpretation and Analysis • Extremely difficult to either interpret or analyze expression results without being aware of all the variables • Typically, these annotations, if they exist at all, are not attached to the data Biological characteristics, experimental design, protocol parameters, filtering parameters, etc. Perhaps in a lab notebook, eventual publication (if ever published), or in the worst scenario, only in the experimenter’s head

6. Background : MGED • Microarray Gene Expression Database Society • http://www.mged.org/ • Initially established November, 1999, Cambridge, UK. • Realized there were serious problems in communicating the results of genomic-scale expression results • Keen interest in a data standards, specifications, and transmission.

7. Background : Emerging standards • MIAME : Minimal Information About a Microarray Experiment • the requisite information needed to both verify your analysis and allow others to perform distinct analyses • Nature Genetics (2001) 29, 365-371 • MAGE-ML: MicroArray Gene Expression Markup Language • data format standard required for transmission and integration into other expression repositories • Genome Biology(2002), 3(9):research0046.1–0046.9

8. Background : MIAME checklist • MGED Guide to authors, editors and reviewers of microarray gene expression papers • In the interests of full disclosure and open research, a checklist of requirements was proposed, aimed at allowing manuscript readers “to understand the experiment, to identify the sequences being assayed, and to interpret the resulting data. ” http://www.mged.org/Workgroups/MIAME/miame_checklist.html

9. Publication Requirement? … also being adopted by Cell and The Lancet - others to follow…

10. Publishing responsibilities • Pre-publication • Provide the data and full annotation to the reviewers and editors. • This may evolve to sending data to a repository prior to publication (reviewer anonymity) • Post-publication • For the foreseeable future, provide a static snapshot of the raw result data and filtered/clustered data along with the gene annotation at the time of publication

11. Implications of MIAME for Stanford Microarray Researchers • As of December 1, 2002, anyone submitting a paper to a Nature journal must submit his/her data to a public microarray data repository (such as ArrayExpress). • SMD users should start assembling and entering experimental data in preparation for more widespread acceptance of these standards.

12. MIAME checklist • Six parts • Biological Samples • Hybridizations • Data Normalization and Transformation • Experimental Design and Factors • Array Design • Measurements

13. SMD Stores Procedures • Biological Sample (Channels 1 and 2) • Growth Conditions (Channels 1 and 2) • Treatment (Channels 1 and 2) • Extract Preparation (Channels 1 and 2) • Chromatin IP • Amplification (Channels 1 and 2) • Labeling (Channels 1 and 2) • Hybridization Conditions • Scanning Procedure (Channels 1 and 2) • Feature Extraction • User-defined Procedures

14. Recording Procedural Details : Two Mechanisms • Full text Protocols • Great for providing the full documentation of the protocol to a fellow researcher, but… • Poor for indicating which experimental parameter is the key to the experimental design • Procedural parameters • Great for supervised analysis and singling out the important details of the experiment, but… • Poor for synthesizing the entire procedure together in a legible manner

15. Where are the tools? Enter New Data View Existing Data

16. List Existing Protocols • Display within SMD, or View external resource • Edit your protocol from the list

17. Edit Existing Protocol

18. Entering a New Protocol • Choose the procedure • Supply the formatted plain text, or a simple description if providing the URL

19. Flowchart to Add Annotations

20. Use “Edit” to add procedural details to your experiments Edit your hybridizations

21. Experiment Types • CGH • Comparison of genomic copy number between samples (Comparative Genome Hybridization). • Chromatin IP • Investigation of DNA-protein interactions in which protein-bound DNA is immunoprecipitated. • Expression (Type I) • Investigation of gene expression where the control sample is tailored to the particular experiment (not a common reference). • Expression (Type II) • Investigation of gene expression where the control RNA is made from a common reference. • GMS • Genome Mismatch Scanning. Investigation of the parental origin of genomic DNA.

22. Use “Edit” to add procedural details to your experiments Edit your hybridizations

23. Associating a protocol with a hybridization • Associate a previously entered protocol • Enter a new one, if need be

24. Adding Procedural Parameter Values for a Hybridization • Same interface is used to add experimental parameter values • Parameter values are linked directly to the hybridization • Procedural parameters are modeled as experimental factors

25. Use “Edit” to add clinical annotation to your experiments Edit your hybridizations

26. Associating Patient Information • Patient parameters we store • Age at diagnosis • Sex • Ethnicity • Family History • Status • Time from Operation to Death • Date of last follow-up • Patient lost prior to follow-up?

27. Associating Clinical Sample Information • Sample parameters we store • Tracking Information • Unique Sample ID • Linking Database • Sample Information • Sample Source • Time Post-mortem (hrs) of sample removal • Sample State, Size • Granularity • Organ of origin • Attending Surgeon • Pre-Operative Information • Prior Treatment • Clinical Stage • Post-Operative Information • Tumor Grade, Size, Type • Margins • Time from Diagnosis To Operation • Angioinvasion • Total Lymph Nodes • Positive Lymph Nodes • Pathological Stages FollowUp Information • Recurrence • Post Operative Therapy Time from Operation to Recurrence

28. Batch Entry Batch Association of Annotations

29. MIAME checklist • Six parts • Biological Samples • Hybridizations • Data Normalization and Transformation • Experimental Design and Factors • Array Design • Measurements

30. MIAME checklist : Data Normalization and Transformation

31. MIAME checklist • Six parts • Biological Samples • Hybridizations • Data Normalization and Transformation • Experimental Design and Factors • Array Design • Measurements

32. MIAME : Experimental Design • Experimental Design and Factors • type of experiment (set of hybridizations) • The number of hybridizations performed • experimental factors • hybridization design • the type of reference used for the hybridization • quality control steps taken

33. Arraylists Personal list of experiments Contains no annotation More difficult to share with others Flat file that exists in your loader account Accessed through Advanced Search Experiment Sets Annotated list of experiments Exists in the database therefore dynamic (edit, delete, or annotate through a web interface) Easily shared with other users/ collaborators Extensible Accessed through Basic Search Required for publication within SMD Organizing Data: Arraylists vs Experiment Sets

34. Easily convert your arraylist into an experiment set

35. Selecting the data for inclusion within the experiment set • Select experiments using either the basic or advanced search as a starting point Experiment Set Creation

36. Experiment Set Organization

37. Base Annotation for the Experiment Set • Set description • For publications, this would likely be either the abstract or a figure legend

38. Finding Your Sets in SMD: Basic Search Experiment Sets allow you to search data on pre-defined experiment groups.

39. Edit your Experiment Set

41. relates

42. Experiment Factors : Step 1 Procedures Parameters Measurements?

43. These values can be automatically acquired/suggested from your procedural parameters values, but only if you have annotated your experiments. Experiment Factors : Step 2 Note: full text protocols cannot be utilized for this purpose, but fulfill their own purpose.

44. Benefits of Experiment Annotation • Meet MIAME requirements • Meet publishing requirements (see above) • Serve as a basis for new analysis tools

45. Post-publication responsibilities • Making your data easily available and accessible for the foreseeable future • SMD • web supplement • public repositories

46. Post-publication : SMD • Send us the name of your MIAME-annotated experiment set • We’ll make the arrays world-viewable for you, and publicize your paper • Gene annotations and normalizations may change, so you must also provide a distinct, static view (web supplement) Contact array@genome.stanford.edu

47. Post-publication : web supplement • We encourage you to make a web supplement, which represents a snapshot of the data, as published • Options: • You can make the web-site and host it on your own. • You can make the web-site on your own and you can ask us to host it. • You can ask us to construct one for you. Usually, given the amount of work that this entails (ask us ahead of time), the curator creating the website will expect collaborative consideration. Contact array@genome.stanford.edu

48. Post-publication : repositories • Submit your data to a public repository • ArrayExpress at the EBI • http://www.ebi.ac.uk/arrayexpress/ • Gene Expression Omnibus (GEO) and NCBI • http://www.ncbi.nlm.nih.gov/geo/ • We produce valid MAGE-ML for experiment sets and array designs and can communicate these to the repositories for you Contact array@genome.stanford.edu

49. If you require assistance with either the creation of a web supplement or submission of your dataset to a repository, contact us at array@genome.stanford.edu

50. MIAME Resources • MIAME working group • http://www.mged.org/miame • MIAME checklist for authors, editors • http://www.mged.org/miame/miame_checklist.html