METASPACE Training

Slides given at ourcon 2017

Transcript

1. METASPACE training guide 2017 OurCon’V imzML workshop Theodore Alexandrov (EMBL, UCSD) Andy Palmer (EMBL) Vitaly Kovalev (EMBL) Artem Tarasov (EMBL) Adam Pruska, Andreas Roempp, Annabelle Fülöp, Anne Mette Handler, Benedikt Geier, Berin Boughton, Bernhard Spengler, Buck Achim, Carina Ramallo-Guevara, Charles Pineau, Chris Anderton, Christian Janfelt, Christina Burr, Claire Carter, Corinna Henkel, Cristina Gonzalez Lopez, Cristine Quiason, David Muddiman, Denis Sammour, Dhaka Bhandari, Dinaiz Thinagaran, Dirk Hoelscher, Don Nguyen, Dušan Velickovic, Eike Ulrich Brockmann, Emilia Sogin , Emrys Jones, Eric Weaver, Erin Gemperline, Guanshi Zhang, Gus Grey, Heath Patterson, Hidenobu Miyazawa, József Pánczél, James Langridge, James McKenzie, Jan-Hinrich Rabe, Janfelt Christian, Jens Soltwisch, Jialing Zhang, Josephine Bunch, Julian Griffin, Julien Delecolle, Kaija Schaepe, Klaus Dreisewerd, Konstantin Nagornov, Ksenija Radic, Kumar Sharma, Kyana Garza, Lavigne Regis, Lennart Huizing, Liebeke Manuel, Lingjun Li, Livia Eberlin, Logan Mackay, Luca Rappez, Marina Reuter, Mario Kompauer, Mark Bokhart, Marta Sans, Marty Paine, Mathieu Gaudin, Maureen Kane, Max Müller, Michael Becker, Michael Linscheid, Mikhail Belov, Na Sun, Neha Garg, Nicolas Desbenoit, Nicole Strittmatter, Oliver Lechtenfeld, Pegah Khamehgir-Silz,, Renata Soares, Richard Caprioli, Richard Goodwin, Rima Ait-Belkacem, Ron Heeren, Samantha Walker, Sandra Schulz, Sarah Aboulmagd, Sergio Triana, Shane Ellis, Sheerin Latham, Sophie Jacobsen, Spencer Thomas, Stefanie Gerbig, Steve Castellino, Veronika Saharuka, Vitaly Kovalev, Yury Tsybin, Zoe Hall, Zoltan Takats @METASPACE2020 Contributors
2. Welcome everyone! Artem Tarasov “hacker” Andy Palmer “scientist” Vitaly Kovalev “developer” Theodore Alexandrov “leader”
3. Part 1: Introduction Learning outcomes METASPACE project Bioinformatics for metabolite annotation Engine Knowledgebase Part 2: Tutorial Learning outcomes Data requirements Data submission Annotation browsing Interpretation Part 3: Export to imzML FTICR (Bruker) Orbitrap (Thermo) Other vendors Training Overview
4. Introduction
5. What we hope you will learn today • Ins and outs of metabolite annotation in HR imaging MS • Bioinformatics we developed ◦ Metabolite Signal Match (MSM) score ◦ False Discovery Rate estimation ◦ FDR-controlled annotation • Our METASPACE platform ◦ How to prepare data for submission to our service ◦ How to submit your data ◦ How to view molecular annotations in our webapp
6. Project overview: slides on slideshare Bioinformatics: slides on slideshare
7. Overview of the annotation engine
8. Outline • Input (data and metadata) • Online Software • Data Submission • Annotation Browsing • Use Cases a. mouse brain, MALDI-FTICR (U Rennes 1) b. human colorectal tumor, DESI-Orbitrap (ICL)
9. The METASPACE platform http://annotate.metaspace2020.eu imzML, metadata upload annotations database task scheduler explore annotations 10 minutes engine Amazon Cloud
10. Upload: Data centroided imzML http://ms-imaging.org/
11. Upload: Metadata sample information acquisition details
12. Upload: Interface annotate.metaspace2020.eu/#/upload metadata collection data upload
13. Example 1 Mouse brain (MALDI-FTICR) Data provided by Regis Lavigne, Charles Pineau, University of Rennes 1 Select an annotation See the molecular distribution
14. Example 2 Human colorectal tumor (DESI-Orbitrap) Data provided by James McKenzie, Zoltan Takats, Imperial College London Filter different datasets See data details View metadata
15. Tutorial on how to use METASPACE engine and molecular knowledgebase
16. Learning Outcomes 1. Preparing data for submission 2. Submitting data 3. Browsing results
17. Data Requirements Imaging mass spectrometry data - Any ionisation source - Any spatial resolution - Any tissue - One section per dataset
18. Data Requirements High resolving power R FWHM (@400) > 90K Well-calibrated ideally < 3 ppm
19. Data Requirements Data Format - imzML Centroided - vendor preferred - http://metaspace2020.eu/imzml http://imzml.org/wp/introduction/
20. Customised Processing Processing is tailored to your data! - Technical metadata - Resolving power - isotope prediction - Polarity - adducts R 200 =70K R 200 =280K [C 41 H 78 NO 7 P+K]+
21. Data Requirements Your responsibility: - Data is processed ‘as is’ - Check metadata is correct - Report resolving power accurately (check within data-set) - Low numbers of annotations often correspond to poor quality mass spectra - Calibration inaccuracy - Lock-mass errors
22. Data Submission
23. 1. Follow conversion instructions for your instrument 2. Select the centroided files, .imzML and .ibd 3. The dataset will be copied to the cloud storage (accessible only to our team) Data upload
24. • Start typing to see suggestions • Please fill truthfully ◦ Don’t want to disclose? Just put ‘N/A’ • Click (top right) ◦ Enabled once the files finished uploading Metadata form
25. Browsing Results
26. Annotation knowledgebase web app http://annotate.metaspace2020.eu Results and metadata are public Datasets are not
27. Dataset list processing is in progress queued finished the list can be filtered and exported to CSV Clicking on fields limits the list to datasets with the same value
28. Annotation table Currently selected molecule (click to select) MSM score principal peak m/z
29. Sorting/filtering annotations Click on column headers to sort Add as many filters as you need Quickly add a filter by hovering over a cell and clicking the icon
30. Quick search • search across all fields • works in both ‘Datasets’ and ‘Annotations’ tabs • supports* prefix match, OR operator, negation, e.g. Rennes -(rat | mouse | human) (*) ElasticSearch Simple Query
31. Quick search • search across all fields • works in both ‘Datasets’ and ‘Annotations’ tabs • supports* prefix match, OR operator, negation, e.g. Rennes -(rat | mouse | human) (*) ElasticSearch Simple Query
32. • Automatically generated figures summarising filtered datasets Dynamic Summaries
33. Molecule search Search by name (partial name search) Or by molecular formula (exact match only) Click to edit
34. Details for highlighted annotation molecule distribution (principal isotope) Putative metabolite entries from the database
35. Visual insight into MSM score assignment Exact m/z of each ion image Click and drag to zoom Ion images for each isotope peak Isotopic patterns Blue: theoretical abundance (at instrument resolving power) Red: measured image intensity
36. Step-by-step search Choose molecular formula database
37. Step-by-step search Add dataset filter, then choose dataset(s) (up to 10)
38. Step-by-step search Type molecule name or molecular formula The annotations table will dynamically update
39. Step-by-step search Export to CSV will save the current annotations table. Changing the filters will change which annotations are exported Always export annotations for comparison together (so they are at the same FDR)
40. Results Browsing Summary 1. Choose database 2. Choose data-set 3. Add molecule filter and type ‘PC’ a. molecular class filter 4. Type ‘PC(16:0/18:0) a. single metabolite filter 5. Select row of table a. single ion filter 6. Simple comparison of spatial distributions between adducts 7. Export of annotations to csv Also possible • Filter by m/z • Formula search • Comparison across datasets
41. Interpretation
42. FDR Controlled Annotation False Discovery Rate - the fraction of incorrect annotations Control - request a set of annotations at a fixed estimated FDR Setting the level: - Adjust the number of molecules for follow-up analysis - When only limited numbers of molecules can be reviewed, adjust the FDR so that fewer/great numbers of molecules are annotated - Compare annotations between datasets - A principled way of selecting molecules to compare between datasets True annotation False discovery MSM score FDR = 0.1 FDR = 0.2 FDR = n True n False + n True
43. Choice of metabolite database synthesized/recorded 88M CAS registry biologically occurring/active 50M PubChem compounds single biological system 40K HMDB sample specific 1K LC-MS
44. Choice of metabolite database Impacts search and False-Discovery-Rate estimation • Use one that’s relevant • Larger database ◦ more false-hits --> fewer annotations at a fixed FDR • Different databases give different annotations ◦ even for molecules in both databases due to FDR control ◦ for data-set comparison, use the same database
45. Annotating at level of molecular formula • Possibility of multiple metabolites per sum formula ◦ webapp shows all hits from the database search (learn the ambiguity!) ◦ other databases can be searched (e.g. PubChem) ◦ use enrichment analysis to get biological leads • Use an orthogonal technique for reporting individual metabolites ◦ not directly integrated (yet) ◦ use web-app results help to target MS/MS studies (e.g. purchase of standards)
46. • The METASPACE platform putatively annotates* molecular formula along with several candidate metabolites • A set of annotations should be reported along with the FDR threshold selected. ◦ e.g. “Molecular annotation was performed using the METASPACE annotation engine (Palmer et al, Nature Methods 2017). 150 molecules were annotated against the LipidMAPS database at 10% FDR. Results are publically available at annotate.metaspace2020.eu” • The export function of the website delivered a spreadsheet that can be included as supporting for any publication. Reporting Results Metabolomics Standards Initiative identification levels Sumner et al, 2007, Metabolomics
47. • Preparing data for submission ◦ imzML export ◦ metadata • Submitting data ◦ web-app, upload • Browsing knowledgebase ◦ web-app, annotations Learning Summary • METASPACE team: ◦ web: metaspace2020.eu ◦ email: contact@metaspace2020.eu ◦ twitter: @metaspace2020 ◦ source code: https://github.com/METASPACE2020/ • FTICR data conversion ◦ SCiLS: support@scils.de • Orbitrap data conversion ◦ Thermo Fisher Scientific: kerstin.strupat@thermofisher.com How to get help?
48. imzML Export
49. Export into imzML: FT-ICR data Using SCiLS Lab’s METASPACE export
50. Export to METASPACE • Export your centroided high-resolution spectra in the imzML format • Available for “FT-ICR type” SCiLS Lab files from SCiLS Lab 2016b • Performance in version 2018b significantly increased (4x speedup, batch export) • Best results in METASPACE if peak list is required for centroiding • Two different Bruker data formats ◦ SQLite peak list data: Peak list provided during import ◦ FT-ICR profile data: Generate a peak list after import
51. Create imzML file for METASPACE • In the objects tab, click the export symbol of the region to be exported and select “Export to METASPACE” • The Export Spectra dialog opens • Set your normalization of choice • Select your peak list of choice for example “Imported Peaks” in case of SQLite • Provide your scan polarity • Click OK to save imzML file
52. SQLite peak list data • Data must have been acquired with on-the-fly centroid detection i.e. there is a file called ‘peaks.sqlite’ within the .d folder of the data-set • In SCiLS Lab a peak list “Imported peaks” is available, selecting most frequent peaks By default all peaks appearing more frequently than 1% of spectra
53. FT-ICR profile data • Older Solarix Files do not directly contain a peak list to perform centroiding • Create peak list with Data Analysis SCiLS Lab Help Section 7.4 • Use METASPACE tool for peak finding https://spatialmetabolomics.github.io/centroidize/ • Use other external tools (mMass, …) • Import the external peak list into SCiLS Lab File > Import > m/z intervals from CSV or Clipboard
54. Use METASPACE tool for peak finding • Select the overview spectrum CSV exported from SCiLS • Upload CSV file to METASPACE tool • Copy values to clipboard • Use File > Import > m/z intervals from CSV
55. Upload imzML files to METASPACE • Go to http://annotate.metaspace2020.eu/#/upload
56. Export into imzML: Orbitrap data (.raw) Instructions: metaspace2020.eu/imzML Software tools: imageQuest / raw-converter - Recommended for: MALDI images (Thermo MALDI- / TransMIT AP-S-MALDI-) imzmlConverter - Recommended for: DESI/flowProbe with separate files per row Recommended for bioinformaticians: pyimzML (Python parser)
57. .raw -> imzML • Commercial ◦ Thermo Scientific imageQuest
58. Raw-imzml converter .raw -> imzML • Free • http://ms-imaging.org/wp/raw-to-imzm l-converter/
59. .raw -> mzML -> imzML • MSConvert ◦ free (link) • imzMLConverter ◦ free ◦ requires registration ◦ http://www.cs.bham.ac.uk/~ibs/imzMLConverter Export into imzML: Generic
60. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement № 634402. Acknowledgments METASPACE R&D team at EMBL Theodore Alexandrov Vitaly Kovalev Artem Tarasov Andrew Palmer Dominik Fay SCiLS Dennis Trede Jan Hendrik Kobarg METASPACE data contributors Achim Buck, Adam Pruska, Andreas Roempp, Andrew Palmer, Annabelle Fülöp, Anne Mette Handler, Benedikt Geier, Berin Boughton, Bernhard Spengler, Carina Ramallo-Guevara, Charles Pineau, Chris Anderton, Christian Janfelt, Christina Burr, Claire Carter, Corinna Henkel, Cristina Gonzalez Lopez, Cristine Quiason, David Muddiman, Denis Sammour, Dhaka Bhandari, Dinaiz Thinagaran, Dirk Hoelscher, Don Nguyen, Dušan Velickovic, Eike Ulrich Brockmann, Emilia Sogin , Emrys Jones, Eric Weaver, Erin Gemperline, Guanshi Zhang, Gus Grey, Heath Patterson, Hidenobu Miyazawa, József Pánczél, James Langridge, James McKenzie, Jan-Hinrich Rabe, Janfelt Christian, Jens Soltwisch, Jialing Zhang, Josephine Bunch, Julian Griffin, Julien Delecolle, Kaija Schaepe, Klaus Dreisewerd, Konstantin Nagornov, Ksenija Radic, Kumar Sharma, Kyana Garza, Lavigne Regis, Lennart Huizing, Lingjun Li, Livia Eberlin, Logan Mackay, Luca Rappez, Manuel Liebeke, Marina Reuter, Mario Kompauer, Mark Bokhart, Marta Sans, Marty Paine, Mathieu Gaudin, Maureen Kane, Max Müller, Michael Becker, Michael Linscheid, Mikhail Belov, Na Sun, Neha Garg, Nicolas Desbenoit, Nicole Strittmatter, Oliver Lechtenfeld, Pegah Khamehgir-Silz, Rappez Luca, Regis Lavigne, Renata Soares, Richard Caprioli, Richard Goodwin, Rima Ait-Belkacem, Ron Heeren, Samantha Walker, Sandra Schulz, Sarah Aboulmagd, Sergio Triana, Shane Ellis, Sheerin Latham, Sophie Jacobsen, Spencer Thomas, Stefanie Gerbig, Steve Castellino, Theodore Alexandrov, Veronika Saharuka, Vitaly Kovalev, Yury Tsybin, Zoe Hall, Zoltan Takats