Bio-image analysis, bio-statistics, programming and machine learning

1. Bio-image analysis, bio-statistics, programming and machine learning Robert Haase, Myers lab, MPI CBG Claudio Duran, Cannistraci lab, Biotec Mahmood Nazari, ABX-Cro / Schroeder lab, Biotec Martin Weigert, Myers lab, MPI CBG https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis/ May 14th 2019

2. Lecture overview Today • Image registration (skipped to next week) • Alternate image analysis tools • Python • Knime • Cell Profiler • Exercises

3. Workflow Todays workflow is about measuring area of objects. Load image Filtering Segmentation Labelling Measurement

4. Alternate Image Processing tools: Python Robert Haase, Myers lab, MPI CBG May 14th 2019

5. Reminder: Python • Loops • Conditional statements • Custom functions

6. Working with folders and files • Use for-loops and if-statements to go through a folder of images and process them

7. Working with images: scikit-image and matplotlib • Load images • Show images https://matplotlib.org/ https://scikit-image.org/

8. Image Filtering • Filters can be found in the filters module of skimage https://scikit-image.org/docs/stable/api/api.html

9. Image segmentation: thresholding • Threshold algorithms are also filters in skimage https://scikit-image.org/docs/stable/api/api.html

10. Watershed • Some filters are collected in skimage morphology module. Take care, the naming is a bit different to what you learned. https://scikit-image.org/docs/stable/api/api.html

11. Connected components analysis • CCA can be found in the measure module https://scikit-image.org/docs/stable/api/api.html

12. Feature extraction • For working with tables, pandas is a great good toolkit. It calls them DataFrames https://pandas.pydata.org/

13. Summary: Python • Learning python: https://swcarpentry.github.io/python-novice-inflammation/ • Plotting with python: https://matplotlib.org/tutorials/index.html • Image analysis in python: https://scikit-image.org/docs/stable/auto_examples/index.html • Data analysis python: http://pandas.pydata.org/pandas-docs/stable/getting_started/index.html

14. Alternate Image Processing tools: Knime Robert Haase, Myers lab, MPI CBG May 14th 2019

15. Knime • Partly open source / partly commercial (Spin-off University of Konstanz) • A toolbox for data analysts • Also supports image analysis (but it’s no priority)

16. Knime • Knime is build on the concept of tables and nodes Node was executed properly Node wasn’t executed yet There is an issue with this node. Configure it! This node cannot be executed because there is an issue with an other node.

17. Knime • Knime is build on the concept of tables and nodes • Tables can also contain images and segmented objects

18. Knime installation • Installation • https://www.knime.com/downloads • Help > Install new Software… • Select Community contributions: Image processing and analysis

19. Setting up Knime workflows • File > New > New Knime Workflow

20. Searching for Knime nodes • Search in the node repository

21. Building workflows • Connect nodes to build workflows / pipelines

22. Configuring nodes • Select input file(s)

23. Configuring nodes • Confgure threshold algorithms by right clicking

24. Inspect what a node does • Execute nodes and see what they do

25. Labeling features • This is comparable to the “Summary” checkbox in the Particle analyser of ImageJ/Fiji.

26. Run entire workflows • Execute the whole workflow by executing its last node.

27. Troubleshooting • If a workflow doesn’t do what it’s supposed to do, inspect the results of all nodes.

28. Troubleshooting • If a workflow doesn’t do what it’s supposed to do, inspect the results of all nodes.

29. Troubleshooting • If a workflow doesn’t do what it’s supposed to do, inspect the results of all nodes.

30. Troubleshooting • If a workflow doesn’t do what it’s supposed to do, inspect the results of all nodes.

31. Troubleshooting • If a workflow doesn’t do what it’s supposed to do, inspect the results of all nodes.

32. Troubleshooting • If a workflow doesn’t do what it’s supposed to do, inspect the results of all nodes. • Reconfigure or replace the first node which delivers weird results.

33. Troubleshooting • When searching for the right node, think about synonyms or alternatives to the functionality you are looking for.

34. Clean up intermediate results • If a result table contains unwanted columns, filter it.

35. Clean up intermediate results • If a result table contains unwanted columns, filter it.

36. Summary: Knime • https://www.knime.com/knime-introductory-course/chapter1 • https://www.knime.com/sites/default/files/inline-images/KNIME_quickstart.pdf • https://forum.knime.com/ • If you use it, please cite it: Alexander Fillbrunn, Christian Dietz, Julianus Pfeuffer, René Rahn, Gregory A. Landrum, Michael R. Berthold, KNIME for reproducible cross-domain analysis of life science data, Journal of Biotechnology, Volume 261, 2017, Pages 149-156

37. Alternate Image Processing tools: Cell Profiler Robert Haase, Myers lab, MPI CBG May 14th 2019

38. Cell Profiler • https://cellprofiler.org/ • An invention by Anne Carpenter • Maintained by her lab at the broad institute • https://personal.broadinstitute.org/anne/

39. Cell Profiler • Made for high-throughput screening • Typically, 2D multi-channel images are processed • Plays an major role in pharmacy • If you use it, cite it: Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biology 7:R100. PMID: 17076895 Image sources: https://cellprofiler.org/citations/ https://commons.wikimedia.org/wiki/File:BioMek_FX_P200_96_liquid_handling_robot.jpg

40. Cell Profiler • Getting started: Do what it says.

41. Building workflows • Open the menu with a right-click. • Add a new entry from the menu.

42. Configuring filters • Enter parameters of the filter. Especially input image!

43. Thresholding • Select input image and threshold method

44. Thresholding • Select input image and threshold method

45. Executing workflows • Click on Analyse Images. Observe the progress in the command line window.

46. Intermediate results • Turn off eye icons if you don’t want to see all intermediate results.

47. Save results

48. Decide for the right tools • Deciding for the right tool can spare you days of work! • Become very good in at least one tool! • Don’t ignore the other tools! Do you already roughly know how to process this image? Yes No Are you a good programmer? Yes No Is it the workflow complicated? No Yes Go for Cell Profiler Go for Knime Go for Python Go for Fiji • Disclaimer: This is my personal opinion. I don’t receive any money from any company for this slide ;-)

49. Exercises Robert Haase, Myers lab, MPI CBG https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis/ May 14th 2019

50. Exercise • Download example_images.zip from https://git.mpi-cbg.de/rhaase/lecture_applied_bioimage_analysis/tree/master/07_examples • It contains two subfolders with 10 images each. • Analyse the images. • Determine average blob area and the area ratio between conditions. • Use • Fiji, • Python, • Knime or • CellProfiler • Or: There is another hidden way to find out the area ratio.