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Image-based profiling course in Portuguese (2023/2024)

  • Contains materials, documentation, and slides used in the 2023/2024 Bioimage Analysis Course for NanoCell Interactions Lab.

  • This course is recorded in Portuguese, and the materials are available in English.

  • All materials were prepared and presented by fefossa.

Aims

Our aim is to provide a detailed material on how to plan, organize, and perform a bioimage analysis protocol using CellProfiler [1] for segmentation and feature extraction, pycytominer [2] for profiling, and Python for data visualization and interpretation.

Materials

For each day of the course, we will provide the written documentation, slides, and recording (in Portuguese).

ATTENTION: Videos are in Portuguese (PT). Slides and most materials in English (EN).

Most notebooks and scripts used for this course is available at scripts_notebooks_fossa repo. To learn how to use this repository properly for your analysis, watch this tutorial on how to create a submodule of a GitHub repository in PT.

Topics

  1. Folders and file naming for bioimage analysis

    1.1 Practicing: shell and bash basic commands

  2. Using Cellpose to train your own model

    2.1 Installing and using Cellpose human-on-a-loop Video in PT

    2.2 Using RunCellpose (Cellpose + CellProfiler integration). Video in EN

  3. Installing CellProfiler with RunCellpose

    Install CellProfiler+RunCellpose on Windows in PT

  4. Generate Metadata

    4.1 Presentation about metadata in bioimage analysis in PT here and Q&A

    4.2 Using the Load Data Generator and Layout to CSV apps demonstration and Q&A

    4.3 Using CellProfiler LoadData module here

  5. Assay development and Analysis with CellProfiler

    5.1 Assaydev pipeline

    5.2 Analysis pipeline

    • Practicing: CellProfiler

  6. Profiling using Pycytominer

    6.1 What's profiling?

    6.2 Profiling evaluation using mean Average Precision

    6.3 Practicing: Create environment & Jupyter notebooks

  7. Batch correction with PyCombat

    Batch effect and plate layout design in PT

  8. Visualizing and interpreting profiling data

    Morpheus part 1 in EN and Morpheus part 2 in EN

    Morpheus part 1 in PT and Morpheus part 2 in PT

    Visualize single-cells notebook https://youtu.be/55uu1YcmtH0

  9. Machine learning

    Videos explaining the ideas and mathematics behind machine learning are freely provided by StatQuest in EN

    A basic explanation of Machine Learning in PT is also available.

    Notebooks for feature importance using Random Forest, Linear Regression, and T-test

EXTRAS

References

[1] Carpenter, A.E., Jones, T.R., Lamprecht, M.R. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7, R100 (2006). https://doi.org/10.1186/gb-2006-7-10-r100

[2] Way, G., Chandrasekaran, S. N., Bornholdt, M., Fleming, S., Tsang, H., Adeboye, A., Cimini, B., Weisbart, E., Ryder, P., Stirling, D., Jamali, N., Carpenter, A., & Singh, S. Pycytominer: Python package for processing image-based profiling data (Version 0.3.0) [Computer software]. https://github.com/cytomining/pycytominer

[3] PyCombat