[Statistics question]: UMAP 6

[haesleinhuepf]

Question:

Create a synthetic dataset of measurements of cells from microscopy images (intensity, aspect ratio, perimeter, elongation) as pandas dataframe. Then, determine a UMAP from these parameters, write the UMAP parameters back to the dataframe and visualize the UMAP using seaborn. Save the plot as png before showing it.

Python Tools

• List of Python libraries we should use for answering this:
  • numpy
  • scipy
  • pandas
  • seaborn
  • umap-learn
  • scikit-learn

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git-bob try to do this

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In response to issue #40, a Jupyter Notebook named statistics_umap_analysis.ipynb was created to demonstrate UMAP analysis on a synthetic microscopy dataset. This notebook includes code to generate a dataset of cell measurements, such as intensity, aspect ratio, perimeter, and elongation, and then apply UMAP dimensionality reduction using the specified Python libraries. The resulting UMAP parameters are saved back into the dataset, and the visualization is performed using seaborn. Additionally, the generated UMAP plot is saved as umap_projection.png and displayed in the notebook.

• : Added a PNG image of the UMAP projection created by the notebook.
• statistics_umap_analysis.ipynb: Created a Jupyter notebook to perform UMAP analysis on synthetic microscopy data and visualize the results.