[Statistics question]: UMAP

[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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I addressed issue #41 by creating a synthetic dataset representing cell measurements in a pandas dataframe with parameters such as intensity, aspect ratio, perimeter, and elongation. I then applied UMAP (Uniform Manifold Approximation and Projection) to reduce the dimensionality of these parameters and appended the resulting UMAP components to the dataframe. Utilizing seaborn, I visualized the UMAP components and saved the plot as a PNG image named umap_projection.png, which demonstrates the clustering of cells based on their features.

• Graphical output of UMAP visualization displaying cell feature clustering.
• analysis/umap_analysis.ipynb Created a Jupyter Notebook for performing UMAP analysis on synthetic cell measurements and visualizing the results.