[Statistics question]: UMAP 3

[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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To address Github Issue #37, I developed a Jupyter Notebook named umap_synthetic_data_analysis.ipynb to create a synthetic dataset representing cell measurements, including parameters like intensity, aspect ratio, perimeter, and elongation. The code utilizes libraries such as numpy, pandas, seaborn, umap-learn, and scikit-learn. I applied UMAP on these parameters, integrated the resulting UMAP parameters back into the dataframe, and visualized the outcome using seaborn. The resulting plot was saved as a PNG file named umap_synthetic_data.png.

• umap_synthetic_data.png: This file contains the UMAP visualization plot created from the synthetic dataset.
• umap_synthetic_data_analysis.ipynb: This notebook constructs a synthetic dataset, applies UMAP, and visualizes the results using seaborn.