[Statistics question]: UMAP 4

[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

Note: Your images and the text you enter here may be sent to OpenAI's online service where we use a large language model to answer your request.
Do not upload any data you cannot share openly. Also do not enter any private or secret information. By submitting this Github issue, you confirm that you understand these conditions.

git-bob try to do this

[github-actions]

^{This message was generated by git-bob (version: 0.11.0-dev, model: gpt-4o-2024-08-06, log), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

To address issue #38, I created a Jupyter Notebook named "umap_analysis.ipynb" in the haesleinhuepf/git-bob-playground repository. The notebook synthesizes a dataset of cell measurements (intensity, aspect ratio, perimeter, elongation) using pandas and applies UMAP for dimensionality reduction, incorporating numpy, scipy, pandas, seaborn, umap-learn, and scikit-learn. After computing the UMAP, the results were added back to the DataFrame, and a visualization was generated using seaborn. The resultant plot was saved as "umap_projection.png".

• umap_projection.png: Added a UMAP visualization plot saved as a PNG.
• umap_analysis.ipynb: Created a notebook for synthetic data generation, UMAP computation, and visualization.