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Cell signal, and cell volume analysis (Sfp1)

Contents of this repository

This repository contains multiple directories that apply a similar sort of analysis on microscopy imaging data.
All three sub-folders contain a Jupyter notebook, as well as a script. The notebook was/is used for exploratory analysis, while the script is an efficient way to execute the complete analysis.

Sub-folder gfp-only-analysis

Provides insight into the Sfp1 difference in nuclear and cytoplasmic signal intensity. In this particular case, the only data used from the tif images is the GFP channel, which tracks Sfp1. Local/adaptive thresholding is performed to find the nucleus its centroid. This is used to calculate the average nuclear signal. The cytoplasmic signal is taken as an average of the rest of the cell (so omitting the nucleus).

Sub-folder rfp-scarlet-analysis

Provides insight into the nuclear and cytoplasmic signal intensity. In this particular case, the only data used from the tif images is the RFP channel. Local/adaptive thresholding is performed to find the nucleus its centroid. This is used to calculate the average nuclear signal. The cytoplasmic signal is taken as an average of the rest of the cell (so omitting the nucleus). Since these images diverge from the other ones, the settings and approach for the thresholding are/is different.

Sub-folder volume-analysis

In this case, instead of calculating the nuclear to cytoplasmic signal strengths, the nuclear and whole-cell volumes are calculated from through whole-cell masking and nuclear masking. This is also done using local/adaptive thresholding. Outliers are removed using the IQR-approach and the data is stored in a dataframe.

Setup

Environment

  1. Make sure Python is installed (preferably version 3.9)
  2. Create a virtual environment like this: python3 -m venv preferred-venv-location.
  3. Activate the environment you just created by running source path-to-your-venv/bin/activate or path-to-your-venv/Scripts/activate on Windows.
  4. Install all necessary dependencies by running: pip3 install -r requirements.txt.

General instructions; script execution

  1. Make sure to check all hard-coded filepaths at the top of the main file(s) are configured correctly.
  2. Make sure the data is in the correct place.
  3. Run the script using python.
  4. Debug any minor error messages (such as incorrect paths etc.).
  5. Rerun.