In this module, we download labeled datasets from mitocheck_data.
Complete instructions for data download and preprocessing are located at: https://github.com/WayScience/mitocheck_data
In this repository, we download all labeled data from a version controlled mitocheck_data.
We specify the path to each set of mitocheck_data with labeled_data_paths in download_data.ipynb.
The labeled dataset includes CellProfiler (CP) and DeepProfiler (DP) features as well as metadata (location, perturbation, etc) for cells from the original MitoCheck project.
The breakdown of cell counts by manually-assigned phenotypic class for the ic (illumination corrected) dataset is as follows:
| Phenotypic Class | Cell Count |
|---|---|
| Interphase | 420 |
| Polylobed | 367 |
| Prometaphase | 345 |
| OutOfFocus | 304 |
| Apoptosis | 273 |
| Binuclear | 184 |
| MetaphaseAlignment | 175 |
| SmallIrregular | 164 |
| Hole | 114 |
| Elongated | 110 |
| ADCCM | 95 |
| Anaphase | 84 |
| Large | 79 |
| Grape | 74 |
| Metaphase | 74 |
| Folded | 54 |
Note: The get_features_data() function (defined in split_utils.py) used to load the labeled cell dataset excludes cells from the Folded phenotypic class when loading the labeled cells.
In our testing, the low representation of Folded cells leads to significantly low classification accuracy for this class (only tested with multi-class models).
Thus, we opt to exclude these cells from all training and testing.
Use the commands below to download labeled training dataset:
# Make sure you are located in 0.download_data
cd 0.download_data
# Activate phenotypic_profiling conda environment
conda activate phenotypic_profiling
# Download data
bash download_data.sh