This repository contains analysis scripts for the paper "Single-cell morphological profiling reveals insights into cell death" ([Add Link to Paper]).
The repository is structured to facilitate reproducibility and includes scripts for supervised and unsupervised analysis, calculation of key metrics, and generation of publication figures.
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supervised/
Contains scripts for supervised classification tasks using single-cell and aggregated profiles.Supervised_analysis.ipynb: Jupyter notebook for evaluation of supervised models.autogluon_classifier_celldeath.py: Script for model training using AutoGluon.
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unsupervised/
Contains scripts for clustering and dimensionality reduction tasks.Unsupervised_analysis.ipynb: Jupyter notebook for unsupervised analysis, including UMAP and PCA.
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metrics/
Scripts for calculating perturbation metrics like grit score and e-distance.grit_script.py: Script to calculate grit scores.run_etest.py: Script to calculate e-distances and perform permutation tests.etest_grit_analysis.ipynb: Notebook for analyzing and visualizing metric results.
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visualization/
Scripts for generating figures and visualizations.visualize_attention_Celldeath.py: Script to generate attention maps and related plots.
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config/
Configuration files for reproducibility.config_finetune_unmasked.yaml: Configuration for training DINO models. Note: We do not provide all imaging data here.
For DINO training, we adapted the codebase from DINO4Cells. Please refer to their repository for detailed instructions. The checkpoints to our model can found in [add FigShare].
To obtain representative cell images shown in Fig. 3, Suppl. Fig. S5–S7, S9, S10, and S14, we used the CellViewer tool. The code for CellViewer is available at [Add CellViewer GitHub Link].
Extracted features can be found in [add FigShare]. Provided are normalised profiles. Aggregation and analysis steps can be found in the scripts. Grit scores and e-distance result can be found on FigShare.
If you use this code or data, please cite: Single-cell morphological profiling reveals insights into cell death ([Add DOI/Link to Paper])