Figure 1. Dataset and analysis approach. (A) Single-cell counts per labeled phenotype stratified by phenotype category. The labeled MitoCheck dataset included a total of 2,916 single nuclei. The original dataset contained labels for 16 classes, but we have removed “folded” because of low counts. Counts are not evenly distributed between the classes. (B) Our analysis pipeline incorporated image analysis, image-based profiling, and machine learning.
Perform the following steps to set up the phenotypic_profiling
environment necessary for processing data in this repository.
# Run this command to create the conda environment for phenotypic profiling
conda env create -f phenotypic_profiling_env.yml
# Run this command to activate the conda environment for phenotypic profiling
conda activate phenotypic_profiling
The repository structure is as follows:
Order | Module | Description |
---|---|---|
0.download_data | Download training data | Download labeled single-cell dataset from mitocheck_data |
1.split_data | Create data subsets | Create training and testing data subsets |
2.train_model | Train model | Train ML models on training data subset and shuffled baseline training dataset |
3.evaluate_model | Evaluate model | Evaluate ML models on all data subsets |
4.interpret_model | Interpret model | Interpret ML models |
5.validate_model | Validate model | Validate ML models |
6.single_cell_images | Single Cell Images | View single cell images and model interpretation |
7.figures | Figures | Create paper-worthy figures |
Specific data download/preprocessing instructions are available at: https://github.com/WayScience/mitocheck_data. This repository downloads labeled single-cell data from a specific version of the mitocheck_data repository. For more information see 0.download_data/.
We use Scikit-learn (sklearn) for data manipulation, model training, and model evaluation.
Pedregosa et al., JMLR 12, pp. 2825-2830, 2011 describe scikit-learn
as a machine learning library for Python.
Its ease of implementation in a pipeline makes it ideal for our use case.
We consistently use the following parameters with many sklearn
functions:
n_jobs=-1
: Use all CPU cores in parallel when completing a task.random_state=0
: Use seed 0 when shuffling data or generating random numbers. This allows "random" sklearn operations to have consist results. We also usenp.random.seed(0)
to make "random" numpy operations have consistent results.
We use seaborn for data visualization.
Waskom, M.L., 2021 describe seaborn
as a library for making statisical graphics in python.
All parts of the machine learning pipeline are completed with the following feature types:
CP
: Use only CellProfiler features fromMitoCheck
labeled cellsDP
: Use only DeepProfiler features fromMitoCheck
labeled cellsCP_and_DP
: Use CellProfiler and DeepProfiler features fromMitoCheck
labeled cells
See MitoCheck_data for more information on feature types and how they are extracted from MitoCheck
labeled cells.
We create two versions of the same machine learning model:
final
: Model fit using real feature valuesshuffled_baseline
: Model fit using shuffled training data (to create a suitable baseline comparison for the final model during evaluation)
Throughout this repository, we store intermediate .tsv
data in tidy long format, a standardized data structure (see Tidy Data by Hadley Wickham for more details).
This data structure makes later analysis easier.
Some intermediate data used in this repository are too large to be stored on GitHub. These intermediate data are available on the Way Lab Zenodo page.
Specific code and steps used are available within each module folder.
The Way Lab always strives for readable, reproducible computational biology analyses and workflows. If you struggle to understand or reproduce anything in this repository please file an issue!