This project implements a machine learning model for selecting optimal time points in single-cell analysis. The goal is to maximize the information learned from single-cell experiments by predicting the most informative time points for data collection. The model uses features derived from gene expression data to inform decision-making.
- Predicts optimal time points for single-cell analysis.
- Supports pseudobulk and bulk RNA-seq data preprocessing.
- Uses a hybrid neural network for time series prediction.
To run the example code, please follow these steps:
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Download the Required Files:
- Obtain the
Example_Code.ipynbfile and theexample_datafolder.
- Obtain the
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Set Up the Computational Environment:
- Ensure that Python and all required dependencies are installed. The necessary packages can be installed directly from within the notebook if not already available.
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Execute the Code:
- Place the
example_datafolder in the same directory as the notebook. - Open
Example_Code.ipynbusing Jupyter Notebook, Jupyter Lab, or Google Colab. - Run the cells in the notebook sequentially to train the model and generate the outputs.
- Place the
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Outputs:
- The notebook will produce the trained model as well as average MAE and R2 values.
These steps ensure reproducibility of the findings described in this work.
This project is licensed under the GNU General Public License. See the LICENSE file for more details.