This repository contains Jupyter notebooks used for the preprocessing of single-cell RNA-seq datasets. The preprocessing steps include data cleaning, normalization, comparative analysis, and data preparation to ensure the quality and usability of the data for further analysis.
The aim of this project is to systematically prepare single cell RNA-seq data by applying various preprocessing steps, ensuring that the data is primed for high-quality downstream analyses.
The original datasets used in this project can be accessed here.
assets/: Contains graphical assets and additional resources used in the notebooks.scripts/: Contains Jupyter notebooks for data preprocessing:preprocessing_1.ipynb: Handles initial data loading, cleaning, and basic statistical analysis.Preprocessing_2.ipynb: Further cleans the data, subsets it for analysis, and exports it for use in downstream applications.rank_value.ipynb: Performs comparative analysis, data manipulation, and final data preparation.
README.md: This file, describing the project and repository structure.
- preprocessing_1.ipynb: Starts with data loading, followed by initial cleaning and basic statistical analysis.
- Preprocessing_2.ipynb: Focuses on further data cleaning, subsetting, and exporting the data for downstream uses.
- rank_value.ipynb: Handles complex data manipulations, comparative analyses, and prepares final datasets for analysis.
- Clone this repository to your local machine.
- Ensure you have Jupyter Notebook or JupyterLab installed.
- Navigate to the
scripts/directory and open the notebooks using Jupyter. - Run the notebooks in sequence to replicate the preprocessing steps.
- Python 3
- Pandas
- Numpy
- Matplotlib
- h5py
- Other Python libraries as required for data handling and visualization.
For any further inquiries or contributions, feel free to contact the repository maintainer.
This project is licensed under the MIT License.