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Clonalyzer: Kinetics Data Analysis for CHO Cell Clones

Overview

Clonalyzer is a Python-based tool designed to clean, process, and analyze kinetic data from CHO (Chinese Hamster Ovary) cell clones. It supports experiments involving multiple clones and replicates, enabling users to evaluate performance across various experimental conditions systematically.

Current Features

  1. Data Cleaning:

    • Removes inconsistencies and ensures the dataset is ready for analysis.
    • Handles missing values using user-defined strategies (e.g., mean, median, or zero filling).

  2. Data Preview:

    • Provides statistical summaries (describe) and basic information (info) of the dataset to facilitate exploration.

  3. Visualization:

    • Automatically generates key plots, such as:
      • Time vs. individual parameters (e.g., Viable Cells, Glucose, Glutamine).
      • Combined plots for parameters with similar units (e.g., Glucose and Glutamine).
    • Saves all plots in a dedicated figures directory.

    Examples of Generated Visualizations:

    • Glucose and Glutamine vs Time: Glucose and Glutamine vs Time
    • Kinetic Parameter Comparison: Kinetic Parameter Comparison

  4. Kinetic and Stoichiometric Parameter Calculation:

    • Calculates key parameters such as:
      • Specific growth rates (( \mu )).
      • Substrate uptake rates (( q )).
      • Biomass yields (( Y )).
    • Outputs results as a consolidated DataFrame for easy interpretation and visualization.

Future Development

Planned features include:

  • Advanced visualizations for kinetic and stoichiometric parameter comparisons across clones.
  • Automated identification of the exponential growth phase.
  • Expanded support for additional culture parameters (e.g., recombinant protein production).

This tool is ideal for researchers in biopharmaceutical development, streamlining the analysis of CHO cell cultures for clone optimization and process improvement.

Authors

Emiliano Balderas Ramírez
PhD Student at the Instituto de Biotecnología, UNAM
Email: ebalderas@live.com.mx
Phone: +52 2221075693

Dr. Octavio Tonatiuh Ramírez Reivich
Principal Investigator, Instituto de Biotecnología, UNAM
Email: tonatiuh.ramirez@ibt.unam.mx

Repository Structure

The repository is organized as follows:

clonalyzer/
│
├── data/                 # Contains input datasets (CSV files)
│   └── 2024-05-18_Clones_B_C_Kinetics.csv  # Example dataset
│
├── figures/              # Contains generated figures from the analysis
│   └── Glucose_and_Glutamine_vs_Time.png  # Example plot
│   └── kinetic_parameters_comparison.png # Example plot
│
├── clonalyzer.py         # Main script for data processing
├── README.md             # Documentation for the project
└── LICENSE               # License for the repository

Requirements

To run the notebook, ensure you have Python 3.8+ and the following packages installed:

  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scipy

Install these packages using pip:

pip install pandas numpy matplotlib seaborn scipy

Usage

  1. Prepare your dataset:

    • Place your kinetic data in the data/ folder. The dataset should be a CSV file formatted as described below.

  2. Run the notebook:

    • Open the Jupyter Notebook clonalyzer.ipynb in the script/ folder using JupyterLab or Jupyter Notebook: 
      jupyter notebook script/clonalyzer.ipynb
    • Follow the cells in the notebook to preprocess, clean, and analyze the data.

  3. Outputs:

    • The notebook generates cleaned datasets and visualizations (e.g., time-series plots, scatter plots).

Input File Requirements

File Format

The input data must be a CSV file with the following specifications:

  1. File Location:

    • Place the CSV file in the data/ folder of the repository.
    • Update the dataset_path variable in the code to reference your file: 
      dataset_path = 'data/your_file_name.csv'

  2. Metadata Row:

    • The first row in the CSV file is reserved for metadata. The script skips this row automatically during processing.
    • Leave this row blank or use it for notes about the experiment.

  3. Column Names and Units:

    • The dataset must include the following columns with the specified names and units:
Column Name Description Units Example
Clone Identifier for the CHO cell clone. - Clone_A, C1
T Timepoints for measurements. Days 0, 1, 2
G Glucose concentration. g/L 6.5, 5.9
Gln Glutamine concentration. mmol/L 2.5, 3.1
Xv Viable cell density. cells/mL 1.2e6, 2.5e6
Xd Dead cell density. cells/mL 5.0e4, 3.0e5
L Lactate concentration. g/L 0.5, 1.2
Glu Glutamate concentration. mmol/L 1.5, 2.0
V Viability as a percentage. % 95, 98
MAb Monoclonal antibody concentration. mg/mL 0.8, 1.5
rP Recombinant protein concentration. mg/mL 0.5, 0.9
rep Replicate number for each clone. Integer 1, 2, 3

Example Dataset

Clone T G Gln Xv Xd L Glu V MAb rP rep
C1 0.0 6.5 2.5 1.2e6 5.0e4 0.5 1.8 95 0.8 0.5 1
C1 1.0 6.2 2.4 1.8e6 4.5e4 0.6 2.0 98 1.0 0.6 1
C2 0.0 6.4 2.6 1.1e6 5.2e4 0.5 1.7 94 0.7 0.4 2
C2 1.0 6.1 2.3 1.7e6 4.7e4 0.6 1.9 97 0.9 0.5 2

Contact

For questions or suggestions, feel free to contact:
Emiliano Balderas Ramírez
PhD Student at the Instituto de Biotecnología, UNAM
Email: ebalderas@live.com.mx
Phone: +52 2221075693