QuPath H-DAB Project
  • 📊QuPath guide for H-DAB Cell Counting Docs
  • 💡Pipeline
    • 💡Simple Pipeline
  • Fundamentals
    • 🛠️Getting Set Up
  • QuPath H-Dab Docs
    • 💡QuPath H-DAB Tutorial
      • Creating and Opening of Projects
      • Estimating Stain Vectors
      • Training Image Creation
      • Cell Detection
      • Object Classifier
      • Tissue Detection
        • 1️⃣Automatic General Tissue Detection
        • 2️⃣Manual Specific Tissue Detection
    • 🧾QuPath Script
      • Batch Processing
    • 📂Output
  • Result Analysis Docs
    • 💡Processing Package Tutorial
      • Addition Information for Python insiders
      • In-Depth Python usage
    • 🗄️Final Spreadsheet creation
    • 🆘Out-of-memory error
  • non-expert docs
    • 🔎QuPath Installation
    • 💻Git Bash Installation
    • 🖥️Python Installation & Packages
    • Git Bash installation
Powered by GitBook
On this page
  • Functions specifics
  • License
  1. Result Analysis Docs
  2. Processing Package Tutorial

In-Depth Python usage

This section of the Docs is to describe each function and its utility in the ProcessMRXS Class.

Functions specifics

Call the process_data method to process MRXS data, merge it with inventory, and calculate immunopositivity statistics for every slide (this function is called internally for each file in the process_directory method):

result_df = processor.process_data()

To process immunopositivity rate from Excel/CSV files and merge into a unique main DataFrame, call the process_positivity method:

xlsx_file = "path/to/your/immunopositivity_data.xlsx"
final_df = ProcessMRXSData.process_positivity(xlsx_file, result_df)

Process MRXS data from a directory, save antibody-specific data, and return the final DataFrame using the process_directory method (internally calling the process_data):

directory_path = "path/to/your/mrxs/files/directory"
output_path = "path/to/output/data/directory"
output_filename = "output_data.csv"  # Name of the output CSV file
final_data = ProcessMRXSData.process_directory(directory_path, inventory_file, output_path, output_filename)

Process immunopositivity rate from saved files and merge it into a final DataFrame using the process_rate method:

final_rate = ProcessMRXSData.process_rate(output_path, final_data)

To generate and save correlation heatmaps based on immunopositivity rate data, call the process_heatmaps method:

data_file = "path/to/your/immunopositivity_data.csv"
ProcessMRXSData.process_heatmaps(data_file)

To generate and save scatterplots based on immunopositivity rate data, use the process_scatterplots method:

data_file = "path/to/your/immunopositivity_data.csv"
ProcessMRXSData.process_scatterplots(data_file)

License

This project is licensed under the MIT License - see the LICENSE file for details to the rightful owner.

Last updated 1 year ago

For more details, examples, and command-line usage, please refer to the code and documentation in this .

💡
repository