mtphandler 0.2.20

Python package for processing, enriching, and converting microtiter plate data into standardized EnzymeML time-course data, ready for data science.

MTPHandler

ℹ️ Overview

mtphandler is a tool for managing and processing data from microtiter plates. It allows direct reading of output files from various photometers, enabling low-friction data handling. The tool facilitates a workflow for importing raw data, assigning molecules with their respective concentrations and units to wells.

Wells for creating standard curves can be automatically detected and fitted to calibration models, which are then used to calculate the concentration of unknown samples. Finally, the plate data can be transformed into time-course concentration data in the EnzymeML format, enabling downstream analysis.

graph LR
  AP[🧪 Plate Reader] --> A[📄 Output File];
  style AP fill:transparent,stroke:#000,stroke-width:2px;
  A -->|read| B{mtphandler}
  style B stroke-width:4px
  subgraph in Jupyter Notebook
    subgraph with mtphandler
        B --> B1[Enrich Data with Metadata]
        B1 --> B2[Blank Data]
        B2 --> B3[Create and Apply Calibration Models]
        B3 --> B

        style B1 stroke-dasharray: 5, 5
        style B2 stroke-dasharray: 5, 5
        style B3 stroke-dasharray: 5, 5
    end
  B -->|convert| G[📄 EnzymeML time-course Data]
  G <-.-> H[📊 Data Science and Insights]

  style H stroke-dasharray: 5, 5,fill:transparent
  end
  G -->|export| I[📄 EnzymeML File]

⭐ Key Features

• 🚀 Parser Functions
  Features a custom parser for various plate readers, enabling low-friction data processing.

• 🌟 Enrich measured data with metadata
  Assigns molecules with their respective concentration and unit to wells, capturing the experimental context of each well.

• ⚙️ Adaptive Data Processing
  Automatically adapts and blanks measurement data based on initial conditions set for each well. Automatically classifies wells without protein as calibration data and those with protein as reaction data.

• 🌐 FAIR Data
  Maps well data to the standardized EnzymeML format, yielding time-course data with metadata for further analysis.

🔬 Supported Plate Readers

The following table lists currently supported plate reader output formats:

Manufacturer	Model	File Format
Agilent	BioTek Epoch 2	xlsx
Molecular Devices	SpectraMax 190	txt
Tekan	Magellan (processing software)	xlsx
Tekan	Spark	xlsx
Thermo Scientific	Multiskan SkyHigh	xlsx
Thermo Scientific	Multiskan Spectrum 1500	txt

📦 Installation

Install mtphandler via PyPI:

pip install mtphandler # 🚧 not released yet

or from source:

pip install git+https://github.com/FAIRChemistry/MTPHandler.git

Please refer to the documentation for more information on how to use the package.