Chromatopy
ℹ️ Overview
chromatopy is a Python package which aims to streamline the data processing and analysis of chromatographic time-course and calibration experiments.
It can read peak area data from various formats, enrich it with metadata such as reaction time, temperature, pH, and initial concentrations of reaction components. Finally, the peaks of interest can be aggregated, concentrations calculated, and the time-course data for each analyte of interest transformed into an EnzymeML Document.
graph LR
AD[🌈 Chromatographic Instrument] --> CAL
AD --> RXN
subgraph "📁experimental_data"
CAL["<div style='text-align:left;font-family:monospace'>
📂 calib_substrate<br>
├── mh1_10mM.json<br>
├── mh2_50mM.json<br>
└── mh3_90mM.json<br><br>
📂 calib_prod1<br>
├── prod1_10mM.json<br>
├── prod1_50mM.json<br>
└── prod1_90mM.json<br><br>
</div>"]
RXN["<div style='text-align:left;font-family:monospace'>
📂 reaction_mh9<br>
├── mh9_1h.json<br>
├── mh9_2h.json<br>
├── mh9_3h.json<br>
├── mh9_4h.json<br>
├── mh9_5h.json<br>
├── mh9_6h.json<br>
└── mh9_12h.json
</div>"]
end
CAL -->|read| C_cal{"<span style='font-family:monospace'><b>chromatopy</b></span><br>"}
RXN -->|read| C_react{"<span style='font-family:monospace'><b>chromatopy</b></span><br>"}
cal1["<div style='text-align:left'>
Define measured molecules<br>
– retention time<br>
– PubChem CID
</div>"]
cal2["<div style='text-align:left'>
Create calibration standard
</div>"]
E4["Define reaction conditions"]
E3["Add measured molecules"]
E5["Define enzymes"]
Enz[📄 EnzymeML Document]
subgraph "Calibration mode"
C_cal --> cal1
cal1 --> cal2
end
subgraph "Reaction mode"
C_react --> E4
E4 --> E3
E3 --> E5
cal2 --> E3
end
E5 -->|convert| Enz
For some output formats, chromatopy provides a direct interface to read in data. For more information on the supported file formats and data preparation to use the chromatopy workflow, refer to the data preparation section.
⭐ Key Features
- 🌱 Low friction data processing
Leave behind data processing in spreadsheet applications and directly start with data analysis based on raw data. - 🧪 Enrich reaction data with metadata
Assign metadata like initial concentrations of reactants, temperature, pH, etc. to reaction data to yield modeling-ready data. - 📈 Create and apply calibration curves
Create calibrators for your analytes and use them throughout your data analysis for seamless concentration calculation. - 📂 FAIR data
Transform your data into EnzymeML format for subsequent analysis pipelines.
🛠️ Installation
Install chromatopy using pip:
or from source: