PyPCR 0.0.2

A library for processing and interpreting DNA high-resolution melt and amplification curves for the meningoencephalitis panel.

PyPCR

A Python library for processing and interpreting DNA High-Resolution Melt and Amplification Curves for the Meningoencephalitis Panel

Installation • Features • Documentation • Help

PyPCR

PyPCR primarily focuses on feature extraction and interpreting results from parsed data. here the first step is to parses files into the .rex format, extracting essential information such as HRM (High Resolution Melting) data, cycle threshold (CT) values, and sample details. It facilitates feature extraction from HRM data by converting it into melt curves, and for parsed CT data, it identifies take-off points crucial for determining the severity of affected pathogens. PyPCR integrates these features to interpret results and generate comprehensive reports for respective identifiers.

Installing with PIP

python -m pip install PyPCR

or

pip3 install PyPCR

Classifiers

Development Status	5 - Production/Stable
Intened Audience	Healthcare
License	OSI Approved :: MIT License
Operating System	Microsoft :: Windows :: Windows 10
Programming Language	Python 3

Features

1. Rextractor
   1. Extract the data from Rotor Gene Experiment(.rex) files.
      1. High Resolution Melt (HRM)
      2. Amplification Curve - Cycle Time (CT)
   2. Processing Data
      1. Filter Only MEP Pathogens
      2. Separate by patients
2. MEP panel
   1. Feature Extraction
      1. Target – Pathogen Name
      2. Temperature (Peak of the Melt Curve)
      3. Width
      4. Prominence
      5. Take of Point
      6. Take down Point
      7. Area Under the curve
   2. Interactive Visulization.
      1. High Resolution Melt
      2. Melt Curve
      3. Amplification Curve
   3. Result
      1. Interpreting the results of the pathogens
      2. Generate report for test Results

Input Data format

The input file for the library is the run file from the Rotor-Gene machine, which is in the format of a Rotor-Gene Experiment (.rex) file, and we can directly provide the .rex file without any preprocessing.

Documentation

Importing Library

from PyPCR.Rextractor import rex_reader
from PyPCR.Mep_panel import Mep_diagnoser

PyPCR.Rextractor.rex_reader()

PyPCR.Rextractor.rex_reader(path = None)

Parameters:

path : Rotor Gene Experiment file path (.rex)

Returns:

A dictionary containing various patient ID for each separate patients: class 'dict_keys'

A dictionary containing various dataframes (HRM) for respective patient ID's: class 'dict_keys'

A dictionary containing various dataframes (CT) for respective patient ID's: class 'dict_keys'

Example

from PyPCR.Rextractor import rex_reader
Patient_ids,raw_hrm,raw_ct = rex_reader("your .rex file path")

PyPCR.Mep_panel.Mep_diagnoser

md = Mep_diagnoser()

hrm_data = md.transform_hrm(dataframe: Any,figure: bool = False)
ct_data = md.transform_ct(dataframe: Any,figure: bool = False)
melt = md.hrm_to_melt(figure: bool = False)
hrm_features = md.hrm_feature_extraction()
ct_values = md.ct_value()
md.Tm_threshold()
result = md.predict_result()
report = md.report( output_fie_path: Any,patient_id: Any)

Function Documentation

transform_hrm

Parameters:

• dataframe: HRM data obtained from rex_reader().
• figure (optional): Boolean flag indicating whether to generate an HRM graph.

Returns:

• Processed HRM data.
• If figure is True, an HRM graph is also returned.

transform_ct

Parameters:

• dataframe: CT data acquired from rex_reader().
• figure (optional): Boolean flag to specify if a CT graph should be produced.

Returns:

• Processed CT data.
• If figure is True, a CT graph is included in the output.

hrm_to_melt

Parameters:

• figure (optional): Boolean flag for generating a melt graph.

Returns:

• Melted data.
• If figure is True, a melt graph is also provided.

hrm_feature_extraction

No Parameters Needed

Returns:

• HRM features extracted from the data.

ct_value

No Parameters Needed

Returns:

• CT values derived from the data.

predict_result

No Parameters Needed

Returns:

• Predicted result based on the processed data.

report

Parameters:

• output_file_path: Path to save the generated report.
• patient_id: Identifier for the patient.

Returns:

• Generated report based on the analysis.

Sample code Snippet for interpreting individual result

from PyPCR.Rextractor import rex_reader
from PyPCR.Mep_panel import Mep_diagnoser

# File extraction from the rex file
patient_id,hrm,ct = rex_reader("Your .rex file path")
print(patient_id)

# Create Object
md = Mep_diagnoser()

# Interpreting the results by providing patient ID manually
hrm_data = md.transform_hrm(hrm[id])
ct_data = md.transform_ct(ct[id])
melt = md.hrm_to_melt(figure: False)
hrm_features = md.hrm_feature_extraction()
ct_values = md.ct_value()
md.Tm_threshold()
result = md.predict_result()
report = md.report( output_fie_path=f"{id}.pdf",patient_id={id})

Sample code Snippet for interpreting all individual result

from PyPCR.Rextractor import rex_reader
from PyPCR.Mep_panel import Mep_diagnoser

# File extraction from the rex file
patient_id,hrm,ct = rex_reader("Your .rex file path")

# Create Object
md = Mep_diagnoser()

# Interpreting the results
for id in patient_id:
    hrm_data = md.read_hrm(hrm[id])
    ct_data = md.read_ct(ct[id])
    melt = md.hrm_to_melt(figure: False)
    hrm_features = md.hrm_feature_extraction()
    ct_values = md.ct_value()
    md.Tm_threshold()
    result = md.predict_result()
    report = md.report( output_fie_path=f"{id}.pdf",patient_id={id})

Getting Help

If you need to get in touch with the team, please contact through email address:
[e-mail]
[e-mail]

Developers

The developer of this Software are M.Sc Data Analytics Students in Department of Computer Application at Bharathiar University

Chandru Ganeshan

Vikram Sekar