PyHRM

PyHRM is a python based library for processing High Resolution Melting (HRM) data, especially, DNA melting signals to extact features like 'Melting Temperatures', 'Take-off and Touch-down points of melting signal (Temperature at which peak start rising and temperature at which peak falls down)','Peak prominences',and 'Area Under the curve'. Additionally, the library offers interactive visualization for DNA melting singal and vision based filtering, to eliminate noisy signals from the data and provides only genuine peaks with all the above mentioned features.

Installing with PIP

python -m pip install PyHRM

or

pip3 install PyHRM

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

• Rapid preprocessing.
• Feature Extraction
  • Tm (Melting Temperature (Max 2))
  • Tstart (Starting temperature point)
  • Tend (Ending Temperature)
  • Prominence
  • Area Under the curve
• Interactive Visulization.
• Computer Vision based thresholding for eliminating noisy signals.
• Report Generation.

Input Data format

Format
The input format should be as followed below. 'Text','X','Y'....

The current release only support .xls and .xlsx formats. Further updates on multiple file supports will be released in the upcoming versions.

Documentation

Import

from PyHRM import melt

Creating the class Instance

obj = melt.MeltcurveInterpreter()

PyHRM.melt.MeltcurveInterpreter.data_read

PyHRM.melt.MeltcurveInterpreter.data_read(data = None, path = None, labels =False, index = False, figure = False)

The function takes either a pandas dataframe or the path of the file (.xls, .xlsx). The method could read CT, MELT and raw fluorescence data as well.

Parameters: data : pandas dataframe object

        A pandas dataframe with the specified input format of HRM data extracted from machines.

       path : path of the file (.xls or .xlsx)

        A path of the HRM data file extracted from machines.

       labels : bool : default False

        Returns a list containing dataframe and label of the samples given in the 'Text' attribute based on a boolean value.

       index : bool : default False

        Required a boolean value to remove the index (if the HRM data has.)

       figure : bool : default False

        Required a boolean value to render a interactive plot in the browser.

Returns:    x, y cordinates : pandas.core.frame.DataFrame

        A pandas dataframe with the temperature co-ordinates as 'X' and the signal co-ordinates as 'Y','Y.1'..'Y.n'

       x,y cordinates and labels : list

        If lables is True, returns a list containing a dataframe and lables

Warns:    ValueError

        Raised when unsupported data format passed.

Example

from PyHRM import melt
obj = melt.MeltcurveInterpreter()

#reading the HRM data
hrmdata = obj.data_read(path = './path/file.xls', figure = True)

Alternatively,

import pandas as pd
import openpyxl
from PyHRM import melt
obj = melt.MeltcurveInterpreter()

#reading data with pandas
data = pd.read_excel('./path/file.xls', engine = 'xlrd')

#passing the dataframe to the function
hrmdata = obj.data_read(data = data, figure = True)

PyHRM.melt.MeltcurveInterpreter.plot

PyHRM.melt.MeltcurveInterpreter.plot(data,save = False)

The function takes a pandas dataframe contains signal values and render back the respective figure. The plot function takes any data like CT, MELT and as well as raw fluorescence, and gives back the corresponding visulazation.

Parameters: data : pandas dataframe object

        A pandas dataframe contains signal values.

       save : bool : default False

        Required a boolean value to save the plot in a figure object.

Returns:    figure: plotly.graph_objs._figure.Figure

        Returns an interactive plotly figure object.

Example

from PyHRM import melt
obj = melt.MeltcurveInterpreter()

#reading the HRM data
hrmdata = obj.data_read(path = './path/file.xls')

#visualizing the data
fig = obj.plot(data = hrmdata, save = True)
fig.show()

PyHRM.melt.MeltcurveInterpreter.melt_conversion

PyHRM.melt.MeltcurveInterpreter.melt_conversion(figure = False, return_value =False, download = False)

This methods only works for raw fluorescence data, and the input of this method is the class member itself. It converts the raw fluorescence signals into melting signals with signal smoothening.

Note: This method is the beta version of the library, the results may not be acuratae or appropriate and this is still in development

Parameters: figure : bool : default False

         Required a boolean value to render the resultant values as a plot.

       return_value : bool : default False

        Required a boolean value to return the result as dataframe.

       download : bool : default False

        Required a boolean value to save the resultant values as comma separated values.

Returns:    melt signal co-ordinates: pandas.core.frame.DataFrame

        Returns a dataframe, contains melting signal co-ordinates.

Example

from PyHRM import melt
obj = melt.MeltcurveInterpreter()

#reading the raw fluorescence data
rfdata = obj.data_read(path = './path/file.xls')

meltdata = obj.melt_conversion(return_value = True, figure = True, download = True)

PyHRM.melt.MeltcurveInterpreter.feature_detection

PyHRM.melt.MeltcurveInterpreter.feature_detection(return_values =False, download = False)

This methods performs feature extraction process on melting signal data, that extracts features like 'Tm','Tstart','Tend','Prominence','Area under the curve'. This also performs noise elimination using a trained CNN model embedded in the package. The input of the method is the class member itself.

Parameters: return_values : bool : default False

        Required a boolean value to return the result as dataframe.

       download : bool : default False

        Required a boolean value to save the resultant values as comma separated values.

Returns:    Features of the signal: pandas.core.frame.DataFrame

        Returns a dataframe, contains features of melting signals

Example

from PyHRM import melt
obj = melt.MeltcurveInterpreter()

#reading the HRM data
hrmdata = obj.data_read(path = './path/file.xls')

#Extracting features of the melt signals
features = obj.feature_detection(return_values = True)

PyHRM.melt.MeltcurveInterpreter.report

PyHRM.melt.MeltcurveInterpreter.report()

The method generates a report with the feature extracting data and it's corresponding melting signal figures.

Parameters: None

Example

from PyHRM import melt
obj = melt.MeltcurveInterpreter()

#reading the HRM data
hrmdata = obj.data_read(path = './path/file.xls')

#Extracting features of the melt signals
features = obj.feature_detection(return_values = True)

#Generating the report
obj.report()

Getting Help

If you need to get in touch with the team, please contact through email address: feusion.ai@gmail.com

Meet the Team

The developers in this project are post graduate students in the Department of Computer Applications @ Bharathiar University

Rajagopal S

Vignesh R

Senthil Kumar N