MS-BFF package has been uploaded to PyPI, so you can install it by pip conveniently.

pip install msbff

• Module 1 Preprocessing data: Read data and perform data cleaning

• Module 2 Processing data: Calculate each variable value.

• Module 3 Data visualization: Plot one line chart and three heatmap.

Run the command and view the help information.

msbff -h

You can run the command with the default parameters and the output files will be saved in a specified folder.

mkdir output  # create output folder
msbff -i rawdata.csv -o output  # run msbff in default

• data_extraction.csv: The data obtained by filtering the input raw data according to the requirements.

• block_score.csv: Percentage of the sum of Pearson correlation coefficients (PCC) within each block.

• max_inhibition_rate.csv: Maximum value of Pearson correlation coefficient (PCC) within each block.

• relative_signal_intensity.csv: Percentage of the sum of S/N average within each block.

• Fig.png: Visualization results of the above three tables of data.

Don't hesitate to contact me by email if you have any problems.

E-mail: [email protected]