SecBic-BCCA: Secured Biclusterings - Bi-Correlation Clustering Algorithm: privacy-preserving gene expression data analysis by biclustering algorithm -- bi-correlation clustering algorithm -- over gene expression data with Homomorphic Encryption operations such as sum, or matrix multiplication in Python under the MIT license.
We apply Pyfhel as a python wrapper for the Microsoft SEAL library on top of the existing implementation of the algorithm in biclustlib.
First you need to ensure that all packages have been installed.
- See
requirements.txt - numpy>=1.23.1
- setuptools>=65.5.0
- pandas>=1.5.0
- scikit-learn>=1.1.1
- Pyfhel>=3.3.1
- matplotlib>=3.5.2
- scipy>=1.9.0
- munkres>=1.1.4
You can clone this repository:
> git clone https://github.com/ShokofehVS/SecBic-BCCA.git
If you miss something you can simply type:
> pip install -r requirements.txt
If you have all dependencies installed:
> pip3 install .
To install Pyfhel, on Linux,gcc6 for Python (3.5+) should be installed. (more information regarding installation of Pyfhel )
> apt install gcc
Biclustering or simultaneous clustering of both genes and conditions as a new paradigm was introduced by Cheng and Church's Algorithm (CCA). The concept of bicluster refers to a subset of genes and a subset of conditions with a high similarity score, which measures the coherence of the genes and conditions in the bicluster. It also returns the list of biclusters for the given data set.
Our input data is yeast Saccharomyces cerevisiae cell cycle taken from Tavazoie et al. (1999) which was used in the orginal study by Cheng and Church;
To measure the similarity of encrypted biclusters with non-encrypted version, we use Clustering Error (CE) as an external evaluation measure that was proposed by Patrikainen and Meila (2006);