vksriharsha / Causal-Inference-on-Gene-Expression-Data

Python implementation of the PCA-CMI algorithm proposed by Zhang et al. and comparison of the results with the IC algorithm to predict gene regulatory network skeleton and edge orientations.

Identifying Gene regulatory network using PCA-CMI and IC algorithms

We have implemented the PCA-CMI algorithm presented in the paper by Zhang et al. (https://academic.oup.com/bioinformatics/article/28/1/98/221936?login=true) and compared the causal graph obtained, with the gold standard network, as well as the network obtained by the out of the box IC algorithm.

The PC-CMI approach uses a conditional mutual information (CMI) measure to estimate the conditional independence between two variables, given a set of known variables. The PC algorithm starts with a complete graph, and uses a greedy approach to eliminate edges based on the conditional independence test.

Dataset: https://iastate.app.box.com/folder/151080162944