Inference of Sparse Networks with Unobserved Variables

This repository contains code the RCweb algorythm that allows inference of sparse networks with unobserved variables given a set of noisy measurements of the observed variables. The RCweb algorythm and its application to gene regulatory networks is described in Slavov, Proceedings of Machine Learning Research (2010). A brief overview of the algorythm was also presented at the Biological systems: In search of direct causal mechanisms.

Functions and scripts reproducing the analysis reported by Slavov, 2010

Man functions

• SpAce.m
• SpAce_Path.m

Supporting functions called by the main functions:

• pm.m -- Computes the largest eigenvector and eigenvalue of a matrix from initial guess
• inv_rank1_add.m -- Rank one (addition) update of an inverse
• inv_rank1_red.m -- Rank one (subtraction) update of an inverse
• inv_rank1.m -- Rank one update of an inverse addition or subtraction depending on the third argument
• inv_up.m -- rank k update on an inverse

Supporting functions called by the scripts:

data_Gen.m -- Simulates data from a model of a sparse network c_corr.m -- Computes the the correlations between the most correlated columns of two matrices

Video presentaion of the RCweb algorythm applied to transcriptional networks

Correspondence

Questions can be directed to the Slavov Laboratory