Learning Directed-Acyclic-Graphs from Large-Scale Genomics Data

TL;DR

This paper introduces GENIE, a novel linear integer optimization method for learning gene interaction DAGs from noisy double knockout data, enhanced by GI-profile data and a scalable sequential approach, outperforming traditional methods.

Contribution

The paper presents a new optimization-based framework, GENIE, for accurately inferring genetic interaction networks from large-scale noisy data, with extensions for improved detection and scalability.

Findings

GENIE outperforms conventional techniques in simulations.

Incorporating GI-profile data enhances detection accuracy.

The sequential scalability method enables analysis of large gene sets.

Abstract

In this paper we consider the problem of learning the genetic-interaction-map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double knockout (DK) data. Based on a set of well established biological interaction models we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE-program by incorporating genetic-interactions-profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically stressable results for real measurement data. Finally, we show via numeric simulations that the GENIE-program as well as the GI-profile data extended GENIE (GI-GENIE)-program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.