# A guided network estimation approach using multi-omic information

## TL;DR

This paper introduces a new method for building biological networks by using one type of data to guide the analysis of another, helping to uncover relationships between metabolites in Arabidopsis.

## Contribution

The novel contribution is a guided network estimation method that integrates multi-omic data using a hierarchical relationship between omics types.

## Key findings

- The method successfully detects metabolite groups with similar genetic or transcriptomic bases in Arabidopsis.
- The approach conditions target networks on guiding data networks, improving integration of multi-omic information.
- The method uses Lasso and L2 penalties to reduce predictors and enforce network-based coefficient similarities.

## Abstract

In systems biology, an organism is viewed as a system of interconnected molecular entities. To understand the functioning of organisms it is essential to integrate information about the variations in the concentrations of those molecular entities. This information can be structured as a set of networks with interconnections and with some hierarchical relations between them. Few methods exist for the reconstruction of integrative networks.

In this work, we propose an integrative network reconstruction method in which the network organization for a particular type of omics data is guided by the network structure of a related type of omics data upstream in the omic cascade. The structure of these guiding data can be either already known or be estimated from the guiding data themselves.

The method consists of three steps. First a network structure for the guiding data should be provided. Next, responses in the target set are regressed on the full set of predictors in the guiding data with a Lasso penalty to reduce the number of predictors and an L2 penalty on the differences between coefficients for predictors that share edges in the network for the guiding data. Finally, a network is reconstructed on the fitted target responses as functions of the predictors in the guiding data. This way we condition the target network on the network of the guiding data.

We illustrate our approach on two examples in Arabidopsis. The method detects groups of metabolites that have a similar genetic or transcriptomic basis.

The online version contains supplementary material available at 10.1186/s12859-024-05778-7.