Issue 7, 2017

GeneSPIDER – gene regulatory network inference benchmarking with controlled network and data properties

Abstract

A key question in network inference, that has not been properly answered, is what accuracy can be expected for a given biological dataset and inference method. We present GeneSPIDER – a Matlab package for tuning, running, and evaluating inference algorithms that allows independent control of network and data properties to enable data-driven benchmarking. GeneSPIDER is uniquely suited to address this question by first extracting salient properties from the experimental data and then generating simulated networks and data that closely match these properties. It enables data-driven algorithm selection, estimation of inference accuracy from biological data, and a more multifaceted benchmarking. Included are generic pipelines for the design of perturbation experiments, bootstrapping, analysis of linear dependence, sample selection, scaling of SNR, and performance evaluation. With GeneSPIDER we aim to move the goal of network inference benchmarks from simple performance measurement to a deeper understanding of how the accuracy of an algorithm is determined by different combinations of network and data properties.

Graphical abstract: GeneSPIDER – gene regulatory network inference benchmarking with controlled network and data properties

Supplementary files

Article information

Article type
Paper
Submitted
25 Jan 2017
Accepted
20 Apr 2017
First published
25 Apr 2017

Mol. BioSyst., 2017,13, 1304-1312

GeneSPIDER – gene regulatory network inference benchmarking with controlled network and data properties

A. Tjärnberg, D. C. Morgan, M. Studham, T. E. M. Nordling and E. L. L. Sonnhammer, Mol. BioSyst., 2017, 13, 1304 DOI: 10.1039/C7MB00058H

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Spotlight

Advertisements