TY - JOUR

T1 - Improving network inference

T2 - The impact of false positive and false negative conclusions about the presence or absence of links

AU - Cecchini, Gloria

AU - Thiel, Marco

AU - Schelter, Bjoern

AU - Sommerlade, Linda

N1 - This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 642563.

PY - 2018/9/1

Y1 - 2018/9/1

N2 - Background A reliable inference of networks from data is of key interest in the Neurosciences. Several methods have been suggested in the literature to reliably determine links in a network. To decide about the presence of links, these techniques rely on statistical inference, typically controlling the number of false positives, paying little attention to false negatives.New method In this paper, by means of a comprehensive simulation study, we analyse the influence of false positive and false negative conclusions about the presence or absence of links in a network on the network topology. We show that different values to balance false positive and false negative conclusions about links should be used in order to reliably estimate network characteristics. We propose to run careful simulation studies prior to making potentially erroneous conclusion about the network topology.Results Our analysis shows that optimal values to balance false positive and false negative conclusions about links depend on the network topology and characteristic of interest.Comparison with existing methods Existing methods rely on a choice of the rate for false positive conclusions. They aim to be sure about individual links rather than the entire network. The rate of false negative conclusions is typically not investigated.Conclusions Our investigation shows that the balance of false positive and false negative conclusions about links in a network has to be tuned for any network topology that is to be estimated. Moreover, within the same network topology, the results are qualitatively the same for each network characteristic, but the actual values leading to reliable estimates of the characteristics are different.

AB - Background A reliable inference of networks from data is of key interest in the Neurosciences. Several methods have been suggested in the literature to reliably determine links in a network. To decide about the presence of links, these techniques rely on statistical inference, typically controlling the number of false positives, paying little attention to false negatives.New method In this paper, by means of a comprehensive simulation study, we analyse the influence of false positive and false negative conclusions about the presence or absence of links in a network on the network topology. We show that different values to balance false positive and false negative conclusions about links should be used in order to reliably estimate network characteristics. We propose to run careful simulation studies prior to making potentially erroneous conclusion about the network topology.Results Our analysis shows that optimal values to balance false positive and false negative conclusions about links depend on the network topology and characteristic of interest.Comparison with existing methods Existing methods rely on a choice of the rate for false positive conclusions. They aim to be sure about individual links rather than the entire network. The rate of false negative conclusions is typically not investigated.Conclusions Our investigation shows that the balance of false positive and false negative conclusions about links in a network has to be tuned for any network topology that is to be estimated. Moreover, within the same network topology, the results are qualitatively the same for each network characteristic, but the actual values leading to reliable estimates of the characteristics are different.

KW - network inference

KW - node degree distribution

KW - false positive

KW - false negative

KW - statistical inference

U2 - 10.1016/j.jneumeth.2018.06.011

DO - 10.1016/j.jneumeth.2018.06.011

M3 - Article

VL - 307

SP - 31

EP - 36

JO - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

SN - 0165-0270

ER -