Abstract
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.
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.
Original language  English 

Pages (fromto)  3136 
Number of pages  6 
Journal  Journal of Neuroscience Methods 
Volume  307 
Early online date  26 Jun 2018 
DOIs  
Publication status  Published  1 Sep 2018 
Keywords
 network inference
 node degree distribution
 false positive
 false negative
 statistical inference
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Bjoern Schelter
 School of Natural & Computing Sciences, Physics  Personal Chair
 Institute for Complex Systems and Mathematical Biology (ICSMB)
Person: Academic