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.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Cecchini G,Thiel M,Schelter B,Sommerlade Ldoi
10.1016/j.jneumeth.2018.06.011subject
Has Abstractpub_date
2018-09-01 00:00:00pages
31-36eissn
0165-0270issn
1872-678Xpii
S0165-0270(18)30190-0journal_volume
307pub_type
杂志文章abstract:BACKGROUND:Human neurons (hNT neurons), obtained from the NTera2/D1 precursor cell line, are highly valued by many neuroscientists as isolation of adult human primary neuronal cells continues to elude us. hNT neurons are generated by differentiation of the NT2 precursors for a period of 4 weeks followed by 2 weeks of m...
journal_title:Journal of neuroscience methods
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journal_title:Journal of neuroscience methods
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journal_title:Journal of neuroscience methods
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journal_title:Journal of neuroscience methods
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journal_title:Journal of neuroscience methods
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journal_title:Journal of neuroscience methods
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journal_title:Journal of neuroscience methods
pub_type: 杂志文章
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journal_title:Journal of neuroscience methods
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journal_title:Journal of neuroscience methods
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journal_title:Journal of neuroscience methods
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journal_title:Journal of neuroscience methods
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journal_title:Journal of neuroscience methods
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journal_title:Journal of neuroscience methods
pub_type: 杂志文章
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journal_title:Journal of neuroscience methods
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