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A simple null model for inferences from network enrichment analysis
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.ORCID iD: 0000-0001-5689-9797
2018 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 13, no 11, article id e0206864Article in journal (Refereed) Published
Abstract [en]

A prevailing technique to infer function from lists of identifications, from molecular biological high-throughput experiments, is over-representation analysis, where the identifications are compared to predefined sets of related genes often referred to as pathways. As at least some pathways are known to be incomplete in their annotation, algorithmic efforts have been made to complement them with information from functional association networks. While the terminology varies in the literature, we will here refer to such methods as Network Enrichment Analysis (NEA). Traditionally, the significance of inferences from NEA has been assigned using a null model constructed from randomizations of the network. Here we instead argue for a null model that more directly relates to the set of genes being studied, and have designed one dynamic programming algorithm that calculates the score distribution of NEA scores that makes it possible to assign unbiased mid p values to inferences. We also implemented a random sampling method, carrying out the same task. We demonstrate that our method obtains a superior statistical calibration as compared to the popular NEA inference engine, BinoX, while also providing statistics that are easier to interpret.

Place, publisher, year, edition, pages
PUBLIC LIBRARY SCIENCE , 2018. Vol. 13, no 11, article id e0206864
National Category
Genetics
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URN: urn:nbn:se:kth:diva-239780DOI: 10.1371/journal.pone.0206864ISI: 000449772600027PubMedID: 30412619Scopus ID: 2-s2.0-85056317407OAI: oai:DiVA.org:kth-239780DiVA, id: diva2:1276678
Note

QC 20190108

Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-08-20Bibliographically approved

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Käll, Lukas

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CiteExportLink to record
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  • apa
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