A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields
Tekijät: Robinson S, Nevalainen J, Pinna G, Campalans A, Radicella JP, Guyon L
Kustantaja: OXFORD UNIV PRESS
Julkaisuvuosi: 2017
Journal: Bioinformatics
Tietokannassa oleva lehden nimi: BIOINFORMATICS
Lehden akronyymi: BIOINFORMATICS
Vuosikerta: 33
Numero: 14
Aloitussivu: I170
Lopetussivu: I179
Sivujen määrä: 10
ISSN: 1367-4803
eISSN: 1460-2059
DOI: https://doi.org/10.1093/bioinformatics/btx244
Tiivistelmä
Motivation:Incorporating gene interaction data into the identification of `hit' genes in genomic experiments is a well-established approach leveraging the 'guilt by association' assumption to obtain a network based hit list of functionally related genes. We aim to develop a method to allow for multivariate gene scores and multiple hit labels in order to extend the analysis of genomic screening data within such an approach.Results: We propose a Markov random field-based method to achieve our aim and show that the particular advantages of our method compared with those currently used lead to new insights in previously analysed data as well as for our own motivating data. Our method additionally achieves the best performance in an independent simulation experiment. The real data applications we consider comprise of a survival analysis and differential expression experiment and a cell-based RNA interference functional screen.
Motivation:Incorporating gene interaction data into the identification of `hit' genes in genomic experiments is a well-established approach leveraging the 'guilt by association' assumption to obtain a network based hit list of functionally related genes. We aim to develop a method to allow for multivariate gene scores and multiple hit labels in order to extend the analysis of genomic screening data within such an approach.Results: We propose a Markov random field-based method to achieve our aim and show that the particular advantages of our method compared with those currently used lead to new insights in previously analysed data as well as for our own motivating data. Our method additionally achieves the best performance in an independent simulation experiment. The real data applications we consider comprise of a survival analysis and differential expression experiment and a cell-based RNA interference functional screen.
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