Significance evaluation in factor graphs

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Dokumenter

DOI

Background
Factor graphs provide a flexible and general framework for specifying probability distributions. They can capture a range of popular and recent models for analysis of both genomics data as well as data from other scientific fields. Owing to the ever larger data sets encountered in genomics and the multiple-testing issues accompanying them, accurate significance evaluation is of great importance. We here address the problem of evaluating statistical significance of observations from factor graph models.

Results
Two novel numerical approximations for evaluation of statistical significance are presented. First a method using importance sampling. Second a saddlepoint approximation based method. We develop algorithms to efficiently compute the approximations and compare them to naive sampling and the normal approximation. The individual merits of the methods are analysed both from a theoretical viewpoint and with simulations. A guideline for choosing between the normal approximation, saddle-point approximation and importance sampling is also provided. Finally, the applicability of the methods is demonstrated with examples from cancer genomics, motif-analysis and phylogenetics.

Conclusions
The applicability of saddlepoint approximation and importance sampling is demonstrated on known models in the factor graph framework. Using the two methods we can substantially improve computational cost without compromising accuracy. This contribution allows analyses of large datasets in the general factor graph framework.
Bidragets oversatte titelSignifikans evaluering i faktor grafer
OriginalsprogEngelsk
TidsskriftBMC Bioinformatics
Vol/bind18
Sider (fra-til)199
ISSN1471-2105
DOI
StatusUdgivet - 31 mar. 2017

    Forskningsområder

  • Significance evaluation, factor graph, saddlepoint approximation, importance sampling

Se relationer på Aarhus Universitet Citationsformater

Download-statistik

Ingen data tilgængelig

ID: 111274026