Assessing statistical significance in causal graphs.

BMC Bioinformatics
Leonid ChindelevitchDaniel Ziemek

Abstract

Causal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs--directed graphs whose edges additionally have a sign denoting upregulation or downregulation--can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities; conversely, they also enable inference of causative agents behind observed expression changes. However, due to their complex nature, signed causal graph models present special challenges with respect to assessing statistical significance. In this paper we frame and solve two fundamental computational problems that arise in practice when computing appropriate null distributions for hypothesis testing. First, we show how to compute a p-value for agreement between observed and model-predicted classifications of gene transcripts as upregulated, downregulated, or neither. Specifically, how likely are the classifications to agree to the same extent under the null distribution of the observed classification being randomized? This problem, which we call "Ternary Dot Product Distribution" owing to its mathematical form, can be viewed as a generalization of Fisher's exact test to ...Continue Reading

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Citations

Jun 26, 2013·Journal of Bioinformatics and Computational Biology·Qinmin Hu, Jimmy Xiangji Huang
Jan 4, 2013·PLoS Genetics·Frances M K WilliamsTim D Spector
Dec 18, 2013·Bioinformatics·Andreas KrämerStuart Tugendreich
Jan 24, 2014·Computational and Mathematical Methods in Medicine·Szilárd NemesKhalil Helou
Jul 2, 2017·Bioinformatics·Glyn Bradley, Steven J Barrett

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Datasets Mentioned

BETA
GSE7683

Software Mentioned

R
igraph

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