Bivariate Causal Discovery and Its Applications to Gene Expression and Imaging Data Analysis

Frontiers in Genetics
Rong JiaoMomiao Xiong

Abstract

The mainstream of research in genetics, epigenetics, and imaging data analysis focuses on statistical association or exploring statistical dependence between variables. Despite their significant progresses in genetic research, understanding the etiology and mechanism of complex phenotypes remains elusive. Using association analysis as a major analytical platform for the complex data analysis is a key issue that hampers the theoretic development of genomic science and its application in practice. Causal inference is an essential component for the discovery of mechanical relationships among complex phenotypes. Many researchers suggest making the transition from association to causation. Despite its fundamental role in science, engineering, and biomedicine, the traditional methods for causal inference require at least three variables. However, quantitative genetic analysis such as QTL, eQTL, mQTL, and genomic-imaging data analysis requires exploring the causal relationships between two variables. This paper will focus on bivariate causal discovery with continuous variables. We will introduce independence of cause and mechanism (ICM) as a basic principle for causal inference, algorithmic information theory and additive noise model ...Continue Reading

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Citations

Mar 7, 2020·Scientific Reports·Zixin HuMomiao Xiong
Mar 17, 2021·Journal of Biomedical Informatics·Pranay Sy, Nithin Nagaraj

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Methods Mentioned

BETA
RNA-Seq

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