Inferring causal relations from multivariate time series: A fast method for large-scale gene expression data

Yinyin Yuan, Chang Tsun Li

Research output: Book chapter/Published conference paperConference paper

Abstract

Various multivariate time series analysis techniques have been developed with the aim of inferring causal relations between time series. Previously, these techniques have proved their effectiveness on economic and neurophysiological data, which normally consist of hundreds of samples. However, in their applications to gene regulatory inference, the small sample size of gene expression time series poses an obstacle. In this paper, we describe some of the most commonly used multivariate inference techniques and show the potential challenge related to gene expression analysis. In response, we propose a directed partial correlation (DPC) algorithm as an efficient and effective solution to causal/regulatory relations inference on small sample gene expression data. Comparative evaluations on the existing techniques and the proposed method are presented. To draw reliable conclusions, a comprehensive benchmarking on data sets of various setups is essential. Three experiments are designed to assess these methods in a coherent manner. Detailed analysis of experimental results not only reveals good accuracy of the proposed DPC method in large-scale prediction, but also gives much insight into all methods under evaluation.

Original languageEnglish
Title of host publicationProceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
Pages92-99
Number of pages8
DOIs
Publication statusPublished - 2009
Event2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009 - Taichung, Taiwan, Province of China
Duration: 22 Jun 200924 Jun 2009

Conference

Conference2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
CountryTaiwan, Province of China
CityTaichung
Period22/06/0924/06/09

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