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

By Yinyin Yuan and Chang-Tsun Li

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

Topics: QA, QH426
Year: 2009
OAI identifier: oai:wrap.warwick.ac.uk:3363

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  1. (2005). An empirical Bayes approach to inferring large-scale gene association networks.” doi
  2. (2006). An introduction to ROC analysis,” doi
  3. (2006). Causality and pathway search in microarray time series experiment.” Bioinformatics, doi
  4. (2005). Comparison of linear signal processing techniques to infer directed interactions in multivariate neural systems,” Signal Processing, vol. In Press, Uncorrected Proof, doi
  5. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing,”
  6. (2007). Genome-wide partial correlation analysis of escherichia coli microarray data,”
  7. (2007). Inferring dynamic genetic networks with low order independencies,” doi
  8. (1979). Information Retrieval.N e w t o n , doi
  9. (2005). Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data.” doi
  10. (1969). Investigating causal relations by econometric models and cross-spectral methods,” doi
  11. (2008). l a r k e ,H .W .R e s s o m ,A .W a n g ,J .X u a n doi
  12. (2007). Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process,” doi
  13. (2002). Maximum likelihood fitting using ordinary least squares algorithms,” doi
  14. (2005). Nonlinear multivariate analysis of neurophysiological signals,” doi
  15. (1975). Signal detection theory and ROC analysis, Series in Cognition and Perception.
  16. (2006). SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms.” doi
  17. (2007). Validating module network learning algorithms using simulated data,” doi

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