Applications of Granger causality to biological data
Zou, Cunlu (2010) Applications of Granger causality to biological data. PhD thesis, University of Warwick.
WRAP_THESIS_Zou_2010.pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Official URL: http://webcat.warwick.ac.uk/record=b2491772~S15
In computational biology, one often faces the problem of deriving the
causal relationship among different elements such as genes, proteins, metabolites,
neurons and so on, based upon multi-dimensional temporal data. In literature,
there are several well-established reverse-engineering approaches to explore
causal relationships in a dynamic network, such as ordinary differential
equations (ODE), Bayesian networks, information theory and Granger Causality.
To apply the four different approaches to the same problem, a key issue is
to choose which approach is used to tackle the data, in particular when they
give rise to contradictory results.
In this thesis, I provided an answer by focusing on a systematic and
computationally intensive comparison between the two common approaches
which are dynamic Bayesian network inference and Granger causality. The
comparison was carried out on both synthesized and experimental data. It is
concluded that the dynamic Bayesian network inference performs better than the Granger causality approach, when the data size is short; otherwise the
Granger causality approach is better.
Since the Granger causality approach is able to detect weak interactions
when the time series are long enough, I then focused on applying Granger causality
approach on real experimental data both in the time and frequency domain
and in local and global networks. For a small gene network, Granger causality
outperformed all the other three approaches mentioned above. A global
protein network of 812 proteins was reconstructed, using a novel approach. The
obtained results fitted well with known experimental findings and predicted
many experimentally testable results. In addition to interactions in the time domain,
interactions in the frequency domain were also recovered.
In addition to gene and protein data, Granger causality approach was
also applied on Local Field Potential (LFP) data. Here we have combined multiarray
electrophysiological recordings of local field potentials in both right inferior
temporal (rIT) and left IT (lIT) and right anterior cingulate (rAC) cortices
in sheep with Granger causality to investigate how anaesthesia alters processing
during resting state and exposure to pictures of faces. Results from both the
time and frequency domain analyses show that loss of consciousness during
anaesthesia is associated with a reduction/disruption of feed forward open-loop
cortico-cortical connections and a corresponding increase in shorter-distance
closed loop ones.
|Item Type:||Thesis or Dissertation (PhD)|
|Subjects:||Q Science > QH Natural history > QH301 Biology|
|Library of Congress Subject Headings (LCSH):||Computational biology|
|Official Date:||December 2010|
|Institution:||University of Warwick|
|Theses Department:||Department of Computer Science|
|Sponsors:||University of Warwick. Dept. of Computer Science|
|Extent:||xii, 190 p. : ill., charts|
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