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Listen to genes: dealing with microarray data in the frequency domain.

Feng J, Yi D, Krishna R, Guo S, Buchanan-Wollaston V - PLoS ONE (2009)

Bottom Line: The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000 genes observed over 22 time points over 22 days.We show our method in a step by step manner with help of toy models as well as a real biological dataset.We also analyse three distinct gene circuits of potential interest to Arabidopsis researchers.

View Article: PubMed Central - PubMed

Affiliation: Centre for Computational System Biology, Shanghai, Fudan University, Shanghai, People's Republic of China. Jianfeng.Feng@warwick.ac.uk

ABSTRACT

Background: We present a novel and systematic approach to analyze temporal microarray data. The approach includes normalization, clustering and network analysis of genes.

Methodology: Genes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources of variations. The model minimizes the correlation among error terms across replicates. The normalized gene expressions are then clustered in terms of their power spectrum density. The method of complex Granger causality is introduced to reveal interactions between sets of genes. Complex Granger causality along with partial Granger causality is applied in both time and frequency domains to selected as well as all the genes to reveal the interesting networks of interactions. The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000 genes observed over 22 time points over 22 days. Three circuits: a circadian gene circuit, an ethylene circuit and a new global circuit showing a hierarchical structure to determine the initiators of leaf senescence are analyzed in detail.

Conclusions: We use a totally data-driven approach to form biological hypothesis. Clustering using the power-spectrum analysis helps us identify genes of potential interest. Their dynamics can be captured accurately in the time and frequency domain using the methods of complex and partial Granger causality. With the rise in availability of temporal microarray data, such methods can be useful tools in uncovering the hidden biological interactions. We show our method in a step by step manner with help of toy models as well as a real biological dataset. We also analyse three distinct gene circuits of potential interest to Arabidopsis researchers.

Show MeSH
Causal relationship between genes: a global circuit.A. A total of 11 genes are shown and a clear hierarchy structure is demonstrated. B. The interactions in the frequency domain.
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pone-0005098-g006: Causal relationship between genes: a global circuit.A. A total of 11 genes are shown and a clear hierarchy structure is demonstrated. B. The interactions in the frequency domain.

Mentions: Finally we turn our attention to a global picture: to analyze the interaction network of all genes. In other words, to analyze how leaf senescence is turned on. All genes are clustered into clusters using the K-mean approach with a total number of different clusters (32, 20,··· etc). After clustering, we then pick up one gene or the centre to represent each cluster. The time trace of the representative gene is plotted in Fig. 6A (see Text S4 for examples of genes belonging to different clusters), together with their causality.


Listen to genes: dealing with microarray data in the frequency domain.

Feng J, Yi D, Krishna R, Guo S, Buchanan-Wollaston V - PLoS ONE (2009)

Causal relationship between genes: a global circuit.A. A total of 11 genes are shown and a clear hierarchy structure is demonstrated. B. The interactions in the frequency domain.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3383793&req=5

pone-0005098-g006: Causal relationship between genes: a global circuit.A. A total of 11 genes are shown and a clear hierarchy structure is demonstrated. B. The interactions in the frequency domain.
Mentions: Finally we turn our attention to a global picture: to analyze the interaction network of all genes. In other words, to analyze how leaf senescence is turned on. All genes are clustered into clusters using the K-mean approach with a total number of different clusters (32, 20,··· etc). After clustering, we then pick up one gene or the centre to represent each cluster. The time trace of the representative gene is plotted in Fig. 6A (see Text S4 for examples of genes belonging to different clusters), together with their causality.

Bottom Line: The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000 genes observed over 22 time points over 22 days.We show our method in a step by step manner with help of toy models as well as a real biological dataset.We also analyse three distinct gene circuits of potential interest to Arabidopsis researchers.

View Article: PubMed Central - PubMed

Affiliation: Centre for Computational System Biology, Shanghai, Fudan University, Shanghai, People's Republic of China. Jianfeng.Feng@warwick.ac.uk

ABSTRACT

Background: We present a novel and systematic approach to analyze temporal microarray data. The approach includes normalization, clustering and network analysis of genes.

Methodology: Genes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources of variations. The model minimizes the correlation among error terms across replicates. The normalized gene expressions are then clustered in terms of their power spectrum density. The method of complex Granger causality is introduced to reveal interactions between sets of genes. Complex Granger causality along with partial Granger causality is applied in both time and frequency domains to selected as well as all the genes to reveal the interesting networks of interactions. The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000 genes observed over 22 time points over 22 days. Three circuits: a circadian gene circuit, an ethylene circuit and a new global circuit showing a hierarchical structure to determine the initiators of leaf senescence are analyzed in detail.

Conclusions: We use a totally data-driven approach to form biological hypothesis. Clustering using the power-spectrum analysis helps us identify genes of potential interest. Their dynamics can be captured accurately in the time and frequency domain using the methods of complex and partial Granger causality. With the rise in availability of temporal microarray data, such methods can be useful tools in uncovering the hidden biological interactions. We show our method in a step by step manner with help of toy models as well as a real biological dataset. We also analyse three distinct gene circuits of potential interest to Arabidopsis researchers.

Show MeSH