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First published online April 20, 2007
Journal of Experimental Biology 210, 1507-1517 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.004432
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Extracting biology from high-dimensional biological data

John Quackenbush

Department of Biostatistics and Computational Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA and Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA


Figure 1
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  		Fig. 1. Biological systems can be thought of as `information management' systems with multiple levels of organization that interact and influence each other. The study of biological systems has a long history and although high-throughput `'omics' approaches are expanding their range of applications, integrating information from various levels can provide powerful insights for the interpretation of high-throughput data.

 

Figure 2
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  		Fig. 2. Genes identified as both differentially expressed and also genetically linked to the differential response to inhaled LPS in a mouse model of environmentally induced asthma. Responses were measured by qPCR, comparing exposed mice to matched controls from the same strain. The ordering of the strains by expression levels for these genes closely mimics that produced when ordered by phenotype.

 

Figure 3
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  		Fig. 3. Heat map and hierarchical clustering dendrogram, in which the elements being clustered are the GO term assignments and the values represented in each cell are the –log10(P-values) of this being significantly different from the null hypothesis, based on EASE analysis. From Larkin et al. (Larkin et al., 2004Go).

 

Figure 4
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  		Fig. 4. An example of a Bayesian Network model for four genes. If we assume that Gene 1 activates Gene 2, then we can construct a conditional probability table (shown at the right) that captures our observations of the state of Gene 1 when we observe Gene 2 to be upregulated. Here the values for Gene 1 of –1, 0, and +1 represent states where Gene 1 is downregulated, unchanging or upregulated, respectively.

 

Figure 5
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  		Fig. 5. Representations of the networks produced by a Bayesian Network analysis of the top 40 genes selected as distinguishing ALL and AML in the microarray dataset of Golub et al. (Golub et al., 1999Go) for links with confidence greater than 0.7; links with confidence greater than 0.9 are shown in bold. Networks represent the consensus of 200 iterations for (A) the microarray data alone, (B) the microarray data with constraints from protein-protein interaction (PPI) data, (C) microarray data with constraints from literature networks, and (D) microarray data with constraints from a combination of microarray and PPI data.

 

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© The Company of Biologists Ltd 2007