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Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease

Radu Dobrin1, Jun Zhu1, Cliona Molony1, Carmen Argman1, Mark L Parrish1, Sonia Carlson1, Mark F Allan24, Daniel Pomp23 and Eric E Schadt15*

Author Affiliations

1 Rosetta Inpharmatics, LLC, Merck & Co., Inc., Terry Avenue North, Seattle, Washington 98109, USA

2 Department of Animal Science, University of Nebraska, Lincoln, NE 68508, USA

3 Department of Nutrition, Cell and Molecular Physiology, Carolina Center for Genome Science, University of North Carolina, Chapel Hill, NC 27599, USA

4 Current address: Pfizer Animal Health, Animal Genetics Business Unit, East 42nd Street, New York, NY 10017, USA

5 Current address: Pacific Biosciences, 1505 Adams Dr, Menlo Park, CA 94025, USA

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Genome Biology 2009, 10:R55  doi:10.1186/gb-2009-10-5-r55

Published: 22 May 2009

Additional files

Additional data file 1:

Figure S1 shows the connectivity distribution P(k) for GGC networks. Figure S2 shows FDR curves for each tissue in the analysis. Figure S3 shows modules in single tissue GGC networks as detected by the algorithm. Each cluster is marked by the yellow rectangles. Figure S4 shows single tissue module enrichment. Each panel has the following structure: top, P-values from FET for cis-eQTL blue bars over- and red bars under-enriched; middle, percentage overlap between module and genes with cis-eQTL; bottom, percentage overlap between each module and genes on each chromosome. The scale is between green and black where green represents 0% overlap and black 100% overlap. Figure S5 shows connectivity distribution for TTC networks. In each panel we show connectivity distribution for both types of genes in the TTC networks as follows: (a) blue for adipose, red for hypothalamus; (b) blue for hypothalamus, red for liver; (c) blue for adipose, red for liver. Figure S6 shows FDR curves for the TTC networks. Figure S7 shows a representation for the AH TTC network. Figure S8 shows a representation for the HL TTC Network. Figure S9 shows a representation for the AL TTC network. Figure S10 shows the number of network partition versus edge removal time. In black we show total number of subnetworks at each edge removal step; in blue we show number of 'open' subnetworks from where we can potentially remove edges. The number is obtained by subtracting from the total number of subnetworks in the partition the subnetworks defined as 'closed'. Figure S11 shows TTC network partitioning. For each of the TTC networks we have highlighted the subnetworks obtained through partitioning. Each color represents a subnetwork. The AH and HL networks are much more modular than the AL network. Figure S12 shows TTC network enrichment. Each panel has the following structure: top, P-values from FET for cis-eQTL over- (blue bars) and under-enriched (red bars); middle, percentage overlap between module and genes with cis-eQTLs; bottom, percentage overlap between each module and genes on each chromosome. The scale is between green and black where green represents 0% overlap and black 100% overlap. Figure S13 shows the TTC network backbones. Node color and symbols match the description from Figures 6, 7 and 8 in the main section of the paper. Each backbone contains the most robust links from the TTC network. Table T1 lists clinical trait descriptions. Table T2 lists microarray probe annotations. Table T3 lists the probes selected for single tissue analysis. Table T4 lists the adipose single tissue modules. Table T5 lists the hypothalamus single tissue modules. Table T6 lists the liver single tissue modules. Table T7 lists the AH TTC network. Table T8 lists the HL TTC network. Table T9 lists the AL TTC network. Table T10 lists the AH subnetworks. Table T11 lists the HL subnetworks. Table T12 lists the AL subnetworks. Table T13 provides the AH network backbone. Table T14 provides the HL network backbone. Table T15 provides the AL network backbone. Table T16 lists the adipose cis-eQTL genes. Table T17 lists the hypothalamus cis-eQTL genes. Table T18 lists the liver cis-eQTL genes.

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