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Open Access Highly Accessed Research article

Meta-analysis of gene coexpression networks in the post-mortem prefrontal cortex of patients with schizophrenia and unaffected controls

Meeta Mistry1, Jesse Gillis2 and Paul Pavlidis34*

Author Affiliations

1 Canadian Institute of Health Research/Michael Smith Foundation for Health Research (CIHR/MSFHR) Graduate Program in Bioinformatics, University of British Columbia, Vancouver, BC Canada

2 Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, One Bungtown Road, NY 11724, USA

3 Department of Psychiatry, University of British Columbia, Vancouver BC, Canada

4 Centre for High-throughput Biology, 177 Michael Smith Laboratories, 2185 East Mall, University of British Columbia, Vancouver BC, Canada

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BMC Neuroscience 2013, 14:105  doi:10.1186/1471-2202-14-105

Published: 26 September 2013

Additional files

Additional file 1: Table S1:

Comparing brain-related disease gene set properties to functional GO groups.

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Additional file 2: Figure S1:

Jackknifed network measures. For each jackknifed network (in which one dataset is removed), we computed shortest path length and clustering coefficient for SZUP and SZDOWN. To summarize trends observed in the jackknife analysis, we plotted clustering coefficient, shortest path length found in the CTL and SZ networks. Results from SZUP are found in A-B, and SZDOWN in C-D. Each line represents a different jackknifed network, with the legend indicating which dataset was removed.

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Additional file 3: Table S2:

Disease characterization of all coexpression modules in each network. Modules in each network were characterized by enrichment of genes that are differentially expressed in schizophrenia. Disease effect p-values were computed by entering the t-statistic for the disease effect of each gene into a Wilcoxon rank-sum test by module. SZUP and SZDOWN are the up- and down-regulated schizophrenia gene sets previously identified in our meta-analysis [2].

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Additional file 4: Table S3:

Cell-type marker enrichment of coexpression modules in each network. For each cluster in the control and schizophrenia networks we report the number of genes that overlap with published lists of cell-type marker genes for oligodendrocytes , neurons, and astrocyte marker genes provided by [15]. Cell-type enrichment was computed for a low-stringency list (> 4-fold) and for a high stringency list (> 10-fold). Hypergeometric probabilities were computed to evaluate significance of overlap.

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Additional file 5: Table S4:

Gene Ontology enrichment of top five disease modules in control.

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Additional file 6: Table S5:

Gene Ontology enrichment of top five disease modules in schizophrenia networks, respectively.

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Additional file 7: Table S6:

Enrichment of genes previously associated with other covariates.

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Additional file 8: Figure S2:

Evaluating the effects of covariates on network modules. For the age up- and pH down regulated genes which are enriched in the immune response module of CTL, the expression data was plotted to evaluate differential expression between control and schizophrenia. A) Genes which remain in the SZ immune module; B) Genes that are lost from the SZ immune module. In either case, the expression for these genes is variable within each cohort and differences in mean expression between cohorts are very small and not significant.

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