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Open Access Research article

Cancer core modules identification through genomic and transcriptomic changes correlation detection at network level

Wenting Li1, Rui Wang2, Linfu Bai1, Zhangming Yan1 and Zhirong Sun1*

Author Affiliations

1 MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Institute of Bioinformatics and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China

2 Computational Biology and Bioinformatics Program, Institute for Genome Science and Policy, Duke University Medical Center, Durham, NC, USA

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BMC Systems Biology 2012, 6:64  doi:10.1186/1752-0509-6-64

Published: 12 June 2012

Additional files

Additional file 1::

Table S1. Lists summary of the modules identified from the network weighted by the Pearson vs Spearman correlation, respectively. Table S2. Lists the inter-datasets reproducibility results (overlapping percentage) from different methods.Table S3. Lists the detailed options about the mutation data from the COSMIC. Table S4.lists the CAN-genes in core modules. Table S5. lists the GO summary of the core modules in all cancer types. Table S6. lists the network features of the mutated genes in the core modules.

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Additional file 2::

Figure S1. Shows the intra-dataset reproducibility results of TRMs identified from the Barrier datasets. Figure S2. Shows inter-dataset reproducibility results of TRMs identified from two breast cancer datasets (edge weighted by the spearman correlation). Figure S3. Shows the TRMs’ mutation enrichment at module level (edge weighted by the spearman correlation).

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