On October 4, 2014, we released an upgraded version, dmGWAS 3.0, which implements a new algorithmEW_dmGWAS: Edge-weighted dense module search for genome-wide association studies and gene expression profiles. In dmGWAS 3.0, the algorithm EW_dmGWAS introduces the following features: |
- The new algorithm, EW_dmGWAS, combines node weight, which are computed based on GWAS signals, and edge weight, which are computed based on gene expression data.
- Allow tissue-specific gene expression profile. For example, for breast cancer GWAS analysis, gene expression data from breast cancer patients can be used; for schizophrenia, gene expression data in brain tissues can be used.
- Edge weights can be computed based on differential gene co-expression (DGCE) patterns, i.e., by comparing tumor to normal samples.
- In dmGWAS 3.0, users can still apply the "old" models that use only node weight from GWAS signal (see the user guide below).
- dmGWAS 3.0 is compatible to igraph 0.6.x.
All old versions, i.e., <3.0, are available from在这里. |
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- Wang Q, Yu H, Zhao Z, and Jia P (2015) EW_dmGWAS: Edge-weighted dense module search for genome-wide association studies and gene expression profiles. Bioinformaticsonline access
- Jia P, Zheng S, Long J, Zheng W, and Zhao Z (2011) dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics 27(1):95-102PubMed
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