I am using citrus Affymetrix array to reach a set of genes differentially expressed in particular stages. I would like to know the genes (transcripts) which are co expressed or coregulated.
Please suggest how should I proceed to determine this interaction.
You can use Weighted Gene Co-expression Network Analysis (WGCNA) package in R. It will gives you modules (cluster) of genes which are co-expressed on the basis of Topological Overlap between the genes. Further you can do promoter analysis for transcription factor binding sites (TF enrichment) to identify the co-regulation of the genes.
Method can be as follow.
1. First compute the pairwise correlation.
2. Next transform Cor matrix to Adjecency Matrix and then to Topological Overlap Matrix.
3. give TO matrix as input for clustering.
4. identify the clusters.
5. Do biological process analysis of clusters.
6. You can also do TF enrichment analysis by using the promoter sequences of genes in a modules.
For more information please find this paper.
http://www.biomedcentral.com/1471-2105/9/559/
"WGCNA: an R package for weighted correlation network analysis"
see te recent paper, may be help you
ANDERSON, A.M., CARTER, K.W., ANDERSON, D. and WISE, M.J. (2012). Coexpression of nuclear receptors and histone methylation modifying genes in the testis: implications for endocrine disruptor modes of action. PLoS ONE 7: e34158
May be this tutorial may help you if you are using R.
http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ASPMgene/BrainCancerTutorial/GBMtutorialPartAWGCNA.pdf
You can use Weighted Gene Co-expression Network Analysis (WGCNA) package in R. It will gives you modules (cluster) of genes which are co-expressed on the basis of Topological Overlap between the genes. Further you can do promoter analysis for transcription factor binding sites (TF enrichment) to identify the co-regulation of the genes.
Method can be as follow.
1. First compute the pairwise correlation.
2. Next transform Cor matrix to Adjecency Matrix and then to Topological Overlap Matrix.
3. give TO matrix as input for clustering.
4. identify the clusters.
5. Do biological process analysis of clusters.
6. You can also do TF enrichment analysis by using the promoter sequences of genes in a modules.
For more information please find this paper.
http://www.biomedcentral.com/1471-2105/9/559/
"WGCNA: an R package for weighted correlation network analysis"
Thanks to all. Use of R language is bit difficult as currently I have started learning this for microarray analysis. Recently I find programme GeneMania plug-in (for Cytoscape) where the available GEO data of NCBI has been used to create network analysis including coexpression and protein protein interaction studies.
So, would it not be more easy and helpful to get the network associated with the differentially expressed sequences.
It is easier to use butt does not give exactly the same results and the possible to modify some parmeters.
I have a gene list from up and down regulated genes of microarray.
How can I draw a co-expression network with my gene list using GeneMania plug-in of Cytoscape?
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