I want to infer gene regulatory interactions from microarray gene expression data. I have calculated correlation matrix (Pearsons correlation coefficient) on the differentially expressed genes. Further I am unable to find out regulatory interactions that which technique can be used to infer activation and inhibition interactions. Please guide me
you should take a look to the "NetworkInference" biocView of the Bioconductor software that will list for you all the Bioconductor software packages that categorize themselves as suitable for inferring networks from data. You can find this by going to http://www.bioconductor.org, follow the "Install" tab, then the link called "Software" on the right and then on the left under "biocViews" follow "Software" -> "BiologicalQuestion" -> "NetworkInference". The direct link is:
Among the available packages I can advice you to use qpgraph since I'm its maintainer :) ln its documentation the vignettes entitled "BasicUsersGuide.pdf" and "Reverse-engineer transcriptional regulatory networks using qpgraph" are good starting points.
Can you please guide me how to obtain real interactions amongst genes from microarray data. like correlation returns diagonal and symmetrical matrix. I need such a correlation measure in which gene A to gene A interaction is not always equal to 1, also interaction value of gene A to Gene B must be different from interaction value of Gene B to Gene A.
The kind of associations you are referring to, self-loops and directed associations, can in general be only reliably inferred from time-course dynamic data. Unfortunately, qpgraph does not provide methods for such type of data. In the biocViews of NetworkInference that I was referring to in my first answer above of two months ago, I see there are two packages that seem to work with dynamic data, GRENITS and rHVDM. Take a look at them and see whether they work for you. I cannot give you further advice.