Hi all, I have used the WGCNA framework to generate network and identify modules. Next, I wish to relate the modules with the disease status. My samples include malaria parasites isolated from patients with different disease status. For example, I have 20 malaria parasite samples of which 7 are from uncomplicated malaria patients, 7 are from patients with complication 1 and 6 are from complication 2. Is there any procedure to see the relationship of eigenegnes with specific disease status? More specifically to know which modules are positively or negatively correlates to particular group of malaria samples? Looking forward for suggestions. Best regards Amit -- Amit Kumar Subudhi Research Scholar, CSIR-Senior Research Fellow, Molecular Parasitology and Systems Biology Lab, Department of Biological Sciences , FD III, BITS, Pilani, Rajasthan- 333031 e mail- amit4help@gmail.com amit.subudhi@pilani.bits-pilani.ac.in Mob No- 919983525845 [[alternative HTML version deleted]]

On Mon, Jun 30, 2014 at 5:28 AM, amit kumar subudhi <amit4help at="" gmail.com=""> wrote: > Hi all, > > I have used the WGCNA framework to generate network and identify modules. > > Next, I wish to relate the modules with the disease status. My samples > include malaria parasites isolated from patients with different disease > status. > > For example, I have 20 malaria parasite samples of which 7 are from > uncomplicated malaria patients, 7 are from patients with complication 1 and > 6 are from complication 2. > > Is there any procedure to see the relationship of eigenegnes with specific > disease status? More specifically to know which modules are positively or > negatively correlates to particular group of malaria samples? Yes, turn your 'complication' with values uncomplicated, complication 1, complication 2 into a set of binary variables: one variable that is 1 for uncomplicated and 0 for everything else, one variable that is 1 for complication 1 and zero for everything else, and one variable that is 1 for complication 2 and zero for everything else. You could also create pairwise comparisons, for example a variable that is 1 for complication 1, 0 for complication 2, and missing (NA) for uncomplicated. Then correlate the eigengenes to the binary indicators. Best, Peter