Hi,
I am trying to do a meta microarray study to determine potential receptor transcripts that could facilitate virus invasion. The idea is to identify upregulated transcripts from a particular cell type as compared to nine others. From what I understand, this kind of study is fraught with computational and data-normalisation issues, but different approaches have been used to varying degrees of success by different people.
One way of conceivably approaching this would be to identify a set of genes that are expressed significantly more than a threshold value within each individual microarray data-set, draw up the resulting lists, and identify genes uniquely expressed by the target cell type. I can see issues with this right off the bat however (this might exclude receptor genes right away, the assumption that low transcript levels implies not enough receptor molecules to facilitate invasions may be erroneous).
I hope I'm not wrong in assuming that the analysis on the data sets cannot be done individually if am to generate meaningful results. The goal would then be to identify differentially expressed genes on one cell type when compared to nine others. From what I gather, normalization is the biggest issue here. What method would be best to approach this kind of meta-microarray study. Would RankProd work fine (but I read that it compares only 2 experiments at a time). I've come across another method that uses a measure called Cohen's D : http://www.pnas.org/content/103/16/6368.full (any packages that could implement this? ) I'm basically trying to figure out what approach to compare the data works best. Also any ideas to implement this in R would be very helpful