Hello Bioconductor list server, I am working on a microarray experiment comparing a control to a treatment and the effects on cells. I have the results from my current experiment and from a past experiment using the same microarrays but a different treatment. I have already set up the workflow for both experiments. I find the significant genes using topTable. > results = topTable(fit2, coef="control-treatment", adjust.method="fdr", lfc=2) > trimmed_results = results_v4c4[results_v4c4[,5]<0.05,] I would like to find if the there are similar results from the past experiment to the new experiment. I would like to say in the paper: treatment A had similar transcripts in treatment B indicating similar mechanism between the two treatments or (if there are no similar transcripts) the two treatments do not use the the same mechanism. I would then like to find the transcripts similar in both results and pull out the transcripts with the log fold change and p-values for both results. The log fold changes and p-values can be different between the to experiments. What would be the best method for accomplishing this task? I have read the answers similar to my question on the list server. The main conclusion is to do the contrasts during the matrix design. I ran the contrasts and compared the results to the different experiments' results. Result from the contrasts: ID logFC AveExpr t P.Value adj.P.Val 49667 240417_at -2.392707 3.715039 -5.967782 0.0002286033 0.9319275 24271 214975_s_at 2.720147 4.163144 5.574570 0.0003717616 0.9319275 The first ID is only found to be significant in one of the experiments. The second ID is found in both experiments, but in on the logFC is up regulated and the other is down regulated. I went through 10 Ids and fond the similar results to all of them. How would I explain the discrepancies between the two experiments using contrasts? I have tried using > intersect(old_results, new_results), but I do not get any results back. I know there are at lest two transcripts found in both results, but I do not what to search all of the results. Thank you for your time and energy, Andrew Block Nebraska Center of Virology University of Nebraska at Lincoln [[alternative HTML version deleted]]