Dank Wolfgang, I'll look into the book. >> could someone recommend a review or book on PCA/MDS/SVD/Factor >> Analysis techniques & best practices for gene expression data? >> I want to visualize each of my samples (3 different conditions; 20 >> timepoints - no need to visualize replication because things will >> become too messy) . > > I am not sure I understand: making sure that variation between > replicates is small compared to the variation between your > conditions and timepoints seems like a basic sanity test - without > which anything that follows would be waste of time. I tried several simple PCAs & see that variation between conditions & timepoints is much bigger than between replicates. Now I want to be able to make some general statements about the conditions - eg: which are more similar to each other. But to do that I'd like to understand what I'm doing. In some places I've seen that people simply disregard the first prinicipal component for microarray data. And I see that some distances (eg: using "maximum" distance with cmdscale) give quite different results than manhattan or euclidian distances... Thanks & regards, yannick >> But I'm frankly a bit overwhelmed by the plethora of options. >> My doubts include: >> - when it's appropriate to use which technique >> - should I use it on my complete normalized gene expression >> data set? Or only on significant genes? > > Depends on what you want to see. The two options look for different > things. > > Best wishes > Wolfgang > > ---------------------------------------------------- > Wolfgang Huber, EMBL-EBI, http://www.ebi.ac.uk/huber > > >> Or on the covariance matrix between microarrays? >> - even for a simple PCA, there are an overwhelming number of >> implementations in R (ade's dudi.pca, prcomp, princomp, several in >> MASS, several in pcaMethods) >> thanks :o) >> yannick >> -------------------------------------------- >> yannick . wurm @ unil . ch >> Ant Genomics, Ecology & Evolution @ Lausanne >> http://www.unil.ch/dee/page28685_fr.html >