Dear Bill, Perhaps I have not understood your problem. You could normalize your data as suggested. After that, if you want to get some type of significance value, you do a two-sample test such as a t-test or Wilcoxon test. To date I am using a gene by gene analysis, by applying the function to each row of the expression matrix. (see affy command "expr and R commands "apply", t.test and Wilcox.test) There are probably more efficient ways to do this. This can also be done in limma, using the design (1 1 1 -1 -1 -1) assuming the first 3 arrays come from one condition and the other 3 from the other. To detect outliers, I usually do MvA plots or array x array expression plots (on the log scale) i.e. array x array plots would be the 3 plots arising from all pairs of arrays from the same treatment, with expression from array i on the x-axis and expression from array j on the y-axis. Outliers are points far from the diagonal. MvA plots are the 3 plots arising from all pairs of arrays from the same treatment. The x-axis is the expression value averaged over the arrays, and the y-axis is the difference in expression values between the two arrays. This generally looks like a sideways raindrop, with the wide end near the origin. Again, points away from the point cloud are outliers. --Naomi At 09:15 PM 1/12/2004, William Kenworthy wrote: >Unfortunately, they came to us too late and were forced by circumstances >to complete the experiment as designed. Next time ... > >I am thinking a straight average of the replicate values for each gene >may the best solution (not per probe). However, a significance value >may show up a suspect gene (if a difference exists across the >replicates, something is wrong in a yes/no fashion, rather than looking >at degrees of significance) > >My preference is to use RMA from the affy package as a first pass, then >expand using some of the other algorithms but concrete examples (and >documentation) on how to specify replicates is lacking. (i.e., the data >examples say there are replicates, but how are they specified/handled - >or not?) > >Billk > > >On Sun, 2004-01-11 at 23:03, Naomi Altman wrote: > > I usually use mixed model ANOVA to determine differential expression. By > > putting in a random effects term for sample (you have 2 technical reps per > > sample) you end up with the correct analysis. > > > > However, in your case you have no sample replication. As a result, you > > cannot do a statistically valid ANOVA. People do use various > > approximations - the Affy-type Wilcoxon tests which treat the probes as > > replicates (makes me very very nervous); using the technical replicates as > > if they were biological replicates (makes me very nervous - there is no > > guarantee that there is much relationship between the technical and > > biological variation). Of course, the best thing to do is to convince the > > investigators that biological replication is far more important than > > technical replication. In most Affy studies, technical replication will > > not be cost-effective due to the relatively small technical variance and > > the large cost of individual arrays. > > > > At 01:18 AM 1/9/2004, William Kenworthy wrote: > > >Hi, I have just been passed a set of affy data that consists of 3 > > >states, two technical replicates of each state (6 chips overall) > > > > > >1. whats the best way (normalisations, algorithms) to leverage technical > > >replicates? > > > > > >2. how do you tell algorithms such as rma which are the replicates? (I > > >presume the phenoData in the AffyBatch specifies this, but the examples > > >are in a binary format so you cant open the raw data file and see how > > >they are put together!) > > > > > >BillK > > > > > >_______________________________________________ > > >Bioconductor mailing list > > >Bioconductor@stat.math.ethz.ch > > >https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor > > > > Naomi S. Altman 814-865-3791 (voice) > > Associate Professor > > Bioinformatics Consulting Center > > Dept. of Statistics 814-863-7114 (fax) > > Penn State University 814-865-1348 (Statistics) > > University Park, PA 16802-2111 Naomi S. Altman 814-865-3791 (voice) Associate Professor Bioinformatics Consulting Center Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111