Dear All, I am new to both bioconductor and microarrays and am struggling quite a bit. I am trying to analyze data collected at 7 time points for just 1 treatment (so basically at time 0 something was added to the cells and we want to know how expression changed along time). There are no replicates. Not having replicates seems to cause quite a lot of problems. I assume I'm just not using the right packages/calls. I tried using the limma package, but my design matrix is actually a vector (as I only have one treatment) and that doesn't seem to work. I would like to consider genes that show a minimum of two-fold change in expression. So (I think - again, I'm a complete newbie) I need to compare each of time points 1-6 to time point 0 and look at the difference in expression levels. How can I do that? Your help will be greatly appreciated! Liat. -- output of sessionInfo(): -- Sent via the guest posting facility at bioconductor.org.

Hi Liat, On 7/10/2012 7:09 AM, Liat [guest] wrote: > Dear All, > > I am new to both bioconductor and microarrays and am struggling quite a bit. > > I am trying to analyze data collected at 7 time points for just 1 treatment (so basically at time 0 something was added to the cells and we want to know how expression changed along time). There are no replicates. > > Not having replicates seems to cause quite a lot of problems. I assume I'm just not using the right packages/calls. It depends on what assumptions you are willing to make. If you want to assume that there is a linear response between time and gene expression (where time is considered to be a continuous covariate rather than a factor level), then you can fit a model using these data. You could even allow for curvature in the line by adding a quadratic or cubic term. In that situation you could still use limma. However, if you are simply looking to find differences between say time 1 and time 0, then you have no replication and will have to rely only on fold change. This is a simple matter of subtracting one column of your data matrix from another (assuming that you have taken logs, which you should do). > > I tried using the limma package, but my design matrix is actually a vector (as I only have one treatment) and that doesn't seem to work. The design matrix will not be a single vector regardless. You are not comparing treatment, as you have only one treatment. You are comparing time, for which you have seven observations. Let's say you used 0, and 1-6 hours as time points. You could use a design matrix like > time <- seq(0,6,1) > model.matrix(~time) (Intercept) time 1 1 0 2 1 1 3 1 2 4 1 3 5 1 4 6 1 5 7 1 6 attr(,"assign") [1] 0 1 Where obvs, the first column is the intercept and the second column is the time as continuous covariate. You could also add a quadratic term > model.matrix(~time+I(time^2)) (Intercept) time I(time^2) 1 1 0 0 2 1 1 1 3 1 2 4 4 1 3 9 5 1 4 16 6 1 5 25 7 1 6 36 where you are allowing for curvature in one direction. You might want to add a cubic term as well, which will allow for two curves, but you are really running out of degrees of freedom at that point. There are other things you could do as well. You could look for big changes between time points (and relatively unchanged expression at all other times) by aggregating time points. As an example, a given gene might be relatively unchanged at the first three time points, then jump up to a higher expression level and remain there for the remaining four time points. A t-test comparing the mean of the first three points and the remaining four time points would tease that out. You could do several such comparisons (time 0 vs all others, time 0 and 1 vs times 2-6, etc). Again, there are underlying assumptions for this sort of analysis, and you are looking for a very particular pattern. It really comes down to what sort of assumptions you are willing to make, and whether or not you will be able to defend those assumptions to others. Best, Jim > > I would like to consider genes that show a minimum of two-fold change in expression. So (I think - again, I'm a complete newbie) I need to compare each of time points 1-6 to time point 0 and look at the difference in expression levels. > > How can I do that? > > Your help will be greatly appreciated! > Liat. > > -- output of sessionInfo(): > > > > > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099

Dear James, Just wanted to thank you for your detailed reply. It was very helpful! It gives me a lot to think about. I will use fold change for now to get an idea of what is there, and discuss possible assumptions with the person who was involved in the experiment design (I wasn't...). Thanks again! Liat Shavit Grievink.