Hi On 10/06/13 14:40, Cor Lieftink [guest] wrote: > We use DESeq for our pooled shRNA screen analysis. Often this is > about comparing a single cell-line for drug treatment (TR) vs > untreated (UT) both after a certain number of days, fe. 7. In this > experimental design the transfection on timepoint 0 (T0) is the same > for both conditions, so that when comparing Treated/Untreated one > doesnot have to correct for T0, as (TR7/T0) / (UT7/T0) = TR7/UT7. > However in case of comparing different cell-lines, the transfection > is different, so one need to correct for T0, for example by dividing > the counts of both conditions with the counts of T0. The question is > if one can then still use DESeq for differential analysis, perhaps > after certain transformations of the data like a log2 transformation > of the ratios in order to get a (more) symmetrical distribution? Or > what else would be the appropriate statistical test? No and Yes. No: Methods meant for count data must not be used with data which does not consist of counts, such as transformed or normalized data. Yes: Of course you can analyse such data as long as you refrain from performing any divisions and leave it to DESeq to do this. This is what generalized linear models (GLMs) are good for. You set up a data frame with one row for each sample and two factors as columns, say "time" (with levels "T7" and "T0") and "drug" (with levels "treated" and "untreated"). Then you fit two models (see the section on GLMs in the vignette): reduced model: count ~ drug + time full model: count ~ drug * time and compare them, as shown in the vignette. Simon