Hello,
I have some doubts after reading the DESeq2 vignette and different threads on this topic. If I have this data:
If my objective is to compare the Status of the samples, in principle the formula would be ~Status. However, there are big differences between the Zone, as we can see in this PCA:
Would we have to make the formula like ~Zone+Status in order to control the effect of the Zone over Status? Or is it better to do it only with ~Status? If a new variable such as Sex is added and we want to control its effect, we would have to reconvert the formula to ~Zone+Sex+Status? Where can I find an explanation of what this "control" is based on?
Thank you
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Rafael Soler •
Why its better to use this formula rather than only ~Status? If a new variable such as Sex, we would have to reconvert the formula to ~Zone+Sex+Status?
Thanks for the reply, although I would appreciate a bit of explanation.
Best
You have variability within each group caused by zone. If you omit zone, the software just thinks there is a lot of variability. If you include zone, then it understands that there is an underlying reason for some of the variability and it can be included in the mathematical model, so you get better results.
And yes, you can just pile sex, or batch in the design like that.
Thanks, now I understand it better.
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I would rely on how much the
Zonesare affecting yourStatus. Did you try doingStatus ~ Zonesand checking the results? If you see that yourZonesare really influencing your Status, then do~ Status + Zones. Same thing for a new variableSexas you mentioned.Thanks. In the DESeq2 vignnete we can see that
design = ~ batch + condition. Shouldn't it be in this order?design = ~ Zones + StatusBest