I have a dataset where I want to see effect of a drug on my patients who responded and not responded towards treatment. I collected their blood at three different time point or visit. For each patient I have their age and sex information with me. Now to perform differential expression analysis I used DESeq2 to perform time series analysis as I have collected blood at three different visit. I want to control age and gender effect on my data so I can see interaction between responder group and different time point. Here is the sample table and my DESeq2 design formula:
sample Phenotype visit Age Gender 1 NonResponder 1 42 female 2 NonResponder 2 42 female 3 NonResponder 3 42 female 4 NonResponder 1 49 female 5 NonResponder 2 49 female 6 NonResponder 3 49 female 7 NonResponder 1 27 male 8 NonResponder 2 27 male 9 NonResponder 3 27 male 10 Responder 1 77 female 11 Responder 2 77 female 12 Responder 3 77 female 13 Responder 1 51 male 14 Responder 2 51 male 15 Responder 3 51 male 16 Responder 1 47 male 17 Responder 2 47 male 18 Responder 3 47 male
So which design should I use to control age and gender effect on my data
dds=(design= ~age+gender+visit+phenotype+visit:phenotype+age:phenotype+gender:phenotype) dds=DESeq(dds)
dds=(design=~age+gender+visit+phenotype+visit:phenotype) dds=DESeq(dds,test="LRT", reduced=~age+gender)
I will highly appreciate help with this
Dear Dr. Michael Love,
Thank you so much for the reply. Actually, I break the age in two part. I found median age in my data and used age as factor variable to define age as more than median and below median. So now in my dataset age has two group more than median age and less than median age. I also saw your previous reply to other post and used age in this way. My design has age as you suggested.
However, I could not understand that which design I should use. So you are suggesting me to use second design
as this will control the age and gender variable while I am testing the effect of phenotype and visit interaction. But I have a question that if I use second design than I will find genes where visit or phenotype have any effect but what are the chances that these genes will not be affected by age and gender?
Or do you think that using this second design I have already controlled the age and gender effect on my data.
It controls for age and gender.
Thank you so much for reply.