Question: DESeq2 time series data and age and gender correction
0
gravatar for Lalit
12 months ago by
Lalit10
Jodhpur
Lalit10 wrote:

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 and I am interested to find out genes that changed expression with time in responder group. Please let me know which design I should follow to control age and gender effect and to find out diff. expressed genes with time. This is my datasets (few samples are given here) 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

and these are the designs

design (a) dds=(design=~age+gender+visit+phenotype+visit:phenotype) dds=DESeq(dds,test="LRT", reduced=~age+gender)

design (b) dds=(design= ~age+gender+visit+phenotype+visit:phenotype+age:phenotype+gender:phenotype) dds=DESeq(dds,test="LRT", reduced=~age+gender)

design (c) dds=(design= ~age+gender+visit+phenotype+visit:phenotype+age:phenotype+gender:phenotype) dds=DESeq(dds,test="LRT", reduced=~age+gender+visit+phenotype)

rna-seq deseq2 time series R • 634 views
ADD COMMENTlink modified 12 months ago by Kevin Blighe39k • written 12 months ago by Lalit10
0
gravatar for Kevin Blighe
12 months ago by
Kevin Blighe39k
Republic of Ireland
Kevin Blighe39k wrote:

Hello Lalit,

Your models look overly complex. Just use ~ age + gender + visit + phenotype + visit::phenotype. Even his is quite complex and may result in over-adjustment. Try to keep the model as simple as possible.

Kevin

ADD COMMENTlink modified 9 months ago • written 12 months ago by Kevin Blighe39k
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